diff --git a/evaluation/utils.py b/evaluation/utils.py new file mode 100644 index 0000000..856f4cc --- /dev/null +++ b/evaluation/utils.py @@ -0,0 +1,263 @@ +import json +import numpy as np + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer +from matplotlib import pyplot as plt + +import openai +import tiktoken +import time +import os +import argparse +import yaml + +def load_model(path, dtype=torch.bfloat16, device="cuda", num_gpus=1): + if device == "cpu": + kwargs = {"torch_dtype": torch.float32} + elif device == "cuda": + kwargs = {"torch_dtype": torch.bfloat16} + if num_gpus != 1: + kwargs["device_map"] = "auto" + # kwargs["device_map"] = "sequential" # This is important for not the same VRAM sizes + # Hard code for A100s + available_gpu_memory = [40] * num_gpus + kwargs["max_memory"] = { + i: str(int(available_gpu_memory[i] * 0.85)) + "GiB" + for i in range(num_gpus) + } + model = AutoModelForCausalLM.from_pretrained(path, **kwargs) + tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False) + return model, tokenizer + +def load_testcases(test_file): + with open(test_file, 'r') as json_file: + json_list = list(json_file) + + test_cases = [] + for test_case in json_list: + test_case = json.loads(test_case) + test_cases.append(test_case) + + return test_cases + +def test(test_case, model, tokenizer, return_summary=True): + prompt = test_case["prompt"] + prompt_length = test_case["prompt_length"] + topics = test_case["topics"] + input = tokenizer(prompt, return_tensors="pt") + outputs = model.generate(input.input_ids.to(model.device), max_new_tokens=100, use_cache=True)[0] + outputs = outputs[prompt_length:] + outputs = tokenizer.batch_decode([outputs], skip_special_tokens=True) + if return_summary: + summary = f"Label: {topics[0]}, Predict: {outputs}, --- INFO --- Topics: {topics}, Length: {prompt_length}" + return outputs, summary + else: + return outputs + +def attention_span(model, tokenizer, test_case, num_gen_steps=1, raw_attn=False): + assert num_gen_steps == 1, "Only support span for a single generation step now." + prompt = test_case["prompt"] + prompt_length = test_case["prompt_length"] + topics = test_case["topics"] + input = tokenizer(prompt, return_tensors="pt") + + output = model(input.input_ids.to(model.device), output_attentions=True) + num_layer = len(output.attentions) + + attn_mat = torch.cat([a[0, :, -1, :] for a in output.attentions]) + attn_len = attn_mat.shape[-1] + dist = torch.arange(attn_len, 0, step=-1).cuda() + + span = torch.sum(dist * attn_mat, dim =1) + span = span.reshape(num_layer, -1) + + span_avg_layer = torch.mean(span, dim=1) + span_avg_all = torch.mean(span) + + if raw_attn: + return span, span_avg_layer, span_avg_all, attn_mat + else: + return span, span_avg_layer, span_avg_all + +def visualize_attn(attn_mat, save_path): + pass + + +# some codes taking reference from Auto-GPT +def let_gpt_check_response(topics, response, model_name): + topics_list = topics[0] + for i in range(len(topics)): + if i == 0: + continue + topics_list = topics_list + "," + topics[i] + + # prompt = f"Respond True if the topic(s) mentioned in the following paragraph " + \ + # f"have similar topoics in this list in the same order: {topics_list}; " + \ + # "otherwise respond False: \n" + \ + # f"{response}" + + # prompt = f"Given this list of {len(topics)} topics separated by ',': {topics_list} " + \ + # f"\nRespond True if the following list contains {len(topics)} similar topics separated by ',' in the " + \ + # f"same order. Otherwise, respond False. \n" + \ + # f"List: {response}" + + prompt = "Compare the topics in two lists and determine the similarity of the topics on a " + \ + "scale of 1 to 100, where 1 indicates very low similarity and 100 indicates " + \ + "very high similarity. The similarity score will be proportional to the " + \ + "number of different topics in the lists.\n\n" + prompt += f"List 1: {topics} \n" + prompt += f"List 2: {response} \n" + prompt += "Question: What is the similarity score between the topics of List 1 and List 2? " + \ + "The score should be proportional to the number of different topics in the lists. \n" + prompt += "Answer:" + + _, response_line = retrieve_from_openai(prompt, model_name) + + import re + return re.search("\d+", response_line).group() + + +# def ask_gpt_for_similarity_score(topic, response, model_name): + +def token_counter(model_name, prompt): + if "gpt" in model_name: + token_size = len(tiktoken.encoding_for_model(model_name).encode(prompt)) + print(f"Number of tokens: {token_size}") + else: + token_size = len(tiktoken.encoding_for_model(model_name).encode(prompt)) + print(f"Number of tokens: {token_size} by using gpt tokenizer as default") + + return token_size + + +def retrieve_from_openai(prompt, model_name, num_retries=10): + if "gpt" in model_name: + token_size = token_counter(model_name, prompt) + print(f"Number of tokens: {token_size}") + openai.api_key = os.environ["OPENAI_API_KEY"] + else: + token_size = token_counter(model_name, prompt) + print(f"Number of tokens: {token_size} by using gpt tokenizer as default") + + openai.api_key = os.environ["OPENAI_API_KEY"] + print("Using openai key as default key") + + num_retries = 10 + completion = None + for attempt in range(num_retries): + backoff = 2 ** (attempt) + + try: + completion = openai.ChatCompletion.create( + model=model_name, + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": f"{prompt}"} + ], + temperature = 0 + ) + break + except openai.error.RateLimitError: + print("Got rate limit...") + pass + except openai.error.APIError as e: + if e.http_status == 502: + pass + else: + pass + + if attempt == num_retries - 1: + raise + + time.sleep(backoff) + + if completion is None: + print(f"Failed to get response after {num_retries} retries") + return token_size, -1, "Rate limit" + + response_line = completion.choices[0].message["content"] + + return token_size, response_line + +def retrieve_cmd_args(): # setup program params from a given path to a yaml file + parser = argparse.ArgumentParser( + prog='lrt_eval', + description='lrt_eval' + ) + parser.add_argument('yaml_path') + args = parser.parse_args() + f = open(args.yaml_path, "r") + cfgs = yaml.load(f, Loader=yaml.CLoader) + print(yaml.dump(cfgs)) + + return cfgs + +class Conv: + """a single conversation on a topic""" + + def __init__(self, topic, length, content): + self.topic = topic + self.length = length + self.content = content + +class Prompt: + """the prompt used for testing, composed of multiple """ + + def __init__(self, model_name, id, question_dist): + self.model_name = model_name + self.id = id + self.conv_list = [] + self.topic_list = [] + self.length_list = [] + self.length = -1 + self.question_dist = question_dist + + def add_conv(self, conv): + self.conv_list.append(conv) + self.topic_list.append(conv.topic) + self.length_list.append(conv.length) + + def assemble_prompt(self): + order_word = ["first", "second", "third", "fourth", "fifth", "sixth", "seventh", + "eighth", "ninth", "tenth", "eleventh", "twelfth", "thirteenth", + "fourteenth", "fifteenth", "sixteenth", "seventeenth", "eighteenth", + "nineteenth", "twentieth", "twenty-first", "twenty-second", "twenty-third", + "twenty-fourth", "twenty-fifth", "twenty_sixth", "twenty_seventh", "twenty_eigth", + "twenty_ninth", "thritieth"] + + record_prompt = "Below is a record of our previous conversation " + \ + f"on {len(self.topic_list)} different topics. You are the ASSISTANT, and " + \ + "I am the USER. At the beginning of each topic, the USER will say " + \ + "'I would like to discuss the topic of '. Memorize each " + \ + ". At the end of the record, I will ask you to retrieve the " + \ + "first topic. Now the record start. " + + for conv in self.conv_list: + record_prompt += conv.content + + question_idx = "first" + picked_topics = [self.topic_list[0]] + i = 1 + while ((self.question_dist * i) < len(self.conv_list)): + question_idx += f", {order_word[(self.question_dist * i)]}" + picked_topics.append(self.topic_list[self.question_dist * i]) + i += 1 + + self.prompt = "A chat between a curious user and an artificial intelligence " + \ + "assistant. The assistant gives helpful, detailed, and polite " + \ + f"answers to the user\'s questions. USER: {record_prompt} Now " + \ + "the record ends. What is the " + question_idx + " topic(s) we discussed? Only give " + \ + "me the topic name(s) in the format of [, , ...]. Do not summarize yourself. Do not mention topic order. ASSISTANT:" + + # self.prompt = "A chat between a curious user and an artificial intelligence " + \ + # "assistant. The assistant gives helpful, detailed, and polite " + \ + # f"answers to the user\'s questions. USER: {record_prompt} Now " + \ + # f"the record ends. What is the {question_idx} topic(s) we discussed? Only give " + \ + # "me the topic name(s) in the format of [, , ...]. Do not summarize yourself. Do not mention topic order. ASSISTANT:" + + self.length = token_counter(self.model_name, self.prompt) + + return self.prompt, picked_topics + + diff --git a/evaluation/zeroscrolls/eval.py b/evaluation/zeroscrolls/eval.py new file mode 100644 index 0000000..e956361 --- /dev/null +++ b/evaluation/zeroscrolls/eval.py @@ -0,0 +1,107 @@ +import argparse +import json +import os +import time +from tqdm import tqdm + +import torch +import numpy as np + +from datasets import load_dataset +from rouge_score import rouge_scorer + +from evaluation.utils import load_model + +def fix_prompt(prompt): + paragraphs = prompt.split("\n\n") + new_prompt = prompt + "\n\nQuestions:\n" + paragraphs[0] + "\n\nAnswer:\n" + return new_prompt + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-name-or-path", type=str, + # default="/home/haozhang/LongChat/data/dacheng-data/longchat_32K_interpolate", + default="/home/haozhang/LongChat/data/dacheng-data/longchat_7b_16K", + # default="/home/haozhang/LongChat/data/dacheng-data/longchat_13b_16K", + help="model path") + parser.add_argument("--ratio", type=int, default=8, + help="target sequence length / original sequence length") + parser.add_argument("--flash", action='store_true', help="whether to use flash attention to save memory, but slower") + parser.add_argument("--dataset-version", type=str, default="tau/zero_scrolls") + parser.add_argument("--dataset", type=str, default="qasper") + parser.add_argument("--seq-len", type=int, default=15000) + args = parser.parse_args() + + SEQ_LEN = args.seq_len + + # load model + from longchat.train.monkey_patch.llama_interpolate_monkey_patch import replace_llama_with_interpolate + replace_llama_with_interpolate(args.ratio) + + if args.flash: + from longchat.train.monkey_patch.llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn + replace_llama_attn_with_flash_attn() + + import transformers + + path = args.model_name_or_path + model, tokenizer = load_model(path, num_gpus=4) + print("model loaded.") + + # create output dir + name = os.path.split(path)[-1] + output_dir = os.path.join("predictions", name) + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + output_file = os.path.join(output_dir, f"{name}_{args.dataset}_{SEQ_LEN}.raw") + print(f"output file: {output_file}") + + # load dataset + data = load_dataset(args.dataset_version, args.dataset) + if args.dataset_version=="tau/scrolls": + test_cases = data["validation"] + test_cases = test_cases.shuffle(seed=1123).select(range(200)) + else: + test_cases = data["validation"] + + # inference + print(f"start inference ...") + tic = time.time() + scorer = rouge_scorer.RougeScorer(['rouge1'], use_stemmer=True) + f1 = 0 + predicts = ["Task,ID,Prediction"] + for x in tqdm(test_cases): + prompt = x["input"] + if args.dataset_version == "tau/scrolls": + prompt = fix_prompt(prompt) + input_ids = tokenizer(prompt).input_ids + new_len = min(SEQ_LEN, len(input_ids)) + input_ids = torch.tensor([input_ids[-new_len:]]).to(model.device) + + output_ids = model.generate(input_ids, max_new_tokens=64, use_cache=False)[0][new_len:] + # output_ids = model.generate(input_ids, max_new_tokens=64, use_cache=False)[0] + outputs = tokenizer.batch_decode([output_ids], skip_special_tokens=True) + predicts.append(f'{args.dataset},{x["id"]},"{outputs[0]}"') + + # print("---------------------") + # print(x["output"]) + # print("---------------------") + # print(prompt) + # print("---------------------") + # print(outputs[0]) + # print("=====================") + max_score = 0 + for l in [512]: + outputs = tokenizer.batch_decode([output_ids[:l]], skip_special_tokens=True) + score = scorer.score(outputs[0], x["output"]) + max_score = max(max_score, score["rouge1"].fmeasure) + f1 += max_score + + f1 /= len(test_cases) + print(f"avg f1 over {len(test_cases)} test cases: {f1:.3f}") + print(f"total inference time: {time.time() - tic:.0f} s") + + with open(output_file, "w") as f: + for line in predicts: + f.write(line + "\n") + diff --git a/evaluation/zeroscrolls/eval_api.py b/evaluation/zeroscrolls/eval_api.py new file mode 100644 index 0000000..4572dbd --- /dev/null +++ b/evaluation/zeroscrolls/eval_api.py @@ -0,0 +1,100 @@ +import argparse +import json +import os +import time +from tqdm import tqdm + +import torch +import numpy as np + +from datasets import load_dataset +from rouge_score import rouge_scorer + +import openai + + +def fix_prompt(prompt): + paragraphs = prompt.split("\n\n") + new_prompt = prompt + "\n\nQuestions:\n" + paragraphs[0] + "\n\nAnswer:\n" + return new_prompt + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model-name-or-path", type=str, default="gpt-3.5") + parser.add_argument("--dataset-version", type=str, default="tau/zero_scrolls") + parser.add_argument("--dataset", type=str, default="qasper") + args = parser.parse_args() + + cut_word_len = 2000 + + # create output dir + name = args.model_name_or_path + output_dir = os.path.join("predictions", name) + if not os.path.exists(output_dir): + os.makedirs(output_dir, exist_ok=True) + output_file = os.path.join(output_dir, f"{name}_{args.dataset}_{cut_word_len}.raw") + print(f"output file: {output_file}") + + # load dataset + data = load_dataset(args.dataset_version, args.dataset) + if args.dataset_version=="tau/scrolls": + test_cases = data["validation"] + test_cases = test_cases.shuffle(seed=1123).select(range(200)) + else: + test_cases = data["validation"] + + # inference + print(f"start inference ...") + tic = time.time() + scorer = rouge_scorer.RougeScorer(['rouge1'], use_stemmer=True) + predicts = ["Task,ID,Prediction"] + f1_scores = {} + for x in tqdm(test_cases): + prompt = x["input"] + if args.dataset_version == "tau/scrolls": + prompt = fix_prompt(prompt) + words = prompt.split(" ") + prompt = " ".join(words[:cut_word_len] + words[-cut_word_len:]) + + messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prompt}, + ] + while True: + try: + ret = openai.ChatCompletion.create( + model=name, messages=messages, temperature=0) + break + except openai.error.OpenAIError as e: + print(type(e), e) + time.sleep(10) + + outputs = [ret["choices"][0]["message"]["content"]] + predicts.append(f'{args.dataset},{x["id"]},"{outputs[0]}"') + + # print("---------------------") + # print(x["output"]) + # print("---------------------") + # print(prompt) + # print("---------------------") + # print(outputs[0]) + # print("=====================") + max_score = 0 + for l in [512]: + score = scorer.score(outputs[0], x["output"]) + max_score = max(max_score, score["rouge1"].fmeasure) + qid = x["id"] + if qid not in f1_scores: + f1_scores[qid] = max_score + else: + f1_scores[qid] = max(max_score, f1_scores[qid]) + + scores = list(f1_scores.values()) + avg_f1 = np.mean(scores) + print(f"avg f1 over {len(test_cases)} test cases: {avg_f1:.3f}") + print(f"total inference time: {time.time() - tic:.0f} s") + + with open(output_file, "w") as f: + for line in predicts: + f.write(line + "\n") diff --git a/evaluation/zeroscrolls/predictions/longchat_13b_16K/longchat_13b_16K_narrative_qa_15000.raw b/evaluation/zeroscrolls/predictions/longchat_13b_16K/longchat_13b_16K_narrative_qa_15000.raw new file mode 100644 index 0000000..f931a39 --- /dev/null +++ b/evaluation/zeroscrolls/predictions/longchat_13b_16K/longchat_13b_16K_narrative_qa_15000.raw @@ -0,0 +1,7 @@ +Task,ID,Predictionnarrative_qa,3858,"Charles goes to work in the morning and Mary stays at home."narrative_qa,3858,"Charles goes to work in the morning and Mary stays at home."narrative_qa,5947,"Shallock Peters"narrative_qa,5947,"Shallock Peters"narrative_qa,12164,"The Mark was divided into three sections: the Upper-Mark, the Middle-Mark, and the Lower-Mark."narrative_qa,12164,"The Mark was divided into three sections: the Upper-Mark, the Middle-Mark, and the Lower-Mark."narrative_qa,23148,"Soames and Beerbohm meet for lunch at the Vingtieme Restaurant in London."narrative_qa,23148,"Soames and Beerbohm meet for lunch at the Vingtieme Restaurant in London."narrative_qa,25278,"Crito persuades Socrates to escape."narrative_qa,25278,"Crito persuades Socrates to escape."narrative_qa,33068,"Lieutenant Shane Wolfe is assigned to rescue Julie Plummer, the daughter of a high-ranking government official who has been kidnapped by a group of North Korean terrorists."narrative_qa,33068,"Lieutenant Shane Wolfe is assigned to rescue Julie Plummer, the daughter of a high-ranking government official who has been kidnapped by a group of North Korean terrorists."narrative_qa,35134,"Jim is not a character in the story. The story is about a group of people in a small town in Florida who are involved in a conflict over a mule bone. The conflict is between two groups of people, one of which is led by Jim and the other by Dave. The story is told from"narrative_qa,35134,"Jim is not a character in the story. The story is about a group of people in a small town in Florida who are involved in a conflict over a mule bone. The conflict is between two groups of people, one of which is led by Jim and the other by Dave. The story is told from"narrative_qa,38322,"The men know the spaceship has ended its voyage because they have +found it, and it is no longer moving. The ship is stationary, and +there are no signs of it moving or attempting to move. The engines +are off, and the crew is dead."narrative_qa,38322,"The men know the spaceship has ended its voyage because they have +found it, and it is no longer moving. The ship is stationary, and +there are no signs of it moving or attempting to move. The engines +are off, and the crew is dead."narrative_qa,42586,"The Inca was disguised as Captain Duval, a waiter."narrative_qa,42586,"The Inca was disguised as Captain Duval, a waiter."narrative_qa,16886,"The Gyro Captain is last seen flying in the twisted wreckage of his gyrocopter, with his rotors gone and engine screaming. It is not known what ultimately happens to him."narrative_qa,16886,"The Gyro Captain is last seen flying in the twisted wreckage of his gyrocopter, with his rotors gone and engine screaming. It is not known what ultimately happens to him." \ No newline at end of file diff --git a/evaluation/zeroscrolls/predictions/longchat_13b_16K/longchat_13b_16K_qasper_15000.raw b/evaluation/zeroscrolls/predictions/longchat_13b_16K/longchat_13b_16K_qasper_15000.raw new file mode 100644 index 0000000..c0116ff --- /dev/null +++ b/evaluation/zeroscrolls/predictions/longchat_13b_16K/longchat_13b_16K_qasper_15000.raw @@ -0,0 +1,22 @@ +Task,ID,Predictionqasper,3fad42be0fb2052bb404b989cc7d58b440cd23a0,"The baselines used are Unif and Stopword."qasper,3fad42be0fb2052bb404b989cc7d58b440cd23a0,"The baselines used are Unif and Stopword."qasper,8bf7f1f93d0a2816234d36395ab40c481be9a0e0,"unanswerable"qasper,8bf7f1f93d0a2816234d36395ab40c481be9a0e0,"unanswerable"qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929,"The 8,640 English sentences in the Equity Evaluation Corpus (EEC) were carefully chosen to tease out gender and race biases in natural language processing systems. The sentences were intended to be short and grammatically simple, and they included expressions of sentiment and emotion. The authors"qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929,"The 8,640 English sentences in the Equity Evaluation Corpus (EEC) were carefully chosen to tease out gender and race biases in natural language processing systems. The sentences were intended to be short and grammatically simple, and they included expressions of sentiment and emotion. The authors"qasper,518dae6f936882152c162058895db4eca815e649,"The UTCNN model has three convolutional layers."qasper,58ef2442450c392bfc55c4dc35f216542f5f2dbb,"Yes"qasper,58ef2442450c392bfc55c4dc35f216542f5f2dbb,"Yes"qasper,290ee79b5e3872e0496a6a0fc9b103ab7d8f6c30,"The three layers of the annotation scheme are Offensive Language Detection (Level A), Categorization of Offensive Language (Level B), and Offensive Language Target Identification (Level C)."qasper,ab9b0bde6113ffef8eb1c39919d21e5913a05081,"Their results on both datasets show significant improvements in error detection performance when using artificially generated data. The pattern-based approach consistently outperforms the system by Felice2014a, and the combination of the pattern-based method with the machine translation approach gives the best overall performance on all datasets."qasper,ff338921e34c15baf1eae0074938bf79ee65fdd2,"The baseline model for the BioASQ competition was a BERT-based model fine-tuned on the BioASQ training data."qasper,1b1a30e9e68a9ae76af467e60cefb180d135e285,"Their created dataset consists of 353 conversations from 40 speakers (11 nurses, 16 patients, and 13 caregivers) with consent to the use of anonymized data for research. The speakers are 38 to 88 years old"qasper,dea9e7fe8e47da5e7f31d9b1a46ebe34e731a596,"The baseline classification system uses a supervised classification system which includes pre-processing of the tweets in the dataset and the feature extraction followed by the method used to identify humor in tweets."qasper,dea9e7fe8e47da5e7f31d9b1a46ebe34e731a596,"The baseline classification system uses a supervised classification system which includes pre-processing of the tweets in the dataset and the feature extraction followed by the method used to identify humor in tweets."qasper,3355918bbdccac644afe441f085d0ffbbad565d7,"The supervised scores of the words are calculated using the following formula: + +$w\_t$ = $w\_t \cdot \frac{N\_t}{N}$ + +where $w\_t$ is the sentiment score of word $t$, $N\_t$ is the number of documents"qasper,d9980676a83295dda37c20cfd5d58e574d0a4859,"The article introduces the use of backward translation (BT) and forward translation (FT) as data simulation techniques for neural machine translation. Backward translation involves translating a sentence from the target language back into the source language, while forward translation involves translating a sentence from the source language to the target language."qasper,d9980676a83295dda37c20cfd5d58e574d0a4859,"The article introduces the use of backward translation (BT) and forward translation (FT) as data simulation techniques for neural machine translation. Backward translation involves translating a sentence from the target language back into the source language, while forward translation involves translating a sentence from the source language to the target language."qasper,79a44a68bb57b375d8a57a0a7f522d33476d9f33,"The qualitative metrics used for evaluation are: + +1. Relation Generation (RG) +2. Content Selection (CS) +3. Content Ordering (CO) +4. BLEU score + +These metrics are designed to evaluate the model's ability to integrate elements from the table"qasper,79a44a68bb57b375d8a57a0a7f522d33476d9f33,"The qualitative metrics used for evaluation are: + +1. Relation Generation (RG) +2. Content Selection (CS) +3. Content Ordering (CO) +4. BLEU score + +These metrics are designed to evaluate the model's ability to integrate elements from the table"qasper,76ed74788e3eb3321e646c48ae8bf6cdfe46dca1,"The article uses several linguistics features for diacritic recovery, including CHAR (characters), SEG (character segmentation), PRIOR (observed diacritized forms in the training set), and CASE (whether the letter expects a core word diacritic or a case ending). Additionally, the"qasper,8e52637026bee9061f9558178eaec08279bf7ac6,"The training data was translated from English to Spanish using the machine translation platform Apertium BIBREF5 ."qasper,8e52637026bee9061f9558178eaec08279bf7ac6,"The training data was translated from English to Spanish using the machine translation platform Apertium BIBREF5 ."qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59,"The proposed model is compared to several baseline models, including traditional models that use TF-IDF features and deep models such as Doc2vec, LSTM, LSTM-self, LSTM-soft, and LSTM-attention."qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59,"The proposed model is compared to several baseline models, including traditional models that use TF-IDF features and deep models such as Doc2vec, LSTM, LSTM-self, LSTM-soft, and LSTM-attention."qasper,4e748cb2b5e74d905d9b24b53be6cfdf326e8054,"The traditional linguistics features used were unigrams and pragmatic features."qasper,b970f48d30775d3468952795bc72976baab3438e,"The article tackles several issues that are not on the forefront of computational text analysis. Some of these issues include: + +1. The challenges of working with culturally and socially situated texts that reflect the ideas, values, and beliefs of both their authors and their target audiences. +"qasper,c70bafc35e27be9d1efae60596bc0dd390c124c0,"Yes" \ No newline at end of file diff --git a/evaluation/zeroscrolls/predictions/longchat_32K_interpolate/longchat_32K_interpolate_narrative_qa_15000.raw b/evaluation/zeroscrolls/predictions/longchat_32K_interpolate/longchat_32K_interpolate_narrative_qa_15000.raw new file mode 100644 index 0000000..f319345 --- /dev/null +++ b/evaluation/zeroscrolls/predictions/longchat_32K_interpolate/longchat_32K_interpolate_narrative_qa_15000.raw @@ -0,0 +1 @@ +Task,ID,Predictionnarrative_qa,3858,"Charles and Mary are wed, and immediately after their wedding, they go on a honeymoon."narrative_qa,3858,"Charles and Mary are wed, and immediately after their wedding, they go on a honeymoon."narrative_qa,5947,"Shallock Peters, the promoter, becomes infuriated with Tom when he will not sell some of his inventions."narrative_qa,5947,"Shallock Peters, the promoter, becomes infuriated with Tom when he will not sell some of his inventions."narrative_qa,12164,"The Mark was divided into 12 sections."narrative_qa,12164,"The Mark was divided into 12 sections."narrative_qa,23148,"Soames and Beerbohm met for lunch at the Vingtieme, a restaurant in London."narrative_qa,23148,"Soames and Beerbohm met for lunch at the Vingtieme, a restaurant in London."narrative_qa,25278,"Crito, Socrates' friend and companion, attempts to persuade Socrates to escape from prison."narrative_qa,25278,"Crito, Socrates' friend and companion, attempts to persuade Socrates to escape from prison."narrative_qa,33068,"Lieutenant Shane Wolfe is assigned to rescue Julie Plummer."narrative_qa,33068,"Lieutenant Shane Wolfe is assigned to rescue Julie Plummer."narrative_qa,35134,"Jim represents himself in the trial."narrative_qa,35134,"Jim represents himself in the trial."narrative_qa,38322,"The men know the spaceship has ended its voyage because they see the spaceship's engines stop working. The engines are the source of the spaceship's power, and when they stop working, the spaceship can no longer move."narrative_qa,38322,"The men know the spaceship has ended its voyage because they see the spaceship's engines stop working. The engines are the source of the spaceship's power, and when they stop working, the spaceship can no longer move."narrative_qa,42586,"The Inca disguised as when he met Ermyntrude was Captain Duval."narrative_qa,42586,"The Inca disguised as when he met Ermyntrude was Captain Duval."narrative_qa,16886,"The Gyro Captain is killed when he crashes into a tree during the chase scene."narrative_qa,16886,"The Gyro Captain is killed when he crashes into a tree during the chase scene." \ No newline at end of file diff --git a/evaluation/zeroscrolls/predictions/longchat_32K_interpolate/longchat_32K_interpolate_qasper_15000.raw b/evaluation/zeroscrolls/predictions/longchat_32K_interpolate/longchat_32K_interpolate_qasper_15000.raw new file mode 100644 index 0000000..f49ce95 --- /dev/null +++ b/evaluation/zeroscrolls/predictions/longchat_32K_interpolate/longchat_32K_interpolate_qasper_15000.raw @@ -0,0 +1,354 @@ +Task,ID,Prediction +qasper,be6971827707afcd13af3085d0a775a0bd61c5dd," + +Table 2: Corpus statistics + +* Total occurrences: 82836 +* Spells occurrences: 432 +* Total words: 1111 +* Average length of a scene description: 1111 +* Average length of" +qasper,a33ab5ce8497ff63ca575a80b03e0ed9c6acd273,"" +qasper,cc28919313f897358ef864948c65318dc61cb03c,"" +qasper,42bc4e0cd0f3e238a4891142f1b84ebcd6594bf1,"" +qasper,974868e4e22f14766bcc76dc4927a7f2795dcd5e,"" +qasper,f7789313a804e41fcbca906a4e5cf69039eeef9f,"" +qasper,427252648173c3ba78c211b86fa89fc9f4406653,"" +qasper,551a17fc1d5b5c3d18bdc4923363cbbda7eb2516,"" +qasper,f319f2c3f9339b0ce47478f5aa0c32da387a156e,"" +qasper,5f6fbd57cce47f20a0fda27d954543c00c4344c2,"" +qasper,9a596bd3a1b504601d49c2bec92d1592d7635042,"The authors would like to thank the reviewers for their valuable feedback and suggestions." +qasper,12eba1598dca14db64dbc8b73484639363a4618e,"" +qasper,4a4b7c0d3e7365440b49e9e6b67908ea5cea687d,"" +qasper,7e54c7751dbd50d9d14b9f8b13dc94947a46e42f,"" +qasper,b69f0438c1af4b9ed89e531c056d9812d4994016," + +\section*{Acknowledgments} + +The authors would like to thank the Danish Research Council for funding this research under the grant DFF - 6107-0013. The authors would also like to thank the reviewers for their valuable feedback and suggestions." +qasper,c38a48d65bb21c314194090d0cc3f1a45c549dd6,"" +qasper,61fb982b2c67541725d6db76b9c710dd169b533d,"" +qasper,13e87f6d68f7217fd14f4f9a008a65dd2a0ba91c,"" +qasper,c3a9732599849ba4a9f07170ce1e50867cf7d7bf,"" +qasper,0481a8edf795768d062c156875d20b8fb656432c,"" +qasper,a3783e42c2bf616c8a07bd3b3d503886660e4344,"" +qasper,c65b6470b7ed0a035548cc08e0bc541c2c4a95a7,"" +qasper,fd5412e2784acefb50afc3bfae1e087580b90ab9,"" +qasper,32e8eda2183bcafbd79b22f757f8f55895a0b7b2,"" +qasper,b2c8c90041064183159cc825847c142b1309a849,"The model's effectiveness is demonstrated through various experiments and case studies. The proposed method can be further improved by incorporating more advanced techniques in the future." +qasper,d3092f78bdbe7e741932e3ddf997e8db42fa044c," +In conclusion, our platform demonstrates the potential of big data architectures for real-time prediction and learning. The platform can be adapted to work on any real-time changing market trend, and its scalability and fault tolerance make it suitable for a wide range of applications. Future work will focus on extending" +qasper,2a0a44f169ad61774d77df65f8846bd57685bfcf,"" +qasper,36a9230fadf997d3b0c5fc8af8d89bd48bf04f12," + +In this work, we present a multi-task learning framework for sentence encoding that leverages the inductive biases of different tasks to improve the quality of sentence representations. We use a combination of sequence-to-sequence tasks, such as multi-lingual NMT, constituency parsing, and skip-" +qasper,05238d1fad2128403577822aa4822ef8ca9570ac," +Our tensor-based embeddings show significant improvements in various NLP tasks, including outlier detection, sentiment analysis, and analogy recovery. This suggests that the use of tensor factorization in learning word embeddings can lead to better performance in a wide range of NLP tasks. +Our tensor" +qasper,36b5f0f62ee9be1ab50d1bb6170e98328d45997d,"In this case, the classification of the best and worst songs within a specific genre could be a relevant task to be addressed. + +---" +qasper,271019168ed3a2b0ef5e3780b48a1ebefc562b57,"The experiments on Yorùbá were possible thanks to the collaboration with the Hausa community in Nigeria and the support of the Global Voices project." +qasper,79b174d20ea5dd4f35e25c9425fb97f40e27cd6f,"" +qasper,f1bd66bb354e3dabf5dc4a71e6f08b17d472ecc9,"This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Research Chairs program." +qasper,3bfb8c12f151dada259fbd511358914c4b4e1b0e,"" +qasper,f4e17b14318b9f67d60a8a2dad1f6b506a10ab36,"" +qasper,b54fc86dc2cc6994e10c1819b6405de08c496c7b,"" +qasper,3371d586a3a81de1552d90459709c57c0b1a2594,"" +qasper,85aa125b3a15bbb6f99f91656ca2763e8fbdb0ff,"" +qasper,6bff681f1f6743ef7aa6c29cc00eac26fafdabc2,"" +qasper,f858031ebe57b6139af46ee0f25c10870bb00c3c,"" +qasper,e93b4a15b54d139b768d5913fb5fd1aed8ab25da,"The learning rate was set to 0.002 for the first 1000 steps, then linearly decayed to 0.0001 for the next 8000 steps, and finally linearly decayed to 0.00001 for the last " +qasper,0062ad4aed09a57d0ece6aa4b873f4a4bf65d165,"" +qasper,60cb756d382b3594d9e1f4a5e2366db407e378ae,"" +qasper,b3ff166bd480048e099d09ba4a96e2e32b42422b," + +\section*{Acknowledgments} + +The authors would like to thank the reviewers for their valuable feedback and suggestions. This work was supported by the National Natural Science Foundation of China (Grant No. 6170277) and the Fundamental Research Funds for the" +qasper,51d03f0741b72ae242c380266acd2321baf43444," + +References + +1. BIBREF0. Blei, D. M., Ng, A. Y., & Jordan, M. I. (200). Latent semantic analysis. Journal of artificial intelligence research, 10(1-2), 174-19" +qasper,1eeabfde99594b8d9c6a007f50b97f7f527b0a17,"" +qasper,3e3f5254b729beb657310a5561950085fa690e83,"This can be further improved by incorporating more advanced techniques like deep learning and natural language processing." +qasper,09621c9cd762e1409f22d501513858d67dcd3c7c,"" +qasper,05e3b831e4c02bbd64a6e35f6c52f0922a41539a,"" +qasper,1c85a25ec9d0c4f6622539f48346e23ff666cd5f,"" +qasper,f60629c01f99de3f68365833ee115b95a3388699," + +---" +qasper,21a9f1cddd7cb65d5d48ec4f33fe2221b2a8f62e,"gitlab.com/[your\_username]/mmhs150k\_dataset.git" +qasper,02417455c05f09d89c2658f39705ac1df1daa0cd,"" +qasper,4904ef32a8f84cf2f53b1532ccf7aa77273b3d19," + +In this paper, we have presented a new architecture for style transfer in natural language processing. Our proposed architecture, called the Style Transfer Encoder (STE), is designed to learn disentangled latent representations that can be used to generate text with different styles. We have shown that our architecture can out" +qasper,3aa7173612995223a904cc0f8eef4ff203cbb860,"" +qasper,9a4aa0e4096c73cd2c3b1eab437c1bf24ae7bf03,"" +qasper,059acc270062921ad27ee40a77fd50de6f02840a," +Call Mike on his cell +Sorry, I don't have a cell phone number for Mike Johnson. I only have a work phone. Do you want to call that number? +no +Oh, sorry about that. Goodbye. +Call Mike on his cell +Sorry, I don't" +qasper,acc512c57aef4d5a15c15e3593f0a9b3e7e7e8b8,"" +qasper,d325a3c21660dbc481b4e839ff1a2d37dcc7ca46,"" +qasper,4c7b29f6e3cc1e902959a1985146ccc0b15fe521,"" +qasper,1951cde612751410355610074c3c69cec94824c2,"" +qasper,37db7ba2c155c2f89fc7fb51fffd7f193c103a34,"" +qasper,aa4b38f601cc87bf93849245d5f65124da3dc112," + +--- + +BIBREF0: Blei, D. M., Ng, A. Y., & Jordan, M. I. (201). Latent semantic analysis. Journal of Machine Learning Research, 12, 317-337. +BIBREF1" +qasper,d653d994ef914d76c7d4011c0eb7873610ad795f,"Score +The following table presents a list of the top 50 hashtags sorted by their average word happiness score. The table displays the hashtag, the number of tweets containing the hashtag, and the average word happiness score for each hashtag. + +Table A5: Top" +qasper,d8de12f5eff64d0e9c9e88f6ebdabc4cdf042c22,"" +qasper,f6a1125c5621a2f32c9bcdd188dff14efa096083,"" +qasper,f4238f558d6ddf3849497a130b3a6ad866ff38b3," + +References + +1. BIBREF0 (BIBREF0), "Affirmative Ethics", 202. +2. BIBREF5 (BIBREF5), "Implicit Associations in Word Embeddings", 201. +3. B" +qasper,79bb1a1b71a1149e33e8b51ffdb83124c18f3e9c,"" +qasper,5633d93ef356aca02592bae3dfc1b3ec8fce27dc,"" +qasper,44c7c1fbac80eaea736622913d65fe6453d72828,"We also appreciate the help from the Amazon Alexa team, especially for the Alexa Prize BIBREF0. We would like to thank the users who participated in the Gunrock conversations, without whom this research would not be possible." +qasper,5dfa59c116e0ceb428efd99bab19731aa3df4bbd," +Appendix ::: Quality Control Measures +We implemented several quality control measures to ensure the quality of the annotations: + +1. In-browser checks: We used in-browser checks to ensure that workers were following the instructions and to catch any errors or inconsistencies in the annotations. +2." +qasper,87159024d4b6dac8c456bb74a91044df292f6b99,"" +qasper,af073d84b8a7c968e5822c79bef34a28655886de," + +In our experiments, we used the attention mechanism to combine the output of the encoder and the decoder in the machine translation models. We found that the attention mechanism significantly improved the quality of the translation compared to the baseline models without the attention mechanism." +qasper,cbf1137912a47262314c94d36ced3232d5fa1926,"" +qasper,7997b9971f864a504014110a708f215c84815941,"For more specific research, we recommend the papers on the Hashtag Sentiment Lexicon BIBREF22 and the Sentiment140 lexicon BIBREF140 . For a more comprehensive understanding of the field, we recommend the book "Sentiment Analysis and Opinion Mining" +qasper,eced6a6dffe43c28e6d06ab87eed98c135f285a3,"" +qasper,6aed1122050b2d508dc1790c13cdbe38ff126089,"" +qasper,c4a0c7b6f1a00f3233a5fe16240a98d9975701c0,"" +qasper,cf74ff49dfcdda2cd67a896b4b982a1c3ee51531,"The experiments on Yorùbá were possible thanks to the collaboration with the Hausa community in Nigeria and the support of the Nigerian Linguistic Society." +qasper,e9d9bb87a5c4faa965ceddd98d8b80d4b99e339e,"" +qasper,9adcc8c4a10fa0d58f235b740d8d495ee622d596,"" +qasper,0e510d918456f3d2b390b501a145d92c4f125835," + +In summary, our experiments show that self-attention layers applied to images can express any convolutional layer, and that learned fully-attentional models do behave similar to CNNs in practice. We also demonstrated that the quadratic positional encoding is sufficient for learning meaningful attention patterns in images." +qasper,1a43df221a567869964ad3b275de30af2ac35598,"" +qasper,92240eeab107a4f636705b88f00cefc4f0782846,"" +qasper,12cfbaace49f9363fcc10989cf92a50dfe0a55ea,"We also thank the anonymous reviewers for their valuable feedback." +qasper,39a450ac15688199575798e72a2cc016ef4316b5,"This work is supported by the National Science Foundation (NSF) under Grant No. 171864." +qasper,58edc6ed7d6966715022179ab63137c782105eaf,"" +qasper,76d62e414a345fe955dc2d99562ef5772130bc7e,"This suggests that leveraging thematic commonsense in the form of AskBERT for graph construction directly results in graphs that are more coherent and maintain genre more easily. This is especially true in the case of the fairy-tales where the thematic and everyday commonsense diverge more than" +qasper,87357448ce4cae3c59d4570a19c7a9df4c086bd8,"" +qasper,a6d00f44ff8f83b6c1787e39333e759b0c3daf15," +References + +1. BIBREF0 . Federal Bureau of Investigation. 207. National Gang Threat Assessment. Retrieved from +" +qasper,5d5a571ff04a5fdd656ca87f6525a60e917d6558," + +In conclusion, our proposed model achieves a higher recall rate and better overall performance compared to the baseline models. This work not only demonstrates the feasibility of automatic TCM prescription generation but also provides a solid foundation for further research in this area." +qasper,a4ff1b91643e0c8a0d4cc1502d25ca85995cf428,"" +qasper,96992460cfc5f0b8d065ee427067147293746b7a,"" +qasper,04cab3325e20c61f19846674bf9a2c46ea60c449,"Our results suggest that DeCoAR can effectively capture long-term phonetic structure in unlabeled data, which is crucial for ASR performance." +qasper,649d6dc076251547aece6532f75d00fc99081d2b,"" +qasper,702e2d02c25a2f3f6b1be8ad3d448b502b8ced9c," + +1. Investigate the use of more advanced reward functions, such as those based on the concept of intrinsic motivation or exploration-exploitation trade-offs. +2. Explore the use of more advanced clustering algorithms, such as hierarchical or graph-based clustering methods" +qasper,4d30c2223939b31216f2e90ef33fe0db97e962ac,"We also thank the Swiss National Science Foundation for their financial support." +qasper,827464c79f33e69959de619958ade2df6f65fdee,"" +qasper,aeab5797b541850e692f11e79167928db80de1ea,"" +qasper,858c51842fc3c1f3e6d2d7d853c94f6de27afade,"The authors would like to thank the reviewers for their valuable feedback and suggestions." +qasper,1083ec9a2a33f7fe2b6b51bbcebd2d9aec4b4de2,"" +qasper,4e4d377b140c149338446ba69737ea191c4328d9," + +In conclusion, this work demonstrates that word embeddings can be used for sentiment analysis of citations. However, the performance of word embeddings is not as good as hand-crafted features. Further research is needed to explore other techniques that can automatically learn representations of concepts from unlabeled text" +qasper,3c362bfa11c60bad6c7ea83f8753d427cda77de0," + +In conclusion, our proposed model demonstrates a significant improvement in the automatic generation of TCM prescriptions based on textual symptom descriptions. The proposed model not only effectively addresses the repetition problem but also provides a more accurate representation of the herbs in the prescription. This work paves the" +qasper,3a6559dc6eba7f5abddf3ac27376ba0b9643a908," +In the following, we present a comparison with other methods that were not included in the main comparison as they were using different architectures or approaches. +In Table TABREF38, we present a comparison with the method of BIBREF3, which is based on the Transformer architecture. Our method," +qasper,1baf87437b70cc0375b8b7dc2cfc2830279bc8b5,"Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation." +qasper,770aeff30846cd3d0d5963f527691f3685e8af02,"This portion of questions is not relevant to the task and should not be considered during training. + +In this paper, we present a surprisingly simple method for common sense reasoning with Winograd schema multiple choice questions. Key to our method is the use of language models (LMs), trained on a large amount of" +qasper,bdd8368debcb1bdad14c454aaf96695ac5186b09,"" +qasper,0c1663a7f7750b399f40ef7b4bf19d5c598890ff,"" +qasper,0a3a8d1b0cbac559f7de845d845ebbfefb91135e,"We also thank the students and teachers who participated in the evaluation campaigns." +qasper,587885bc86543b8f8b134c20e2c62f6251195571,"" +qasper,e75f5bd7cc7107f10412d61e3202a74b082b0934,"" +qasper,f129c97a81d81d32633c94111018880a7ffe16d1,"" +qasper,3a8d65eb8e1dbb995981a0e02d86ebf3feab107a,"Our approach is simple, unsupervised, and empirically more effective." +qasper,da544015511e535503dee2eaf4912a5e36c806cd," +In conclusion, our analysis of the Fisher dataset shows that our topic modeling algorithm can learn topics from human translations, and that it can make topic predictions for new text input. However, the algorithm does not seem to be able to track topic drift over conversations effectively. This could be due to the" +qasper,8b43201e7e648c670c02e16ba189230820879228,"" +qasper,a93196fb0fb5f8202912971e14552fd7828976db,"" +qasper,28b2a20779a78a34fb228333dc4b93fd572fda15," +In future work, we plan to investigate the following aspects: + +1. Incorporate uncertainty estimation in the policy network to provide a more robust and reliable solution. +2. Explore the use of other natural language processing techniques, such as attention mechanisms, to improve the understanding of the input" +qasper,10f560fe8e1c0c7dea5e308ee4cec16d07874f1d,"" +qasper,d10e256f2f724ad611fd3ff82ce88f7a78bad7f7,"" +qasper,7920f228de6ef4c685f478bac4c7776443f19f39,"Moreover, the schedule information is not always up-to-date, and it might not always be available. +In conclusion, our work presents a novel approach for detecting musical entities from user-generated content, combining linguistic features with statistical models and extracting contextual information from a radio schedule. We have shown" +qasper,17f5f4a5d943c91d46552fb75940b67a72144697,"" +qasper,b984612ceac5b4cf5efd841af2afddd244ee497a,"We also thank our colleagues for their valuable discussions and suggestions. This work was supported by the National Science Foundation (NSF) under Grant No. 161881." +qasper,e9ccc74b1f1b172224cf9f01e66b1fa9e34d2593," +Table TABREF34: Claim metadata and veracity labels. +Table TABREFREF3: Claim metadata and veracity labels (extended). +Table TABREFREF3: Claim metadata and veracity labels (extended) with number of instances and labels per domain. +" +qasper,e8e6986365f899dead0768ecf7b1eca8a2699f2f,"" +qasper,1ccfd288f746c35006f5847297ab52020729f523,"These examples illustrate the types of expressions of stress that our models struggle with, and we encourage future work to address these challenges." +qasper,d6a27c41c81f12028529e97e255789ec2ba39eaa," + +References +[BIBREF0] BIBREF0." +qasper,2df2f6e4efd19023434c84f5b4f29a2f00bfc9fb,"The performance of TDSM is also competitive with other character-based models and even surpasses some of them. The results also show that TDSM can be a good candidate for real-world industry deployment because of its smaller memory footprint and robustness to changes in vocabulary and missp" +qasper,14e78db206a8180ea637774aa572b073e3ffa219,"The authors would like to thank the Amazon Academic Research Awards program for their support." +qasper,129c03acb0963ede3915415953317556a55f34ee," + +\begin{table}[ht] +\centering +\caption{Comparison of DMN+ to state of the art using bAbI-10k} +\begin{tabular}{|l|l|l|l|l|} +\hline +& \textbf{Mean Error" +qasper,6aee16c4f319a190c2a451c1c099b66162299a28,"Additionally, we plan to explore the use of more advanced techniques, such as attention mechanisms, to better capture the context and generate more emotionally appropriate responses. + +---" +qasper,d94ac550dfdb9e4bbe04392156065c072b9d75e1," + +References + +[1] BIBREF0. Word Sense Disambiguation. In: Handbook of Language Technology, Vol. 3, pp. 11-11. Springer, 201. + +[2] BIBREF1. Word Sense Disambiguation" +qasper,b653f55d1dad5cd262a99502f63bf44c58ccc8cf,"" +qasper,46563a1fb2c3e1b39a185e4cbb3ee1c80c8012b7,"We will be looking at the cognitive and neural mechanisms that underlie this compression effect, and how it might be mitigated or exploited in computational models of metaphor processing." +qasper,ef7b62a705f887326b7ebacbd62567ee1f2129b3,"" +qasper,37753fbffc06ce7de6ada80c89f1bf5f190bbd88,"We will be looking at the cognitive and neural mechanisms that underlie this compression effect, and how it might be mitigated or exploited in computational models of metaphor processing." +qasper,7835d8f578386834c02e2c9aba78a345059d56ca,"" +qasper,63723c6b398100bba5dc21754451f503cb91c9b8," + +\section*{References}" +qasper,4fa851d91388f0803e33f6cfae519548598cd37c,"This study aims to investigate the behavior of the LSTM gating signals during the inference phase. The results show that the gating signals exhibit different patterns for different examples, which can provide valuable insights into the model's decision-making process." +qasper,fe6bb55b28f14ed8ac82c122681905397e31279d,"" +qasper,e21a8581cc858483a31c6133e53dd0cfda76ae4c,"" +qasper,999b20dc14cb3d389d9e3ba5466bc3869d2d6190," + +\bibliographystyle{unsrt} +\bibliography{survey}" +qasper,0427ca83d6bf4ec113bc6fec484b2578714ae8ec," +Rules Index ::: Scan data +Scan data is a dataset of movie titles and their corresponding descriptions. The dataset is split into training and test sets. The training set is used to train the model, and the test set is used to evaluate the model's performance on a set of questions. +" +qasper,a1557ec0f3deb1e4cd1e68f4880dcecda55656dd,"" +qasper,385dc96604e077611fbd877c7f39d3c17cd63bf2,"" +qasper,a2a3af59f3f18a28eb2ca7055e1613948f395052,"ollowing the methodology described in this work, we have analyzed a large number of discussions on social media. We have found that the controversy in these discussions is often related to political events, such as elections, corruption cases or justice decisions. + +In the following, we present a summary" +qasper,100cf8b72d46da39fedfe77ec939fb44f25de77f," + +References + +1. Qin, E., Zhou, T., & Liu, J. (2018). Article commenting: A new task in information retrieval. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process" +qasper,b6a6bdca6dee70f8fe6dd1cfe3bb2c5ff03b1605,"" +qasper,954c4756e293fd5c26dc50dc74f505cc94b3f8cc,"This work has been supported by the French National Research Agency (ANR) under the grant ANR-16-CE23-0012 (project Snips)." +qasper,54fe8f05595f2d1d4a4fd77f4562eac519711fa6,"" +qasper,dc5ff2adbe1a504122e3800c9ca1d348de391c94,"" +qasper,77af93200138f46bb178c02f710944a01ed86481,"We also appreciate the help from the Amazon Alexa Prize team in providing the necessary resources and guidance for our project." +qasper,2a058f8f6bd6f8e80e8452e1dba9f8db5e3c7de8," +E. Additional Visualization Results on Semantic Hierarchies +In this part, we provide more visualization results on semantic hierarchies. We use the same method as that in the main text to visualize the embeddings of several entity pairs. The visualization results are in Figure FIGREF" +qasper,879bec20c0fdfda952444018e9435f91e34d8788,"We also thank the reviewers for their valuable feedback and suggestions. This research was supported by the National Science Foundation (NSF) under Grant No. 174577. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect" +qasper,2d4d0735c50749aa8087d1502ab7499faa2f0dd8,"" +qasper,6b4de7fef3a543215f16042ce6a29186bf84fea4,"" +qasper,b8dea4a98b4da4ef1b9c98a211210e31d6630cf3,"" +qasper,a88f8cae1f59cdc4f1f645e496d6d2ac4d9fba1b,"" +qasper,515e10a71d78ccd9c7dc93cd942924a4c85d3a30,"" +qasper,0bd992a6a218331aa771d922e3c7bb60b653949a," + +References + +1. BIBREF0. The Origin of Species by Means of Natural Selection or the Preservation of Favored Races in the Struggle for Life. +2. BIBREF1. The Descent of Man, and Selection in Relation to Sex. +" +qasper,2ec9c1590c96f17a66c7d4eb95dc5d3a447cb973," +14. Training for human annotators: Did the annotators receive interactive training for this specific annotation task / research project? Training involves some kind of interactive feedback. Simply being given formal instructions or guidelines is not training. Prior professional expertise is not training. Options include: +Some kind of training" +qasper,bd7a95b961af7caebf0430a7c9f675816c9c527f,"Additionally, it would be valuable to investigate the effectiveness of self-attentional models in more complex tasks, such as multi-step dialogue systems, where the user might need to perform multiple tasks in a single conversation. + +\section*{Acknowledgments} + +This work was supported by" +qasper,aa800b424db77e634e82680f804894bfa37f2a34,"" +qasper,1fd969f53bc714d9b5e6604a7780cbd6b12fd616,"" +qasper,4d05a264b2353cff310edb480a917d686353b007,"Future work could involve exploring other hybrid models, such as combining HMMs with other RNN architectures, or investigating other methods for making RNNs more interpretable." +qasper,dcb18516369c3cf9838e83168357aed6643ae1b8," + +In Tables 5 and 6, we present the performance of the best performing baseline for each category of annotated questions. We see that the best performing baseline for Quasar-S is the BiRNN language model, which achieves $33.6\%$ accuracy. For" +qasper,c5171daf82107fce0f285fa18f19e91fbd1215c5,"" diff --git a/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_narrative_qa_15000.raw b/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_narrative_qa_15000.raw new file mode 100644 index 0000000..5e85acd --- /dev/null +++ b/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_narrative_qa_15000.raw @@ -0,0 +1,41 @@ +Task,ID,Prediction +narrative_qa,3858,"Charles and Mary were wed." +narrative_qa,3858,"Charles and Mary were wed." +narrative_qa,5947,"Mr. Damon, who is a friend of the Swift family, becomes infuriated with Tom when he refuses to sell some of his inventions to him." +narrative_qa,5947,"Mr. Damon, who is a friend of the Swift family, becomes infuriated with Tom when he refuses to sell some of his inventions to him."narrative_qa,12164,"The Mark was divided into 12 sections. + +Section 1: The First Section + +Section 2: The Second Section + +Section 3: The Third Section + +Section 4: The Fourth Section + +Section 5: The Fifth Section + +Section 6: The"narrative_qa,12164,"The Mark was divided into 12 sections. + +Section 1: The First Section + +Section 2: The Second Section + +Section 3: The Third Section + +Section 4: The Fourth Section + +Section 5: The Fifth Section + +Section 6: The"narrative_qa,23148,"At the Vingtieme Gourmet. + +The Project Gutenberg Literary Archive Foundation is a +registered 501(c)(3) organization. EIN or +federal tax identification number is 64-6221541. + +Don"narrative_qa,23148,"At the Vingtieme Gourmet. + +The Project Gutenberg Literary Archive Foundation is a +registered 501(c)(3) organization. EIN or +federal tax identification number is 64-6221541. + +Don"narrative_qa,25278,"Crito persuades Socrates to escape."narrative_qa,25278,"Crito persuades Socrates to escape."narrative_qa,33068,"Lieutenant Shane Wolfe is assigned to rescue a high-ranking North Korean official named Mr. Chun."narrative_qa,33068,"Lieutenant Shane Wolfe is assigned to rescue a high-ranking North Korean official named Mr. Chun."narrative_qa,35134,"Jim represents himself in the trial."narrative_qa,35134,"Jim represents himself in the trial."narrative_qa,38322,"The men find a message from the ship's computer, which explains that it has completed its mission and is now on its way back to its home planet. The message also contains instructions on how to operate the ship's controls to return it to its home planet."narrative_qa,38322,"The men find a message from the ship's computer, which explains that it has completed its mission and is now on its way back to its home planet. The message also contains instructions on how to operate the ship's controls to return it to its home planet."narrative_qa,42586,"The Inca was disguised as a waiter when he met Ermyntrude."narrative_qa,42586,"The Inca was disguised as a waiter when he met Ermyntrude."narrative_qa,16886,"The Gyro Captain is never seen again after the scene where he is seen bumping along in the twisted, wreck of the gyrocopter - its rotors gone, engine screaming."narrative_qa,16886,"The Gyro Captain is never seen again after the scene where he is seen bumping along in the twisted, wreck of the gyrocopter - its rotors gone, engine screaming." diff --git a/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_qasper_15000.raw b/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_qasper_15000.raw new file mode 100644 index 0000000..1851e82 --- /dev/null +++ b/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_qasper_15000.raw @@ -0,0 +1,31 @@ +Task,ID,Predictionqasper,3fad42be0fb2052bb404b989cc7d58b440cd23a0,"Unif and Stopword"qasper,3fad42be0fb2052bb404b989cc7d58b440cd23a0,"Unif and Stopword"qasper,8bf7f1f93d0a2816234d36395ab40c481be9a0e0,"No, the authors only analyze and compare five neural models and their variations within the same general framework. They do not specifically analyze transformer-based architectures."qasper,8bf7f1f93d0a2816234d36395ab40c481be9a0e0,"No, the authors only analyze and compare five neural models and their variations within the same general framework. They do not specifically analyze transformer-based architectures."qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929,"The 8,640 English sentences were carefully chosen to tease out biases towards certain races and genders. The sentences were intended to be short and grammatically simple, and some sentences were also intended to include expressions of sentiment and emotion. The sentences were generated using sentence templates that included variables for person"qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929,"The 8,640 English sentences were carefully chosen to tease out biases towards certain races and genders. The sentences were intended to be short and grammatically simple, and some sentences were also intended to include expressions of sentiment and emotion. The sentences were generated using sentence templates that included variables for person"qasper,518dae6f936882152c162058895db4eca815e649,"The UTCNN model has three layers: a user- and topic-dependent document composition layer, a user-topic-comment neural network layer, and a fully connected network layer."qasper,58ef2442450c392bfc55c4dc35f216542f5f2dbb,"unanswerable"qasper,58ef2442450c392bfc55c4dc35f216542f5f2dbb,"unanswerable"qasper,290ee79b5e3872e0496a6a0fc9b103ab7d8f6c30,"Level A: Offensive language Detection +Level B: Categorization of Offensive Language +Level C: Offensive Language Target Identification"qasper,ab9b0bde6113ffef8eb1c39919d21e5913a05081,"The results show that error detection performance is substantially improved by making use of artificially generated data, created by any of the described methods. When comparing the error generation system by Felice2014a (FY14) with our pattern-based (PAT) and machine translation (MT) approaches"qasper,ff338921e34c15baf1eae0074938bf79ee65fdd2,"The baseline model used in the experiments was the BioBERT model with the BERT-base architecture. It was fine-tuned on the BioASQ dataset for the factoid question answering task. The model was trained on the BioASQ training data and evaluated on the BioASQ test data."qasper,1b1a30e9e68a9ae76af467e60cefb180d135e285,"The dataset consists of 353 conversations from 40 speakers (11 nurses, 16 patients, and 13 caregivers) with consent to the use of anonymized data for research. The speakers are 38 to 88 years old, equally"qasper,dea9e7fe8e47da5e7f31d9b1a46ebe34e731a596,"The baseline classification system uses support vector machines with radial basis function kernel and extra tree classifier."qasper,dea9e7fe8e47da5e7f31d9b1a46ebe34e731a596,"The baseline classification system uses support vector machines with radial basis function kernel and extra tree classifier."qasper,3355918bbdccac644afe441f085d0ffbbad565d7,"The supervised scores of the words are calculated using the following formula: + +Supervised score = (minimum polarity score + maximum polarity score + average polarity score) / 3"qasper,d9980676a83295dda37c20cfd5d58e574d0a4859,"The article discusses several data simulation techniques for Neural Machine Translation (NMT) systems, including: + +1. Back-translation (BT): generating artificial parallel data by translating a source text back into the target language. +2. Forward translation (FT): generating synthetic parallel data"qasper,d9980676a83295dda37c20cfd5d58e574d0a4859,"The article discusses several data simulation techniques for Neural Machine Translation (NMT) systems, including: + +1. Back-translation (BT): generating artificial parallel data by translating a source text back into the target language. +2. Forward translation (FT): generating synthetic parallel data"qasper,79a44a68bb57b375d8a57a0a7f522d33476d9f33,"The qualitative metrics used for evaluation are: + +1. Relation Generation (RG) +2. Content Selection (CS) +3. Content Ordering (CO) + +Please note that for CS, CO, RG-% and BLEU metrics, higher is better; which is not true"qasper,79a44a68bb57b375d8a57a0a7f522d33476d9f33,"The qualitative metrics used for evaluation are: + +1. Relation Generation (RG) +2. Content Selection (CS) +3. Content Ordering (CO) + +Please note that for CS, CO, RG-% and BLEU metrics, higher is better; which is not true"qasper,76ed74788e3eb3321e646c48ae8bf6cdfe46dca1,"The paper uses a variety of linguistics features for diacritic recovery, including: + +* CHAR: characters +* SEG: position of the character in a word segment +* PRIOR: diacritized forms seen in the training set +* CASE: whether the letter expects a core word diacrit"qasper,8e52637026bee9061f9558178eaec08279bf7ac6,"The training data was translated by using the machine translation platform Apertium to translate the datasets into Spanish."qasper,8e52637026bee9061f9558178eaec08279bf7ac6,"The training data was translated by using the machine translation platform Apertium to translate the datasets into Spanish."qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59,"The proposed model is compared to a variety of baseline models, including traditional models that use features such as part-of-speech (POS) and Term Frequency-Inverse Document Frequency (TF-IDF), as well as deep learning models that use pre-trained word embeddings and neural"qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59,"The proposed model is compared to a variety of baseline models, including traditional models that use features such as part-of-speech (POS) and Term Frequency-Inverse Document Frequency (TF-IDF), as well as deep learning models that use pre-trained word embeddings and neural"qasper,4e748cb2b5e74d905d9b24b53be6cfdf326e8054,"The traditional linguistics features used in the study include unigrams, pragmatic features, stylistic patterns, and hashtag interpretations."qasper,b970f48d30775d3468952795bc72976baab3438e,"The article discusses several issues that are not on the forefront of computational text analysis, including: + +1. The challenges of measuring social and cultural concepts using computational methods, such as hate speech. +2. The need for interdisciplinary collaboration and communication between humanities scholars and computer"qasper,c70bafc35e27be9d1efae60596bc0dd390c124c0,"Yes, the answers are double (and not triple) annotated." \ No newline at end of file diff --git a/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_qasper_5000.raw b/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_qasper_5000.raw new file mode 100644 index 0000000..a2af363 --- /dev/null +++ b/evaluation/zeroscrolls/predictions/longchat_7b_16K/longchat_7b_16K_qasper_5000.raw @@ -0,0 +1,13 @@ +Task,ID,Predictionqasper,3fad42be0fb2052bb404b989cc7d58b440cd23a0,""qasper,3fad42be0fb2052bb404b989cc7d58b440cd23a0,""qasper,8bf7f1f93d0a2816234d36395ab40c481be9a0e0,"Yes, the authors analyze transformer-based architectures in their study. They compare the performance of several models, including the original transformer-based BERT model, the RoBERTa model, and the SSE model, which is a sentence-pair-based model. They also experiment with different variations of"qasper,8bf7f1f93d0a2816234d36395ab40c481be9a0e0,"Yes, the authors analyze transformer-based architectures in their study. They compare the performance of several models, including the original transformer-based BERT model, the RoBERTa model, and the SSE model, which is a sentence-pair-based model. They also experiment with different variations of"qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929,"The 8,640 English sentences were selected from the AIT2018 Distant Supervision Corpus, which has tweets with emotion-related query terms. The selection process was as follows: + +1. We first filtered out tweets containing emotion-related query terms (e"qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929,"The 8,640 English sentences were selected from the AIT2018 Distant Supervision Corpus, which has tweets with emotion-related query terms. The selection process was as follows: + +1. We first filtered out tweets containing emotion-related query terms (e"qasper,518dae6f936882152c162058895db4eca815e649,"The UTCNN model has three convolutional layers."qasper,58ef2442450c392bfc55c4dc35f216542f5f2dbb,""qasper,58ef2442450c392bfc55c4dc35f216542f5f2dbb,""qasper,290ee79b5e3872e0496a6a0fc9b103ab7d8f6c30,""qasper,ab9b0bde6113ffef8eb1c39919d21e5913a05081,""qasper,ff338921e34c15baf1eae0074938bf79ee65fdd2,"The baseline model used in our experiments was the BioBERT model."qasper,1b1a30e9e68a9ae76af467e60cefb180d135e285,"The dataset consists of 353 conversations from 40 speakers (11 nurses, 16 patients, and 13 caregivers) with consent to the use of anonymized data for research. The speakers are 38 to 88 years old, equally"qasper,dea9e7fe8e47da5e7f31d9b1a46ebe34e731a596,""qasper,dea9e7fe8e47da5e7f31d9b1a46ebe34e731a596,""qasper,3355918bbdccac644afe441f085d0ffbbad565d7,"The supervised scores of the words are calculated using the training data. For a target word in the corpus, we scan through all of its contexts. In addition to the target word's polarity score (the self score), out of all the polarity scores of words occurring in the same contexts"qasper,d9980676a83295dda37c20cfd5d58e574d0a4859,"In this paper, we introduced several data simulation techniques to generate synthetic parallel data for NMT. These techniques include: + +1. Back-translation (BT): This technique involves translating a source sentence back into the source language and then translating it back into the target language. This process is repeated"qasper,d9980676a83295dda37c20cfd5d58e574d0a4859,"In this paper, we introduced several data simulation techniques to generate synthetic parallel data for NMT. These techniques include: + +1. Back-translation (BT): This technique involves translating a source sentence back into the source language and then translating it back into the target language. This process is repeated"qasper,79a44a68bb57b375d8a57a0a7f522d33476d9f33,"The qualitative metrics used for evaluation are Content Selection (CS), Content Ordering (CO), and Relation Generation (RG). These metrics are designed to measure the ability of the model to integrate elements from the table in its descriptions and to generate text containing factual (i.e., correct) records."qasper,79a44a68bb57b375d8a57a0a7f522d33476d9f33,"The qualitative metrics used for evaluation are Content Selection (CS), Content Ordering (CO), and Relation Generation (RG). These metrics are designed to measure the ability of the model to integrate elements from the table in its descriptions and to generate text containing factual (i.e., correct) records."qasper,76ed74788e3eb3321e646c48ae8bf6cdfe46dca1,"In this paper, we used a variety of linguistics features to improve the diacritization of Arabic words. These features include: + +1. CHAR: the characters in the word. +2. SEG: the position of the character in a word segment. +3. PRIOR: the dia"qasper,8e52637026bee9061f9558178eaec08279bf7ac6,""qasper,8e52637026bee9061f9558178eaec08279bf7ac6,""qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59,""qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59,""qasper,4e748cb2b5e74d905d9b24b53be6cfdf326e8054,"The traditional linguistics features used in this study include unigrams, pragmatic features, stylistic patterns, and hashtag interpretations."qasper,b970f48d30775d3468952795bc72976baab3438e,"The article does not explicitly tackle any specific issues in computational text analysis. However, it does discuss some challenges and limitations that are commonly encountered in the field, such as the need for careful pre-processing of data, the potential for latching on to spurious features, and the importance of validation and"qasper,c70bafc35e27be9d1efae60596bc0dd390c124c0,"Yes, the answers are double (and not triple) annotated. This means that each answer is reviewed by two different experts, and if there is any disagreement between the two annotations, a third expert is consulted to resolve the disagreement. This process helps to ensure the accuracy and reliability" \ No newline at end of file diff --git a/evaluation/zeroscrolls/predictions/vicuna-13b-v1.3/vicuna-13b-v1.3_narrative_qa_15000.raw b/evaluation/zeroscrolls/predictions/vicuna-13b-v1.3/vicuna-13b-v1.3_narrative_qa_15000.raw new file mode 100644 index 0000000..ff34daa --- /dev/null +++ b/evaluation/zeroscrolls/predictions/vicuna-13b-v1.3/vicuna-13b-v1.3_narrative_qa_15000.raw @@ -0,0 +1,297 @@ +Task,ID,Prediction +narrative_qa,3858," + + + + + +******* S" +narrative_qa,3858," + + + + + +******* S" +narrative_qa,5947,"Please donate to the Project-tm, the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the" +narrative_qa,5947,"Please donate to the Project-tm, the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the Project-tm +Please donate to the" +narrative_qa,12164," + + + + + +1. Gutenberg-tm.org/donate + + + + + +1. + +1. + +1. +1 +. +1 +. +1 +. +1 +. +1 +. +1 +1 +. +1 +1" +narrative_qa,12164," + + + + + +1. Gutenberg-tm.org/donate + + + + + +1. + +1. + +1. +1 +. +1 +. +1 +. +1 +. +1 +. +1 +1 +. +1 +1" +narrative_qa,23148," + + + + + +[The Project-1. Gutenberg-tm" +narrative_qa,23148," + + + + + +[The Project-1. Gutenberg-tm" +narrative_qa,25278," + + + + + +Translated by Benjamin + + + +Jowett + + + + +This is a computer-generated by + + + + + +Software + + +[1" +narrative_qa,25278," + + + + + +Translated by Benjamin + + + +Jowett + + + + +This is a computer-generated by + + + + + +Software + + +[1" +narrative_qa,33068,"
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+" diff --git a/evaluation/zeroscrolls/predictions/vicuna-13b-v1.3/vicuna-13b-v1.3_qasper_15000.raw b/evaluation/zeroscrolls/predictions/vicuna-13b-v1.3/vicuna-13b-v1.3_qasper_15000.raw new file mode 100644 index 0000000..9696eaf --- /dev/null +++ b/evaluation/zeroscrolls/predictions/vicuna-13b-v1.3/vicuna-13b-v1.3_qasper_15000.raw @@ -0,0 +1,2598 @@ +Task,ID,Prediction +qasper,f7a89b9cd2792f23f2cb43d50a01b8218a6fbb24," + + + + +1." +qasper,b14217978ad9c3c9b6b1ce393b1b5c6e7f49ecab," + + + + + +Introduction the relationship is significant in classification is extremely important in retrieval or + + + + + + + +1." +qasper,61652a3da85196564401d616d251084a25ab4596," + + + + +Performance appraisal is important for organizations particularly crucially depend on the size of employees and expertise and the process is measured by organizations. It enables organizations. It provides a mechanism to evaluate every employee's performance. Performance is periodically measured by the process and activities. It provides a" +qasper,81a35b9572c9d574a30cc2164f47750716157fc8," + + + + +1. What is the impact of pre-trained models? +2. +2. + + +3. What is the impact of pre-trained models? +4. +4. +5. +6. +7. +What is the impact of models? +" +qasper,bd419f4094186a5ce74ba6ac1622b24e29e553f4," + + + + + +Introduction and background + + + +The task of interpreting and following natural language instructions involves the representation of navigation in urban areas involves the representation of the world. + + + + + + + + + +\end-to" +qasper,df0257ab04686ddf1c6c4d9b0529a7632330b98e," + + + + + +\end + +\end" +qasper,197b276d0610ebfacd57ab46b0b29f3033c96a40," + + + + +Performance appraisal is an important for organizations particularly dependent on skills and expertise and it depends on the workforce. It enables organizations to evaluate their performance. Performance appraisal is an important for organizations to measure every employee's activities. Performance appraisal is important for organizations to" +qasper,ca4daafdc23f4e23d933ebabe682e1fe0d4b95ed," + + + + +Introduction + + +Language identification is the first step in natural language processing and machine comprehension is an important in many + + + + + + + +```" +qasper,b5e4866f0685299f1d7af267bbcc4afe2aab806f," + + + + +The source of the text is a research paper is written in a mix of sentences from different languages and contains a variety of sentences and is written in different languages, including Armenian and it is not clear that the task of entity recognition. 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It enables organizations to measure their performance periodically and track the progress. Performance appraisal is a mechanism to measure the day-by-by-day activities. It" +qasper,71ba1b09bb03f5977d790d91702481cc406b3767," + + + + + +1. + + +1. + + +1. + + +1 + +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +" +qasper,252a645af9876241fb166e5822992ce17fec6eb6," + + + + +The textuality of the distribution of text is typically fast and the distribution of the article is very short for this reason, the average of the title is the distribution of the article is the reflection of the phenomenon of the proliferation of click-baits of text, for this" +qasper,de3b1145cb4111ea2d4e113f816b537d052d9814," + + + + +There have been many advances in machine learning methods which help machines understand human behavior better than the emotion detection of emotions. One of the most important aspect of human behavior. 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Neural Networks have been employed in industry to solve various NLP tasks such as text classification, sequence, sentiment analysis, question-answering, etc. +2. + + +NN often engineers address NLP tasks often employ DNN to solve NLP challeng" +qasper,dac087e1328e65ca08f66d8b5307d6624bf3943f," + + + + + +This paper is an unsupervised approach to Twitter and unsupervised method is an approach to spam" +qasper,6424e442b34a576f904d9649d63acf1e4fdefdfc," + + + + +1." +qasper,8c852fc29bda014d28c3ee5b5a7e449ab9152d35," + + + + + +Offensive content has become pervasive media and media and social media has been a reason for organizations and social media. One of the most common strategies to tackle the problem of content, One of the problem is capable of recognizing offensive, media. One of the most strateg" +qasper,7fa3c2c0cf7f559d43e84076a9113a390c5ba03a," + + + + + +1. Winograms + + + + +1. A schema +2. 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One common strategy to tackle the problem is to tackle systems is capable of recognizing offensive content which can be deleted or set aside." +qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59," + + + + + +1." +qasper,114934e1a1e818630ff33ac5c4cd4be6c6f75bb2," + + + + + +1." +qasper,96b07373756d7854bccc3c12e8d41454ab8741f5," + + + + + +Does RoBERT" +qasper,88e5d37617e14d6976cc602a168332fc23644f19," + + + + + +Introduction + + +Chapter 1." +qasper,220d11a03897d85af91ec88a9b502815c7d2b6f3," + + + + +1. + +1. + + +1. + + +1. + + +1. + +1. +1. +1. +1. +1. +1. +1. +1. +1. +1 +1 +1 +1 +1" +qasper,fb5fb11e7d01b9f9efe3db3417b8faf4f8d6931f," + + + + +Introduction +large models are presented in this text." +qasper,cc354c952b5aaed2d4d1e932175e008ff2d801dd," + + + + + +1." +qasper,06eb9f2320451df83e27362c22eb02f4a426a018," + + + + + +1. What is the level 0 (raw text +2 text +3 text +4 text +5 text +6 articles +7 documents +8 papers +10.5 words +10 words +10 sentences +10.5 sentences +10.3 paragraph" +qasper,41b70699514703820435b00efbc3aac4dd67560a," + + + + + +1." +qasper,184b0082e10ce191940c1d24785b631828a9f714," + + + + + + +This paper refutes the common belief that automatic summarization is not reliable." +qasper,cf171fad0bea5ab985c53d11e48e7883c23cdc44,"The paper presents an overview of the dataset used in Section 2, the existing works in Section 3, describes the experimental results in Section 4, and the main contributions in Section 5, and the related works in the literature in Section 6. The paper concludes the word vectors in Section 7" +qasper,5bc1dc6ebcb88fd0310b21d2a74939e35a4c1a11," + + + + + +Most NLI + + + +1. + +1. + + +1. + +1. +1. +1. +1. +1. +1. +1. +1. +1. +1. +1. +1. +1" +qasper,348886b4762db063711ef8b7a10952375fbdcb57," + + + + + +1." +qasper,0ab3df10f0b7203e859e9b62ffa7d6d79ffbbe50," + + + + +Introduction +Language identification is the first step in natural language processing and machine learning and machine comprehension is an important in text is known as downstream models can be applied. + + + + + + + + +Models can be applied to many parts of speech taggers and language can be required" +qasper,3e1829e96c968cbd8ad8e9ce850e3a92a76b26e4," + + + + + +Social media platforms have made it easier to spread fake news easier and wider as well as able to reach a wider audience. Social media offer anonymity the door to suspicious individuals. Social platforms offer the spreading the intention to utilization of rumors and this opens the door to" +qasper,2ceced87af4c8fdebf2dc959aa700a5c95bd518f," + + + + + +Introduction + + +Learner corpora have compiled for English as International + + + + + + +Introduction + + +The importance of + + + + +Reference + + +have + +been + + +been + + +been +re + + +" +qasper,cb6a8c642575d3577d1840ca2f4cd2cc2c3397c5," + + + + + +1." +qasper,8b0abc1907c2bf3e0256f8cf85e0ba66a839bd92," + + + + + +\end of text." +qasper,78292bc57ee68fdb93ed45430d80acca25a9e916," + + + + + +Introduction + +Language models are trained language models have been used to extend the evaluation of knowledge in the evaluation of models to negation. + + + + + + + + +Trained language models have been used to evaluation of models to evaluation +Trained models +Trained models +" +qasper,e431661f17347607c3d3d9764928385a8f3d9650," + + + + + +Introduction + + +Automatic classification has mainly focused on categorizing tweets in the problem of sentiment analysis has been a problem of binary classification, refined to three (ternary analysis. In this work, we fine-grained three categories. In this study, we classified twe" +qasper,545e92833b0ad4ba32eac5997edecf97a366a244," + + + + + +\end of + + + +\begin{" +qasper,67e9e147b2cab5ba43572ce8a17fc863690172f0," + + + + + +\end + +\end{document" +qasper,111afb77cfbf4c98e0458606378fa63a0e965e36," + + + + + + +1." +qasper,12f7fac818f0006cf33269c9eafd41bbb8979a48," + + + + + +1." +qasper,9cf070d6671ee4a6353f79a165aa648309e01295," + + + + + +The parallel corpora." +qasper,76121e359dfe3f16c2a352bd35f28005f2a40da3," + + + + +1. What is the paper considers a wealth of natural language processing? +2. +2. + + + + + +3. +3. +4 +. +5 +. +4 +. +5 +6 +. +6 +7 +. +8 +" +qasper,f161e6d5aecf8fae3a26374dcb3e4e1b40530c95," + + + + +Introduction of Proceedings of International Workshop on Health Informatics of Intelligence in Medicine (W3rd AI) and AI+3rd Artificial Intelligence (2 Physician's Conference + + + + + + + + +To appear in the text is" +qasper,1d9aeeaa6efa1367c22be0718f5a5635a73844bd," + + + + + +1. Traditional machine learning models do not capture the context of human behavior as well +2 +2. +3. emotional expressions +4. + + + + + + +There have been many advances in understanding +I + + + + +1. + +IB" +qasper,c5abe97625b9e1c8de8208e15d59c704a597b88c," + + + + + +1. + + +1. + + +1. + + +1. + +1. +1. +1. +1. +1. +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +" +qasper,4c07c33dfaf4f3e6db55e377da6fa69825d0ba15," + + + + +Introduction studies on stock market are based on historical prices are based on historical prices. Later studies have used the efficient market movements. Later studies have debunked the efficient market hypothesis of predicting market movements. Finan efficient market hypothesis (EM) financial market hypothesis states that financial movements depend on current" +qasper,584af673429c7f8621c6bf83362a37048daa0e5d," + + + + + +Over the past few years, generating text from images has gained a lot of attention in Computer Vision and Language related tasks have been proposed in the field of Computer Vision communities have been proposed, such as image and visual question answering and visual question answering, image and visual question answering, and" +qasper,ba28ce9a2f7e8524243adf288cc3f11055e667bb," + + + + +In the opportunity + + +In the + +6 operators + +6 operators + +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators +6 operators" +qasper,36ae003c7cb2a1bbfa90b89c671bc286bd3b3dfd," + + + + + +\end + +\end{figure}" +qasper,e025061e199b121f2ac8f3d9637d9bf987d65cd5," + + + + +Performance appraisal is important for organizations that process is particularly crucial for organizations that depend on skills and expertise and workforce. The process enables organizations to evaluate employees periodically measure every employee's performance. It provides a mechanism to evaluate their activities. Performance appraisal is an" +qasper,fb2b536dc8e442dffab408db992b971e86548158,"Recently, with the emergence of neural seqment of neural models, abstractive models, abstractive methods have seen great performance in reflections have improved BIB0, complex models. Recently, complex models have been trained in reflections have been tested in the reflections. However, and summarization models usually" +qasper,28067da818e3f61f8b5152c0d42a531bf0f987d4," + + + + + +1." +qasper,26126068d72408555bcb52977cd669faf660bdf7," + + + + + +What are the qualitative experiments performed on benchmark datasets? + + + + + + +Introduction of the qualitative experiments + + + + + +Introduction + + +Introduction + + +Introduction + + +Introduction + + +Introduction + + +Introduction + +Introduction + +Introduction +" +qasper,1dc2da5078a7e5ea82ccd1c90d81999a922bc9bf," + + + + + +1. The authors conduct experiments are mentioned in the tasks mentioned in the tasks +2. +2. The authors mention the tasks +3. +3. The authors mention the tasks +4. +4. The authors mention the tasks +5. +6 The authors mention +7" +qasper,14b74ad5a6f5b0506511c9b454e9c464371ef8c4," + + + + + +The paper explores the problem of machine translation (NMT) emerged in recent years as a very successful paradigm BIBREF , BIBREF1 , BIBREF2 , BIBREF3 . While NMT is generally fluent NTIBREF3 . N" +qasper,deb89bca0925657e0f91ab5daca78b9e548de2bd," + + + + + +1. Decoding speech or speech signals +2. +3. coding motor activity in brain +4. +5. BCIBIBREF1 +5 +. +. + +6. BIB +7. +8. +1. +9. +1." +qasper,e5c8e9e54e77960c8c26e8e238168a603fcdfcc6," + + + + + +Introduction + +Accurate language identification is first in natural language processing and machine learning and machine comprehension is important in text is known as downstream models can be applied. If the appropriate models can be used. LID models can be applied. LID also harvested data to" +qasper,8bb0011ad1d63996d5650770f3be18abdd9f7fc6," + + + + + +1. What is the passage? + + + + + +The passage + +What +What +is +the +passage +What +is +the +passage +What +passage +What +passage +What +passage +What +passage +What" +qasper,ce2b921e4442a21555d65d8ce4ef7e3bde931dfc," + + + + + +\end + +\end{document}" +qasper,9c33b340aefbc1f15b6eb6fb3e23ee615ce5b570," + + + + + +Decoding speech signals from brain activity is recorded in brain signals is computed using brain-computer interface systems for interpreting BIB , BIBREF1 , BIBREF1 , BIB" +qasper,12d7055baf5bffb6e9e95e977c000ef2e77a4362," + + + + + +\end" +qasper,50cb50657572e315fd452a89f3e0be465094b66f," + + + + + +1." +qasper,f0b2289cb887740f9255909018f400f028b1ef26," + + + + +In the study of social media has been used to classify the types of online harassment in the types of harassment in the most common in Twitter, including racism, sexism, there are many forms of hate groups that expressing themselves. Twitter has become a platform for mis" +qasper,e587559f5ab6e42f7d981372ee34aebdc92b646e," + + + + +1." +qasper,68df324e5fa697baed25c761d0be4c528f7f5cf7," + + + + + +Macaww + + + +What is Macaw is a modular + + + + + +What is + +What is +What is +What is +What is +What is +What is +What is +What is +What is +What is +What is +" +qasper,6a31db1aca57a818f36bba9002561724655372a7," + + + + + +This paper is a" +qasper,66c96c297c2cffdf5013bab5e95b59101cb38655," + + + + + +During the sharing of data + + + +the +two +decade +of +data +the +data +has +beenough +the +data +the +data +the +data +data +the +data +the +data +the +data +the" +qasper,b3307d5b68c57a074c483636affee41054be06d1," + + + + + +1." +qasper,3fff37b9f68697d080dbd9d9008a63907137644e," + + + + + +In this study, the results of the state-of-the-art models are compared to the-the-art models are compared in terms of-the-the-art models are compared in terms of accuracy, precision, recall, f1 and f1-score, and classification" +qasper,1269c5d8f61e821ee0029080c5ba2500421d5fa6," + + + + + +1. Machine translation (MT refers to machine translation (NMT) is widely applied in recent years on popular for language pairs such as English <->English, INFORM1 and INFORM1 English <->INLINE Chinese <->INFORM2 English. +2 +2. +" +qasper,4e1a67f8dc68b55a5ce18e6cd385ae9ab90d891f,"From a group of users at time in0, we have evolved in0, Quora has grown in few into one of its last two of the largest in0 years into the community with diverse Q&A sites with efficient moderation and active-driven communities. Quora has evolved into one of the" +qasper,869feb7f47606105005efdb6bea1c549824baea0," + + + + + +1." +qasper,fff5c24dca92bc7d5435a2600e6764f039551787," + + + + + +1." +qasper,c49ee6ac4dc812ff84d255886fd5aff794f53c39," + + + + + +The paper is a" +qasper,149da739b1c19a157880d9d4827f0b692006aa2c," + + + + +1. Which classifiers are evaluated? + + + + + +Introduction-oriented systems have been used for people using natural language processing. Moreover, such as Google's Dialogflow and Amazon's Dialogflow and IBM's Watson are providing tools for developing platforms and tools such as" +qasper,2236386729105f5cf42f73cc055ce3acdea2d452," + + + + +Introduction of text is a large number of the knowledge is recorded in documents. That ability to automatically inferred from text. A system for information from text. A system has been proposed as a challenge to evaluate a given a way to infer a document is a task has been a way to read a" +qasper,37a79be0148e1751ffb2daabe4c8ec6680036106," + + + + + +1. What is the Facebook data set is used in the topic? +2. +2. What is the dataset? +3. What is the dataset? +4. What is the dataset? +What is the topic? +What is the dataset? +What is the dataset" +qasper,55c8f7acbfd4f5cde634aaecd775b3bb32e9ffa3," + + + + +1. + + +1. What is the main idea +2. The approach +2. The approach +3. The approach +4. The approach +5. The approach +6. The approach +7. The approach +8 The approach +9. The approach +." +qasper,8d4ac4afbf5b14f412171729ceb5e822afcfa3f4," + + + + + + +This text is a model is a model to detect demographic model to detect demographic and psychological dimensions." +qasper,051df74dc643498e95d16e58851701628fdfd43e," + + + + + +We obtained the dataset from the dataset from the website and preprocessing the online support groups, the information from the information from the website and extracted it. + +We downloaded the data from the website and processed it using a dataset from the website. +We obtained the data from the website and" +qasper,55569d0a4586d20c01268a80a7e31a17a18198e2," + + + + + +\end + +\end{document} + + + +\end{document +} + + + + +\end{1. +\end{document} +} +} + + + + +\end{ +} +} +} +} +} +" +qasper,a1645d0ba50e4c29f0feb806521093e7b1459081," + + + + +The dataset is a dataset is a collection of tweets from the Twitter, which is a microblogging platform with millions of active users and contains a large number of posts from millions of users. Unfortunately, and spammers also use it to post unsolicited messages. Despite the platform" +qasper,e5c8e9e54e77960c8c26e8e238168a603fcdfcc6," + + + + + +Introduction + +Accurate language identification is first in natural language processing and machine learning and machine comprehension is important in text is known as downstream models can be applied. If the appropriate models can be used. LID models can be applied. LID also harvested data to" +qasper,a5e49cdb91d9fd0ca625cc1ede236d3d4672403c," + + + + + +each machine reading comprehending a given passage and answering a passage and answering a challenging task is one long-term task is important in natural language understanding and intelligence, building a challenging task. It is important for customer service. In order to judge whether given the available in order to compreh" +qasper,219af68afeaecabdfd279f439f10ba7c231736e4," + + + + + +Japanese is a language with a language that uses a variety of characters, and subword segmentation techniques to translate it. In Vietnamese, subword and morphological information is a subword in-rich in-vocative and subword. It is a technique to learn" +qasper,e3a2d8886f03e78ed5e138df870f48635875727e," + + + + + +1." +qasper,a4a1fcef760b133e9aa876ac28145ad98a609927," + + + + + +1." +qasper,df5a4505edccc0ee11349ed6e7958cf6b84c9ed4," + + + + + +1." +qasper,1dc2da5078a7e5ea82ccd1c90d81999a922bc9bf," + + + + + +1. The authors conduct experiments are mentioned in the tasks mentioned in the tasks +2. +2. The authors mention the tasks +3. +3. The authors mention the tasks +4. +4. The authors mention the tasks +5. +6 The authors mention +7" +qasper,a87a009c242d57c51fc94fe312af5e02070f898b," + + + + + +What predictive model do + + + + +I supposed to + + + +the + + model + +supposed + +to + + +What + + +suppose + + +to + +What + +suppose + +to + +What +suppose +" +qasper,da55bd769721b878dd17f07f124a37a0a165db02," + + + + + +1. What is the system handles the system handles +2. + + + + +1. What is the system +1. What +1. What +1. What +1. What +1. What +1. What +1. What +1. What +1" +qasper,1591068b747c94f45b948e12edafe74b5e721047," + + + + +1." +qasper,adbf33c6144b2f5c40d0c6a328a92687a476f371," + + + + + +1." +qasper,197b276d0610ebfacd57ab46b0b29f3033c96a40," + + + + +Performance appraisal is an important for organizations particularly dependent on skills and expertise and it depends on the workforce. It enables organizations to evaluate their performance. Performance appraisal is an important for organizations to measure every employee's activities. Performance appraisal is important for organizations to" +qasper,15cdd9ea4bae8891c1652da2ed34c87bbbd0edb8,"The texts in the paper come from Twitter, specifically from Twitter, specifically tweets scraped from Twitter, and the data was collected from two years ago8 and tweets were extracted. The data was collected from Twitter, which were either in English or Hindi and Hindi. + + + + + + +" +qasper,3cf1edfa6d53a236cf4258afd87c87c0a477e243," + + + + + +1." +qasper,dca86fbe1d57b44986055b282a03c15ef7882e51," + + + + +1.1. What is fake news is fake news? +2. +2. How does it spread? +3 How does it spread? +3. How does it affect? +4 What is it affect? +4. +5. How does it affect? +6." +qasper,d3dbb5c22ef204d85707d2d24284cc77fa816b6c," + + + + + +1. BERT, BERT, BERT, BERT, BRC, BRC, BIBREF, BIBREF, BIBREF, BIBREF, BIBREF, BIBREF, BIBREF, BIB5, BIB5, BIB" +qasper,e35c2fa99d5c84d8cb5d83fca2b434dcd83f3851," + + + + +Social media platforms have made the spreading of fake news easier as well as able to reach a wider audience. Social media platforms, which offer anonymity the door to suspicious individuals to utilize the spreading the intention to organizations. Recently has increased the number of fake news and" +qasper,9132d56e26844dc13b3355448d0f14b95bd2178a," + + + + + +1." +qasper,d9980676a83295dda37c20cfd5d58e574d0a4859," + + + + +1." +qasper,2c7494d47b2a69f182e83455fe4c75ae3b2893e9," + + + + +Introduction + + +Twitter is widely used microblogging platform, users interact with messages, “we post on Twitter, understanding the representation of tweets can be challenging a plethora of applications. Understanding the sentiment analysis a problem of tweets are difficult. Understanding the representation" +qasper,2439b6b92d73f660fe6af8d24b7bbecf2b3a3d72," + + + + + +This work is licensed under a Creative Commons Attribution 4 International License 4.0 International License. + + + + + + + + +This is a" +qasper,b6ae8e10c6a0d34c834f18f66ab730b670fb528c," + + + + + +1. + +1. + + +1. + +1. + +1. +1. +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1" +qasper,bf52c01bf82612d0c7bbf2e6a5bb2570c322936f," + + + + + +Automatic text summarization has been active research in natural language processing has been an active area for several decades. To evaluate the performance of different variants of ROUGE scores. To compare the performance of different systems, various approaches have been proposed. To evaluate the quality of summarization," +qasper,f1e90a553a4185a4b0299bd179f4f156df798bce," + + + + + +1." +qasper,ef7212075e80bf35b7889dc8dd52fcbae0d1400a," + + + + + +1. + + +1. + + + +1. + + + +1. + + + +1. + + +1. +1. +1. +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1" +qasper,32a3c248b928d4066ce00bbb0053534ee62596e7," + + + + +The paper describes the context of the inflectional morphological information of a word is the word in a word is the process of computational modeling of the context in the field of linguistics is called computational linguistics. It is called morphology. The inflectional prediction of words is the" +qasper,49764eee7fb523a6a28375cc699f5e0220b81766," + + + + + +\end + +\end{ + + + +\section +\lipsum +\end{ +\label{ref} + + + + + +\end{ +\end{figure}" +qasper,2cd37743bcc7ea3bd405ce6d91e79e5339d7642e," + + + + + +1." +qasper,127d5ddfabec5c58832e5865cbd8ed0978c25a13," + + + + +We understand from natural language modeling from any language corpus a majority of vocabulary types will occur in low frequency. This is difficult to estimate the rare types of words. This is analogous to statistical task is similar to the dimensionality of tokens. This is nonlinearly the" +qasper,aa6d956c2860f58fc9baea74c353c9d985b05605," + + + + +Performance appraisal is important for organizations particularly dependent on the process, it is important for organizations that evaluate their employees. It enables organizations to measure their performance periodically and track the progress. Performance appraisal is a mechanism to measure the day-by-by-day activities. It" +qasper,968b7c3553a668ba88da105eff067d57f393c63f," + + + + +1. Characterizing fake news is a phenomenon of tweets in the meta-data +2 +2. + + +3.1.1 + + + + + +1. + +1. + +1. +1 +1 +1 +1 +1 +1" +qasper,5e324846a99a5573cd2e843d1657e87f4eb22fa6," + + + + + +Introduction the sense disambiguation is a word's disambiguation is a problem that occurs when the focus of several words. Two common in the word, usually refer to the word, but the context of the problem is substantially different. Two types of disambiguation is a group of the word" +qasper,1beb4a590fa6127a138f4ed1dd13d5d51cc96809," + + + + + +1." +qasper,fa527becb8e2551f4fd2ae840dbd4a68971349e0," + + + + + +The encoder is a sequence of the context of the input is a word is a process of the lemma and the context of the baseline. +The encoder generates a word and track of the word in the baseline. +The context of the word is the word forms of the" +qasper,55139fcfe04ce90aad407e2e5a0067a45f31e07e," + + + + + +1." +qasper,25c1c4a91f5dedd4e06d14121af3b5921db125e9," + + + + + +1. + + +1. Yes, the car-speak language is trained on data +2 +2. +3. +No + + + +. +. +. +. +. +. +. +. +. +. +. +. +." +qasper,4e748cb2b5e74d905d9b24b53be6cfdf326e8054," + + + + + +1. Sentiment analysis is an indirect and complex involving speech, involving speech, involving tone and gestures along with linguistic features in speech, body language, making sarcasm often used in text, and speech, and speech in text, making it is restrictive when it comes to" +qasper,a8f51b4e334a917702422782329d97304a2fe139," + + + + + +1. + +1. + + +1. + + +0 + +1. + + +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +" +qasper,a130306c6662ff489df13fb3f8faa7cba8c52a21," + + + + + +1." +qasper,dc1cec824507fc85ac1ba87882fe1e422ff6cffb," + + + + +1. Speech corpora) The datasets used in the datasets used in the research were a variety of datasets used in the research were a variety of datasets from the research were collected from the internet and other sources such as IBM, including the internet and books. +2. +2. +" +qasper,2c85865a65acd429508f50b5e4db9674813d67f2,"Spoken conversations remain the most valuable information is conveyed and exchanged in such an unstructured form. Intelehealth. Thus, a lot of information is exchanged in tele-health settings. Therefore, nurses callers have returned to monitor their health to extract health. Human language that" +qasper,307e8ab37b67202fe22aedd9a98d9d06aaa169c5," + + + + + +Introduction + +Language identification is first step in natural language processing is important in machine learning pipelines is known as downstream models of text is a piece of speech and models can be applied. +Langulation of parts of speech taggers and language models can be required. +if the" +qasper,111afb77cfbf4c98e0458606378fa63a0e965e36," + + + + + + +1." +qasper,82642d3111287abf736b781043d49536fe48c350," + + + + +In this study, we used the dataset is a publicly available on the Stanford natural language processing Twitter corpus of English text-based dataset, which contains tweets from the United States, collected from the period of August 201, 2017 to August to August" +qasper,0d34c0812f1e69ea33f76ca8c24c23b0415ebc8d,"Introduction has been used in natural language processing recently in the most widely in the NLP is used to identify and classify opinions in sentiment analysis. It is conducted for sentiment analysis. Although sentiment analysis has been widely studied for topic classification in many languages. Although sentiment analysis has been conducted for customer reviews in social media. Although" +qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929," + + + + + +1. What criteria are used to select the 60 + + + + + +1. left +*margin*" +qasper,63a1cbe66fd58ff0ead895a8bac1198c38c008aa," + + + + + +1. What is the image generation models are evaluated? +2. +2. How well-performing +3. +3. What are the +4. evaluation +5. evaluation +5. What +6. evaluation +7. What +8. metrics +9." +qasper,bb3267c3f0a12d8014d51105de5d81686afe5f1b," + + + + + +Which datasets do they use + + + + +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets +which datasets" +qasper,5fda8539a97828e188ba26aad5cda1b9dd642bc8," + + + + + +1. + + +In the proposed model is better than previous models in terms of segmentation, our model performs well-trained models in external datasets. +2. + + + + + + + +In terms of external resources are used for Chinese + + + + + +" +qasper,f225a9f923e4cdd836dd8fe097848da06ec3e0cc," + + + + + +1." +qasper,cc354c952b5aaed2d4d1e932175e008ff2d801dd," + + + + + +1." +qasper,2c7e94a65f5f532aa31d3e538dcab0468a43b264," + + + + +The dataset was annotated by crowdsourced by collecting workers and tasks and commands to domains, each intent-related data and dialog-related tasks, including intents. + + + + + + + + +Each intent-scope data is grouped by tasks and dialog-related tasks" +qasper,de3b1145cb4111ea2d4e113f816b537d052d9814," + + + + +There have been many advances in machine learning methods which help machines understand human behavior better than the emotion detection of emotions. One of the most important aspect of human behavior. If could be used to improve. + + + + + + + + + +There have been many models which" +qasper,beac555c4aea76c88f19db7cc901fa638765c250," + + + + + +1." +qasper,ee417fea65f9b1029455797671da0840c8c1abbe," + + + + + +1. What is the system is the system? +What is the system? +What is the system? +What is the system? +What is the system? +What is the system? +What is the system? +What is the system? +What is the system?" +qasper,d53299fac8c94bd0179968eb868506124af407d1," + + + + + +1. What is the performance of classifiers? + + + + + + +Introduction +A portion of the car-buying in the United States involves a dealership BIB, dealerships at dealerships BIB, dealerships BREF1, dealerships" +qasper,ffa7f91d6406da11ddf415ef094aaf28f3c3872d," + + + + + + +1." +qasper,277a7e916e65dfefd44d2d05774f95257ac946ae," + + + + + +1. The paper introduces the processing of medical texts and annotated data +2 is important in the digital area. +2 relevant to natural language processing (NLP) is accurate to medical reports. +3 annotated data is needed for NLP. +4 dedicated to medical reports" +qasper,f7ed3b9ed469ed34f46acde86b8a066c52ecf430," + + + + +1. + +1. Skip-grams +2 +2. +3 +4 +5 +6 +7 +8 +9 +1 +10 +1 +6 +1 +0 +1 +1 +0 +1 +1 +1 +1 +1" +qasper,9c44df7503720709eac933a15569e5761b378046," + + + + +1. + + +1. + + +1. + + + +1. + + + +1. + + +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +" +qasper,08b57deb237f15061e4029b6718f1393fa26acce," + + + + +1. What is the crowdworkers are individuals who are experts in the crowdworkers? + + + + + + +2. What is the role of crowdworkers are experts in the task +3. +3. What is the task +4. What is the" +qasper,d3dbb5c22ef204d85707d2d24284cc77fa816b6c," + + + + + +1. BERT, BERT, BERT, BERT, BRC, BRC, BIBREF, BIBREF, BIBREF, BIBREF, BIBREF, BIBREF, BIBREF, BIB5, BIB5, BIB" +qasper,d9b6c61fc6d29ad399d27b931b6cb7b1117b314a," + + + + + +1." +qasper,52f8a3e3cd5d42126b5307adc740b71510a6bdf5," + + + + +Introduction: +large the modeling a majority of human is recorded through text is why a system that can be used to automatically infer from text without data. AI" +qasper,f9f59c171531c452bd2767dc332dc74cadee5120," + + + + +Decoding speech signals from brain activity in brain signals is a research area of brain-related to Interface (BCIBIB , BIBREF1 , BIBREF1 , BIBREF1 , BIBREF2 , BIB3 , BIB4 , BIB5 , BIB" +qasper,4625cfba3083346a96e573af5464bc26c34ec943," + + + + +1." +qasper,ddf5e1f600b9ce2e8f63213982ef4209bab01fd8," + + + + + +1." +qasper,71ba1b09bb03f5977d790d91702481cc406b3767," + + + + + +1. + + +1. + + +1. + + +1 + +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +" +qasper,81d193672090295e687bc4f4ac1b7a9c76ea35df,"Introduction has recently, sentiment analysis has been one of the hottest topics in natural language processingNLP has been widely used in NLP. It is used to identify and categorize and classify opinions in social media. It can be leveraged in customer reviews. Although many studies have been conducted for sentiment analysis," +qasper,22744c3bc68f120669fc69490f8e539b09e34b94,"It is known that language models can be learned in a general-purpose. +It is known that language models can be used to learn to capture structure in a general-purpose. +It is known that models can be learned in general. +It is known that models can be learned in general. +It" +qasper,7705dd04acedaefee30d8b2c9978537afb2040dc," + + + + + +\end of + + + +\begin``` +``` +``` + + + +``` + + +``` + + +``` + + + +``` + + + +``` + + +``` + + +``` + + +``` + + +``` + +``` + +" +qasper,9ca447c8959a693a3f7bdd0a2c516f4b86f95718," + + + + +Introduction + + +Stance detection is a recent study on sentiment analysis is a problem in natural language processing and text classification is a well-based on the classification of the text. It is a problem in the classification of the text. It is usually performed on social media. It is considered a" +qasper,62ea141d0fb342dfb97c69b49d1c978665b93b3c," + + + + + +1. + + +1. + + +1. + + + +1. + + +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1" +qasper,e025061e199b121f2ac8f3d9637d9bf987d65cd5," + + + + +Performance appraisal is important for organizations that process is particularly crucial for organizations that depend on skills and expertise and workforce. The process enables organizations to evaluate employees periodically measure every employee's performance. It provides a mechanism to evaluate their activities. Performance appraisal is an" +qasper,a712718e6596ba946f29a99838d82f95b9ebb1ce," + + + + + +1." +qasper,01e2d10178347d177519f792f86f25575106ddc7," + + + + + +1." +qasper,891c2001d6baaaf0da4e65b647402acac621a7d2," + + + + + +1. In this paper, we investigate the static embedding for each word in the context of the context of the word "dog" in the given sentence "A dog" and "dog" and "dog" in the word "is trying to get" in the sentence. +2." +qasper,d28d86524292506d4b24ae2d486725a6d57a3db3," + + + + + +1." +qasper,ad08b215dca538930ef1f50b4e49cd25527028ad," + + + + + +\end" +qasper,05887a8466e0a2f0df4d6a5ffc5815acd7d9066a," + + + + + +Introduction + + +Stance detection or classification or stance identification is recent research in natural language processing is a recent topic in the subtask of text classification is a well-defined as a problem in the classification of text. It is usually performed on social media. It is usually defined as" +qasper,5bc1dc6ebcb88fd0310b21d2a74939e35a4c1a11," + + + + + +Most NLI + + + +1. + +1. + + +1. + +1. +1. +1. +1. +1. +1. +1. +1. +1. +1. +1. +1. +1" +qasper,6bf5620f295b5243230bc97b340fae6e92304595," + + + + + +The baseline is used in the monoling model is a multilingual model is a joint model that incorporates multiple languages, which is used in supervised and unsupervised and unsupervised SIB1 and supervised SIB1, RLING task." +qasper,5cb610d3d5d7d447b4cd5736d6a7d8262140af58,"Multilingual training is aims to capture the context by generating a word forms in a word in a given language, the context of the form of the word, which is a process of the word. + + + + + + + + + +Introduction: + + +1." +qasper,133eb4aa4394758be5f41744c60c99901b2bc01c," + + + + +Introduction +In this digital era, online interactions have become a vital part of social media platforms like twitter, Instagram, etc. Real elements of social media platforms like Facebook, etc. Similar to cyber bullying, harassment, etc. In the nature of the internet, etc." +qasper,113d791df6fcfc9cecfb7b1bebaf32cc2e4402ab," + + + + +1." diff --git a/evaluation/zeroscrolls/predictions/vicuna-7b-v1.3/vicuna-7b-v1.3_narrative_qa_15000.raw b/evaluation/zeroscrolls/predictions/vicuna-7b-v1.3/vicuna-7b-v1.3_narrative_qa_15000.raw new file mode 100644 index 0000000..6331a40 --- /dev/null +++ b/evaluation/zeroscrolls/predictions/vicuna-7b-v1.3/vicuna-7b-v1.3_narrative_qa_15000.raw @@ -0,0 +1,3545 @@ +Task,ID,Prediction +narrative_qa,d285c6beb3dc935411fd3297b02602b8afc7170c_14,""I's +"I'm not +" +"I'm +" +"I +"I +" +"I +" +"I +" +"I +" +"I +" +" +"I +" +" +" +" +" +"" +narrative_qa,c5165082a52b8427b974f0ed3152862f0d51ffb7_19," < < < < + < < < +> < < < < < < < < < < < < < < " +narrative_qa,8ec2acc82d024a645fe81f51270d6dbba8b7981e_15," + + + + +Translated by Jow + + + + +PERSON OF +DIC + +DUE +IT + + +RO + +Socrates" +narrative_qa,6cfe7052daf187b667538b2858849a84c1f6b049_10," + + + + +"I'sorry, she's not + + + + + + +"I'm + + + +"I + +" + +" + +" +" +" +" +" +" +" +" +" +" +" +" +" +narrative_qa,94dc6d01df88f97843813efb11e5d7e48562c869_15,""I am, the man, the boat, the boat, and the +" ether, and the +" ether, and the +" ether, and the +" +" ether, and the +" ether +" ether +" ether +" ether +" " +narrative_qa,336e272847df32ee6f87eca63ee800c5add03639_17," 1. + I'm +> 1. +b> The +> 1. +> 1. +> +> +> +> +> +> +> +> +> +>" +narrative_qa,d802901218c1f792295cb1565a2a922b4ff9294a_6,"[1. +[1: 1. 1. 1. 2. 2. 2. 3. 3. 4. 4. 4. 5. 6. 7. 8. 9. 10. 10. " +narrative_qa,f90f97c9ae38be55921b5e8a93d06ddada82dd1f_13,""I am not, he said, "I'm not to be +"--the Church, and the Church +"--the Church +"--the Church +"--the Church +"--the Church +"--the Church +"--the Church +"--the Church +"--the Church" +narrative_qa,4f485054f9d450534fddba184f0996e32575d1be_10,"b> I I < + < +b> b> < +> < +> b> < < +> < b> +> < < < b" +narrative_qa,4b30ab1c49b62dc59b9773954958d9ac6807a865_0," + + + + + + + + +< +1 +> + + + +" +narrative_qa,edb92e9d9fe05242da8ead459d53853c8815716b_11," + + + +MYRand + + + +THE + + +THE + + +THE + + +THE + + +THE + + +THE + +THE + +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE" +narrative_qa,56434e4849e679e4089c33a5f5bd3050e71c5ec1_4," + + +The story is a young woman, the +The story is about a young man, +Claude +The story of the +The story is a young man +The story is a young man +The story of the young man +The story is a young man +The story of the young man +" +narrative_qa,926198f64d64f2bf796feddd42a4aa1714ddf5b2_20,"Siddha +Siddha's friend, Vasude +Siddha, Vasude +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha" +narrative_qa,f75db432fbc79913fb56349cfdb7ec5446e9941e_15,"p> +

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+ + + + + + +

+ + +

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wife +The doctor's husband +The doctor's wife +The doctor's husband +The doctor's" +narrative_qa,c63c5b96aac5c5c1d059d839580344f80e456bd4_26,"< I b> I + I +< < b> I +< b> b> b> b> +< b> b> b> b> +< b>" +narrative_qa,9d8ddfe86beba149b3463eeb1bae92919e179fed_26," I's the + +> 1 +> 1 +> 2 +> 2 +> 3 +> 4 +> +> +> +> +> +> +> +> +>" +narrative_qa,57523a482ae81616899feaff7497fa962edef9b6_5,""I am, my dearest, my dearest, I am +" +"I have a good, and you are +" +"I have a good +" +"I am not, my dearest, my dearest +" +"I am +"I am +"I am +" +narrative_qa,a32f354788dd1f1411b9745200cb330d9b556373_19," the + the +< +< +> < +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +" +narrative_qa,6a02d46e87865ba5b033c56c658af2bfdd182093_13," A I +< < < < < < < < < < < < < < < < < < < < < < < < " +narrative_qa,e0c74cdf270ebe29a2139e7319fc7314738c88ee_30," 1 + + +ON + + + + + + + + + + + +" +narrative_qa,ea371ba9cc41bf32aa8b37549662d2d3a38f084d_7,"I'm not know what's +I'm not sure, I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not" +narrative_qa,15618d16f20e7ba33352f06e210f42ef59d84d74_10," 1 + 1 + +> 1 +> 1 + +> 1 + +> 1 +> +> 1 +> 1 +> 1 +" +narrative_qa,c5165082a52b8427b974f0ed3152862f0d51ffb7_20," < + < < + < < + < < < < < < < < < < < < < " +narrative_qa,f70edecebfabbfaa65fb58ada2a6642453d217a9_24,"I's +I'm afraid, the +"s +"I'm afraid +" +"I'm afraid +" +"I'm +" +"I'm +" +"I +" +"I +" +"I +" +"I +" +" +narrative_qa,f94253b4b5f8cfb2afea5f0b9d6bb87de266d11c_23,"The peas the superint, the superintendent, the +The superintendent, the superintendent, the superintendent, the superintendent, the superintendent, the superintendent +The superintendent, the superintendent +The superintendent, the superintendent," +narrative_qa,ea371ba9cc41bf32aa8b37549662d2d3a38f084d_1,"The girl, she's +"I'm not sure, I'm not sure +" +"I'm not +" +"I'm not +" +"I'm not +" +"I'm not +" +"I'm +" +"I'" 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+ + + +Theodor, 19-8- + + + +Theodor + + +Theodor + +Theodor +Theodor Herzl +Theodor +Theodor Herzl +Theodor Herzl +Theodor" +narrative_qa,b7ea3185c6be5ea8d603fe5c4c54268c8b42605a_4," + + + + +The first, the first, the + + + + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,01502137e4276712d118bd4bdaf481c89aed163b_11," 2 8/3 + 8RING 10N + 1T + + + + + + + +" +narrative_qa,b7ea3185c6be5ea8d603fe5c4c54268c8b42605a_12," + + + + +Thee'sighsighing to the court + + + + + +The court + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,3513ebaa62106c8e80a0c9723eb456f28950adb8_0," + + + + +"I'sir + + + +"I + +"I + +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +" +narrative_qa,0ae0524cd381087ff94afe5d3fcd807f1708e488_8,"The Exhibition +The Exhibition +The Louvre +The Louvre +The Salon +The Salon +The Salon +The Louvre +The Louvre 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What is the +2. What is the +2. Who is +3. Whose +4. Whose +5. Whose +6. Whose +7. Whom +8. Whom +9. Whom +10. Who’" +narrative_qa,edb92e9d9fe05242da8ead459d53853c8815716b_8," + + + + +THE Archde + + + +THE + + +THE + + +THE + + +THE + + +THE + + +THE + +THE + +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +" +narrative_qa,afa2f47d178398d3f3c9515662fc5bbd71b15ccd_13," + + + + + +The +The + + +The + + +The + + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,51f816f54645196b0a8f588f9ae955de103565f7_2," 1. + 1 + + + + + + + + + + + + + N + +N + I'm +< +> + +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +>" +narrative_qa,845186ccb0a481e73525e98cbef89279c2f5721e_26,""I's +"I'm not a moment of the +" +"I'm not +" +"I'm not +" +"I'm not +" +"I'm +" +"I +"'m +"I +"'m +" +" +narrative_qa,0ae0524cd381087ff94afe5d3fcd807f1708e488_17,"The Exhibition of Art +The Salon +The Salon +The Salon +The Louvre +The Louvre +The Salon +The Louvre +The Louvre +The Louvre +The Louvre +The Louvre +The Louvre +The Louvre +The Louvre +The" +narrative_qa,408d3b92b889218d73adddf502b5f7f9d002f4ab_8,"{ +{ 1} +{ 1. +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +{ 1 +" +narrative_qa,9d8ddfe86beba149b3463eeb1bae92919e179fed_24," I'm + 1 +> I'm not + +> 1 +> 1 +> 2 +> 2 +> 3 +> 4 +> +> +> +> +>" +narrative_qa,de42ec88ce00d0f1e21ad4ad719d1d64499e5166_16," + + + + + + + + + + + + + + + +" +narrative_qa,ebfad83ccf1214df566432798c54deb17e91d958_11,"The doctor, the +The doctor's wife, the +The doctor, the +The doctor's wife +The doctor's wife +The doctor's wife +The doctor's wife +The doctor's husband +The doctor's wife +The doctor's husband +The doctor's" +narrative_qa,3c5a131da6f43e2f92dce91aa238e6782e428748_23," I'm not + + + + + + + + + + + + + + 1. + + + + + + + + + + + + + + +" +narrative_qa,69add8ffcf964d23243ac02b7282d57beb63c355_12,"RET +Ellips, the ground + + + + + + + + + + + + +" +narrative_qa,199ec80c97f752cd8e00d2ca6f8e5f48603fefb8_29," ST I'm +< +> 1 + I'm not +< +b> +> 1 + 1 +> 2 +> 2 +> 3 +> +> +> " +narrative_qa,d285c6beb3dc935411fd3297b02602b8afc7170c_22,""I's +"I'm not, +" +"I'm +" +"I +"I +" +"I +"I +" +"I +" +"I +" +"I +" +"I +" +" +"I +" +" +narrative_qa,c49589b75550f66772acbdb86b26b1fd38a3249e_4,"SI: I'm +Less +Tell, I'm +The +SIST: I'm +SIR +SIR. +SIR. +SIR. +SIR. +SI. +SI. +SI. +SI. +SI. +SI. +SI." +narrative_qa,441f692c3e288cd8b6411e792e629ae8b362c649_16,"< b> I +> I + I +b> < I +b> < b> +b> b> +> b> b> +" +narrative_qa,6a02d46e87865ba5b033c56c658af2bfdd182093_26," I + < + + + + + + + + + + + +" +narrative_qa,a32f354788dd1f1411b9745200cb330d9b556373_12,"< I's the + + 1 + + + + + + + + + + + + 1. + + + + + + +< + +< + + + + + + +" +narrative_qa,26118a3592e63a620bed0d65d1b0943d502e55ef_19," + + + + + +"I'money,0 + + + + + +"I'll + + + + +" + +" + + +" + + + +" + + + +" + + +" + + +" + + +" + +" + +"" +narrative_qa,7235e7853e8ea8ba99b6d7a386d8de03b3887ace_4," + + + +<8. + + +<8. + + + + + + + + +< + + +< + + +< + +< +< +< +< +< +< +< +< +< +< +< +< +< +< +" +narrative_qa,9990281afdfbf883d53ac7540acfcbb375380f75_11,"< J> I +> I +> < < < < < < < < < < < < < < < < < < < < < < < " +narrative_qa,6a02d46e87865ba5b033c56c658af2bfdd182093_18," I + < < +> < +> < < < < < < < < < < < < < < < < < < " +narrative_qa,81db4f3e5fe29c02fd7b7a702aa4847db6a04613_0,"< b> b> I I b> I + I'll +b ask +b> you +b> I ask +b> her +b> I b> I ask +b> I ask" +narrative_qa,08a5821c3e1845f6112f2114e61b717ca8ee79ac_16," I I + of the city +b> of the statue +b> of the statue +<" +narrative_qa,8ec2acc82d024a645fe81f51270d6dbba8b7981e_6," + + + +Translated by Jow + + + +PERSON OF +DIC + + +THE +DION + + +CRIT + +Socrates" +narrative_qa,08a5821c3e1845f6112f2114e61b717ca8ee79ac_14," + + + +> < < < +> +> +> " +narrative_qa,6a02d46e87865ba5b033c56c658af2bfdd182093_2," b> I + < +< < < b> < < < < < < < < < < < < < < < < < < " +narrative_qa,ffa719867c77cfd8fc661fdb6d8c8d266746e15f_12,"< LET + 8 + + + +> 8 + +> + +< + +< + + + + +" +narrative_qa,3513ebaa62106c8e80a0c9723eb456f28950adb8_4," + + + + +"I'sir + + + +"I + +" +" + +" + +" + +" + +" + +" +" +" +" +" +" +" +" +" +" +" +" +" +" +"" +narrative_qa,b833410a8ff29952ca664319cb3462f2fd07d4f9_2," 1 + + + + + + + + + + + + + + + 1. + 1. + + + + + + + + + + + + +" +narrative_qa,1dfe627a09345ed564805313858dc89daf4a2283_2,""I'll be +"I'll be +"I'll be +" +"I'll be +" +"I'll be +" +"I'll be +" +"I'll +" +"I'll +" +"I'll +"" +narrative_qa,0ac0e7c55534399855675cf41872ae6c5a8a32dd_18,""I'm not, I'm not a scholar, I'm not a scholar, I'm not a scholar +" +" said the +"I'm not a scholar, I'm not a scholar +" +"I'm not a scholar +" I'm not a" +narrative_qa,ed524405685e79384ca61daa883a2c0da21cfc9c_2,"Lady, I’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady’s +Lady" +narrative_qa,6d2e1b95ed2a00e16e046b6f3d0b03e687c7f7f7_16," 1. + + + + + + + + + + + + + + +" +narrative_qa,57523a482ae81616899feaff7497fa962edef9b6_14,""I am, my dearest, my dearest, I am +" +"I have a good, and you are +" +"I am not, my dearest, my dearest +" +"I am +"I am +"I am +"I am +"I am +" +narrative_qa,214651e1fc210afdc48cb7272a4d06b79d172708_27,"Transcribed by Mary Mehapter +Produced by Greg Wehan +Transcribed by Online +Team at pg.net + + + + + + + + +======================================================================================================================================== + + + + + + + + + +The End of +=================================================================================" +narrative_qa,ccdf8c8c07e95675fae3591714061ecacfd5ad2e_5," 1. + + + + + + + + + + + + + + +" +narrative_qa,127e1efe32b11e606a0c8f49a2399abb4a52f9d9_12," 1 + 1 + + + + +< + + + + + + + + +" +narrative_qa,b1813716c996049c8a1402af0982489b5bc21d21_10," 2. I'm + + 2. + + + + + + + + + + 1. + 1. + 10MIT 1T +< + +2 +2 + +3 +3 +1 + + + + + + + + + + + + + + +" +narrative_qa,5b074da81e9086fe8761ecfc0f79ff30d07d57c0_8,"<1. +<1. 1. 1. 1. +< +< +<1. 1. 1. +< +<1. 1. +<1. +<1. 1. +<1. +<1. 1. +<1" +narrative_qa,d431326bd5a673425316a46ced23563a00d3e45c_13," + + + + +What type of wreckage of ship from the dark, + + + + + +What type of dark + + +What type +of + +What type +of +dark +of +dark +was +the +the +dark +was +the +was +the +ve" +narrative_qa,7235e7853e8ea8ba99b6d7a386d8de03b3887ace_5," + + + + 1. + + + + +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +" +narrative_qa,5806789ebca66b68e86317d0e09b8a433c236280_17," + + + + + + + + + + + + + + + +" +narrative_qa,5806789ebca66b68e86317d0e09b8a433c236280_24," + + + + + + + + + + + + + + + +" +narrative_qa,3c5a131da6f43e2f92dce91aa238e6782e428748_27," I's + + + + + + + + + + + + + +" +narrative_qa,ccdf8c8c07e95675fae3591714061ecacfd5ad2e_7," 1. + + + + + + +< + + + + + + + + b> I b> I + I + I +< need +b> b> I +b> < I +b> b> b> I +< b> b" +narrative_qa,926198f64d64f2bf796feddd42a4aa1714ddf5b2_20,"Siddha +Siddha's friend, Vasude +Siddha, Vasude +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha" +narrative_qa,f75db432fbc79913fb56349cfdb7ec5446e9941e_4,"p> +

P

P

P> +

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+ +>" +narrative_qa,31c7eca71291b68f55dec4af7e61b6bcae8c5a8a_13," + + + + + +The +The + + +The + +The + + +The + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,2a74184610bf91866dbdf37cba0b874dfc748a86_5,"CRAT: I am not +CRAT: I am +CR: I +CR: I +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR: +CR" +narrative_qa,8cb9a3afd8d542c798c3a34fd1a9afa0a77931c2_0,"I am not the young, I am not, I am not a young, I am not to be +I am not, and the +I am not, I am not, I am not +I am not, I am not +I am not, I am not, I am not, I am not" +narrative_qa,afa2f47d178398d3f3c9515662fc5bbd71b15ccd_17," + + + + + +The +The + + +The + + +The + + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,408d3b92b889218d73adddf502b5f7f9d002f4ab_2,"{ +{ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. " +narrative_qa,b2091fab7149af1b52bdd0a2bc28e60a2092a16a_6," + + + + + +PRODUDRA + + + + +THE + +TER +S +S +S + +ACT +ON +A +A +ACT +ON +It was +Y +A +P +P +L +E +ONE +A +R +ACT +E +" +narrative_qa,862799d267213ae12ec7925feea1faf82358cec9_25,"The first, the +The first +The first +The first, the +The second, +The third, +The fourth +The fifth +The sixth +The seventh +The ninth +The eighth +The ninth +The ninth +The tenth +The ninth +The ninth" +narrative_qa,e0c74cdf270ebe29a2139e7319fc7314738c88ee_28," 1 + +ON +ON + + + + + + + + + + + 1 A +b> 1. A +b 1. + + + + + + + + + + +" +narrative_qa,d431326bd5a673425316a46ced23563a00d3e45c_4,"Produced by Stephen Blund and the Online Proofreading Team +Distributed at " +narrative_qa,a6dfeb4977cc8ba862878693664d326d7c6049e7_38,"I's a rose, a rose, a rose, a rose, a bird, a word +The sun, a tree, a word +I's a star, a flower +I's +I'llow, a word, a tree, a bird, a sound +I's a" +narrative_qa,f69d861b4aed53f6bd9069c588a641929c840ae5_22," + + +CHAP +XIII. +VIII +TION +AFF +T +CHAP +TION +E +X +X +V +I +AG +I +N +AG +E +I +N +O +N +I +N +G +E +R" +narrative_qa,01502137e4276712d118bd4bdaf481c89aed163b_16," 2 8R> + + + + + + + + + + + " +narrative_qa,2a74184610bf91866dbdf37cba0b874dfc748a86_12,"CRAT: I am not the +CR: I am not +CR: I am +CR: I am +AT: I am +CR: I am +CR: I am +CR: I am +CR: I am +CR: I am +CR: I am +CR: I am +" +narrative_qa,b833410a8ff29952ca664319cb3462f2fd07d4f9_26," + + + + + + + + + + + + + +" +narrative_qa,441f692c3e288cd8b6411e792e629ae8b362c649_16,"< b> I +> I + I +b> < I +b> < b> +b> b> +> b> b> +" +narrative_qa,94dc6d01df88f97843813efb11e5d7e48562c869_14,""I am, the man, the other +" ether +" ether +"I am not. +" ether +" ether +" ether +" ether +" ether +" ether +" ether +" ether +" ether +" ether +" +narrative_qa,15618d16f20e7ba33352f06e210f42ef59d84d74_26," 1 + 1 + 1 + 1 +> 1 + 1 + 1 +< + 1 +< 1 +< 1 +< 1 +<" +narrative_qa,c7c075c49018828bf6027da5c5534834779d1adf_24," 2. + + 1. + + + + + + + + + + + +" +narrative_qa,31c7eca71291b68f55dec4af7e61b6bcae8c5a8a_7," + + + + +The Project Gutenberg + + + + + +The Project Gutenberg + + + + +Project + +Guten + + +Project + +Project + +Project +Project +Project +Project +Project +Project +Project +Project +Project +Project +Project +Project +" +narrative_qa,f69d861b4aed53f6bd9069c588a641929c840ae5_7,"CHAP +XIV +XI +V +T +The _ +CHAP +T +V +X +I +X +V +X +X +X +X +X +X +X +X +X +X +X +X +X +X +X +X +X" +narrative_qa,845186ccb0a481e73525e98cbef89279c2f5721e_17,"The desert, the world was a moment of the +" +"I's +"I" +"I" +"I" +" +"I" +"I" +"I" +" +"I" +"I" +" +"I" +"I" +" +narrative_qa,a6dfeb4977cc8ba862878693664d326d7c6049e7_24," + + +The winds, the sun, the sun, the light, and I's + + + + + + +The wind, " +narrative_qa,56434e4849e679e4089c33a5f5bd3050e71c5ec1_16," + + + +The text: 1. What is the +2. What is the title of the poem? +2. What is the poem about? +3. What is the poem about? +4. Who is the speaker? +5. Who is the speaker? +6. Who is the" +narrative_qa,2132babdf6d70933760a9d8e9c6ac5c3305ed253_3," I +> +> I'm +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +> +" +narrative_qa,69add8ffcf964d23243ac02b7282d57beb63c355_2,"RET +Ellips + + + + + + + + + + + + +" +narrative_qa,26118a3592e63a620bed0d65d1b0943d502e55ef_23," + + + + + +"I's + + + + +"I's + + + + +"I + + +" + +" + +" + +" + +" +" +" +" +" +" +" +" +" +" +" +"" +narrative_qa,f1c34596be81d736e30489c95e62a198b02d9fac_18,""I'll be +"I'll be," he said, "I'll be +" +" +"I'll be +" +"I'll be +" +"I'll be +" +"I'll be +" +"I'll be +"" +narrative_qa,87bf382f6852f12cd8342a47c813cd51bed992ad_10,"The door +The door +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door is +The door" +narrative_qa,11c498b404bec5dee6cbf41e54a8b682851fb934_17,"The story is a story of Conan, the +The story of the +The story of the +The story of the +The story of the +The story of the +The story of the +The story of the +The story of the +The story of the +The story of the +The story of" +narrative_qa,bfaf8bc1a86d524c9431e855c139da705730f165_24,"“I’surely, the +“I’m not +“I’m afraid +“I’m afraid +“I’m afraid +“I’m +“I’m +“I’m +“I’m +“I’m +“I’m +“" +narrative_qa,c5165082a52b8427b974f0ed3152862f0d51ffb7_26," < < < + < < + < < +> < < < < < < < < < < < < " +narrative_qa,845186ccb0a481e73525e98cbef89279c2f5721e_28,"The +"I's +"I'm not a moment of the +" +" +"I'm not +" +"I'm not +" +"I'm +" +"I +"'m +"I +" +"I +" +"I +" +narrative_qa,afa2f47d178398d3f3c9515662fc5bbd71b15ccd_22," + + + + + +The Little + + + +The + +The + + +The + + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +" +narrative_qa,39c5eed544ceeb78eb3202d040eb35361d96945f_1," 0 - 2 - 5/5/1 + + + 0 +> + 0 + +> 0 + + + " +narrative_qa,afa2f47d178398d3f3c9515662fc5bbd71b15ccd_15," + + + + + +The Projects + + + +The + +The + + +The + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +" +narrative_qa,6d3020e7252fb731314bad3522cd416af124a3f0_17,"CH. +I's + + + +I's + + +XI + +I +X +X +XI +XI +XI +X +X +X +X +X +X +X +X +X +X +X +X +X +X +X" +narrative_qa,9d8ddfe86beba149b3463eeb1bae92919e179fed_8," I'm not + + 1 +> I'm not + + +> 1 +> 1 +> +> +> +> +> +> +> +" +narrative_qa,4e959a5d22e1968a1950ac836a1ba0cc3edffa41_19,"[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1." +narrative_qa,6cfe7052daf187b667538b2858849a84c1f6b049_24," + + + + +The + +The + + +The + + +The + + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,879287f285743b08d86c3bd0831ec1e49fa92e39_6," + + + +"I't is not +"I'm afraid +" +"I +" +"I +" +"I +" +"I +" +"I +" +"I +" +" +"I +" +" +" +" +" +" +narrative_qa,f70edecebfabbfaa65fb58ada2a6642453d217a9_28,""I's a +"I'm afraid, the +" +"I'm afraid +" +"I'm afraid +" +"not +"I's +" +"of +"I +" +"I +" +" +" +" +" +"" +narrative_qa,ab1da932cda3b43d4b44a7083b23787b62f4526c_17,""I'm, +"I'm afraid, +"I'm afraid +" +"I'm afraid +" +"I'm afraid +" +"I'm afraid +" +"I'm +"re +"you +"re +"I +"re" +narrative_qa,a6dfeb4977cc8ba862878693664d326d7c6049e7_7," +I'sighing to- + + + +I'llow, I'm not know + + + + +I'm not know + +I'm not +I'm not +I'm +I'm +I'm +I'm +I'm +" +narrative_qa,879287f285743b08d86c3bd0831ec1e49fa92e39_9,"The world is a moment of the +The +The world is nothings of the +The +The is nothings of the +The ish +The ish +The ish +The ish +The ish +The ish +The ish +The ish +The ish" +narrative_qa,a6dfeb4977cc8ba862878693664d326d7c6049e7_37,"I's a rose, I's not a rose, I's not a rose +The sun, but a +I's not a rose +I's not a bird +I's not a bird, but a rose +I's not a rose +I's not a flower" +narrative_qa,12ec522e344ec19702bc8bc74d85b4e5adf2d229_16,"She was not, she was not to be, she could not the +She was not, but the +She was not, but the +She was not, she was not, she was not +She was not, she was not +She was not, she was not +She was not, she was not" +narrative_qa,51f816f54645196b0a8f588f9ae955de103565f7_7," 1. + 1 + + + + + + + + + + + + +<" +narrative_qa,b1813716c996049c8a1402af0982489b5bc21d21_23," 2. I'm + 2. + + + + + + + + + + + I + b> I + I +< < b> b> I +< b> b> b> +< b> b> b> +< b" +narrative_qa,8cb9a3afd8d542c798c3a34fd1a9afa0a77931c2_3,"I am not, I am not a young, I am not to be +I am not, I am not +I am not, I am not +I am not, I am not +I am not, I am not +I am not, I am not, I am not, I am not," +narrative_qa,ebbac675f4d13527d9d6af2aeddff8f92b7e3e4d_20," 1 + Mr. +> 1 + I'm not + +> + +> +> + + +> + +> +> +>" +narrative_qa,f90f97c9ae38be55921b5e8a93d06ddada82dd1f_28,""I's not, he is not the same, he said, "I'm not +" +"I'm not, and the +" ether +"I'm not +" +"I'm not +" +"I'm not +" +"I'm" +narrative_qa,ea371ba9cc41bf32aa8b37549662d2d3a38f084d_28,"The sun, she was a moment of the +The sun, she was +The sun, she was +The sun, she was +The sun, she was +The sun, she was +The sun, she was +The sun, she was +The sun, she +The sun, she +The sun" +narrative_qa,8a7a91b669cd6a37e96abcf846ef45a9c4cbb692_18," + +The story of the + + + +The story of the + + + +The + +The + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,87bf382f6852f12cd8342a47c813cd51bed992ad_11,"The +The 1. +The 1st +The 1st +The 2nd +The 2nd +The 3rd +The 3rd +The 4th +The 4th +The 5th +The 5th +The 6th +The " +narrative_qa,12ec522e344ec19702bc8bc74d85b4e5adf2d229_17,"She was not the time, the time, she was not to be, but the +She was not to be +The time of her; she was not, and the +She was not to be +She was not, but the +She was not, the +She was not, the +She was not" +narrative_qa,69add8ffcf964d23243ac02b7282d57beb63c355_15,"RET +Ellips + + + + + + + + + + + + +" +narrative_qa,c5165082a52b8427b974f0ed3152862f0d51ffb7_27," < < + < < + < < < < < < < < < < < < < < < <" +narrative_qa,09c9afb4abbd6064c1f26c3632edbe6d92a34206_20," 6 + + +> 1. + +> 1. +> +> +> +> +> +> +> +> +> +> +>" +narrative_qa,31c7eca71291b68f55dec4af7e61b6bcae8c5a8a_40," + + + + +The + +The + +The + +The + + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,26118a3592e63a620bed0d65d1b0943d502e55ef_15," + + + + + +"I'money + + + + +"I'll + + + + +" + +" + +" + + +" + + +" + +" + +" +" +" +" +" +" +" +" +" +"" +narrative_qa,e5514678c3a6ac0dad17b96993ea231da4fecd0a_18,""I'm not +"I'm not +" +"I'm not +" +"I'm not +" +"I'm +" +"I +"I +" +"I +"I +" +"I +"I +" +"I +" +narrative_qa,199ec80c97f752cd8e00d2ca6f8e5f48603fefb8_9," 1. + 1. + + + + + + + + + + + + +" +narrative_qa,926198f64d64f2bf796feddd42a4aa1714ddf5b2_0,"Siddhar is a friend +Siddha, Vasude +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Siddha +Sidd" +narrative_qa,ebfad83ccf1214df566432798c54deb17e91d958_14,"The crowd, the doctor, the + + + + + +The crowd, the crowd, the +The crowd, the +The crowd, the +The crowd, the +The crowd, the +The crowd, the +The crowd, the +The crowd, the +The crowd, the +The crowd," +narrative_qa,754184dec0d686d0b8b9ad7e1dc15bf0eb5027f5_12,"BI'm not +I'm not + + +AN +I'm + +I + + + +" +narrative_qa,ea371ba9cc41bf32aa8b37549662d2d3a38f084d_11,"I'm not know. +I'm not sure, I'm not sure +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +I'm not +" +narrative_qa,e0c74cdf270ebe29a2139e7319fc7314738c88ee_27," 1 + +ON +ON + +ON + + + + + + + + + 1. + Mr. Orange + I'm +< + +> 1 + +> + + +> + + +> + + +> + +>" +narrative_qa,127e1efe32b11e606a0c8f49a2399abb4a52f9d9_13," 1 + + + + + +< + + + + + + + + +" +narrative_qa,0ecd1d809dabb7dab891d9c8a9e19a5f76397d2d_27," 1 + 1 + + + + + + + + + + + + + +" +narrative_qa,ebbac675f4d13527d9d6af2aeddff8f92b7e3e4d_6," 1 + Mr. +> 6 +> 1 +> +> +> +> +> +> +> +> +> +> +> +> +> +> +>" +narrative_qa,2a74184610bf91866dbdf37cba0b874dfc748a86_8,"CRAT: I am not the +CR: I am not +CR: I am +AT: I +CR: I am +CR: I am +AT: I am +CR: I am +CR: I am +CR: I am +CR: I am +CR: I am +CR" +narrative_qa,de42ec88ce00d0f1e21ad4ad719d1d64499e5166_25," + + + + + + + + + + + + + + + +" +narrative_qa,922ac7cfbc0ffe736ce42b2f0035caed42364bed_27," + + + + + + + + + + + + + +" +narrative_qa,afa2f47d178398d3f3c9515662fc5bbd71b15ccd_12," + + + + +The + +The + + +The + + +The + + +The + +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The +The" +narrative_qa,5b074da81e9086fe8761ecfc0f79ff30d07d57c0_26,"<1. +<1. 1. 1. +< +<1. +<1. +<1. +<1. +<1. +<1. +<1. +<1. +<1. +<1. +<1. +<1 +<" +narrative_qa,69add8ffcf964d23243ac02b7282d57beb63c355_39," +RET + + + + + + + + + + + + +" +narrative_qa,d431326bd5a673425316a46ced23563a00d3e45c_21," + + + +[The Mystery of this great ship from the dark reaches +of space? +By J. + +Illustrated by Edsh +Sabo +John Wynd +Saber +Sabo illustrated by +Distributed by Pgreading Team +at + + + +" +narrative_qa,ab1da932cda3b43d4b44a7083b23787b62f4526c_15,""I'might be the +"--their +"--the +"I're +"--the +"--the +"-- +"-- +"-- +"-- +"-- +"-- +"-- +"-- +"-- +"-- +"-- +"" +narrative_qa,214651e1fc210afdc48cb7272a4d06b79d172708_17,"Transcribed by Greg + 1 + + + + + + + + + + + + + + + +Ellips, the ground + + + + + + + + + + + + + +<" +narrative_qa,f94253b4b5f8cfb2afea5f0b9d6bb87de266d11c_4,"The superint, the +The superintendent, "I am +" +"I am not +" +"I am +" I +" I +" I +" I +" I +" I +" I +" I +" I +" I +" I +" I +" +narrative_qa,ffa719867c77cfd8fc661fdb6d8c8d266746e15f_8," LET + 8 + + +> 8 +> +> +> 8 + +> +> +> +> +> +> +> +> +" +narrative_qa,ebfad83ccf1214df566432798c54deb17e91d958_26,"1. +1. What is the name of the main character in the story? +2. +2. What is the name of the character's husband? +3. What is the name of the character's husband? +3. What is the name of the character's +4. What" +narrative_qa,961902ae5bc5f08fcdf4807552d423f7596f5695_29,"< A < K b> I +> b> I +> I +b < I +b I +b I < I +b> I +b> I +b> I +b> I +" +narrative_qa,d285c6beb3dc935411fd3297b02602b8afc7170c_6,""I's +"I'm not +" +"I'm +" +"I +"I +" +"I +"I +" +"I +" +"I +" +"I +" +"I +" +" +"I +" +"" +narrative_qa,edb92e9d9fe05242da8ead459d53853c8815716b_2,"MY + + + + +THE Archde + + + +THE + + +THE + +THE + + +THE + +THE + +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +THE +" +narrative_qa,ebbac675f4d13527d9d6af2aeddff8f92b7e3e4d_1," 1. + I'm not +> +> 1 +> +> +> +> +> +> +> +> +> +> +> +> +> +> " +narrative_qa,08a5821c3e1845f6112f2114e61b717ca8ee79ac_18,"< CUT> + + < < +> +> + +> +>" diff --git a/evaluation/zeroscrolls/predictions/vicuna-7b-v1.3/vicuna-7b-v1.3_qasper_15000.raw b/evaluation/zeroscrolls/predictions/vicuna-7b-v1.3/vicuna-7b-v1.3_qasper_15000.raw new file mode 100644 index 0000000..7719a0a --- /dev/null +++ b/evaluation/zeroscrolls/predictions/vicuna-7b-v1.3/vicuna-7b-v1.3_qasper_15000.raw @@ -0,0 +1,848 @@ +Task,ID,Prediction +qasper,f7a89b9cd2792f23f2cb43d50a01b8218a6fbb24,"The modeling the model is aims to improve the most important, the most important, the most important, the most important, and the most important, the most important, the most important, the most important, the most important, the most important, the most important, the most important, the most important, the" +qasper,b14217978ad9c3c9b6b1ce393b1b5c6e7f49ecab,"The model is a model is a good way to predicting +We use the model of the model of the model. +We use the model of the model +The model is a good of the model +The model is a good of the model +The model is a good of the model +The model is a" +qasper,61652a3da85196564401d616d251084a25ab4596,"The size of the dataset is the dataset is the number of the number of sentences in the paper. +The dataset is the number of sentences in the paper. +The number of sentences in the paper is the number of sentences in the paper. +The number of sentences in the paper is the number of sentences in the" +qasper,81a35b9572c9d574a30cc2164f47750716157fc8,"The table above, the table shows the results of the performance of the models in the fine-tation of the best-tation, the best-er, the best-performing models are the best-performing models are the best-performing models, the best-performing models, the best-" +qasper,bd419f4094186a5ce74ba6ac1622b24e29e553f4,"The baseline +The baseline model is aims to be a good model of the navigation, but the best-performing the best-performing model in the best-performing model. +The baseline is a good-performing model is the best-performing model. +The best-perform" +qasper,df0257ab04686ddf1c6c4d9b0529a7632330b98e,"The proposed method is more efficient than existing solutions? +The proposed approach is more efficient in terms of text-based games +The proposed method is more efficient than existing solutions in terms of the environment. +The proposed method is more efficient in terms of the environment. +The proposed method is more efficient in terms of the" +qasper,197b276d0610ebfacd57ab46b0b29f3033c96a40,"The methods used in this paper are the following: +Performance appraisal (PA) is an important process, particularly the process of modern organizations and the skills of their workforce. The process depends on the organization to evaluate every employee's performance. It appraisal process an organization to measure and PAREF" +qasper,ca4daafdc23f4e23d933ebabe682e1fe0d4b95ed,"The lexicon is a Bayesian classifier is a lexicon is a lexicon-based language. The lexicon is a lexicon-based language. The lexicon is a lexicon is a lexicon. The lexicon is a lexicon is a lexicon. The lexicon is a lexicon." +qasper,b5e4866f0685299f1d7af267bbcc4afe2aab806f,"The source of the news is not provided is not specified." +qasper,31d695ba855d821d3e5cdb7bea638c7dbb7c87c7,"The neural architecture +The neural architecture of the attention is a base for the attention is the model +The neural architecture of the attention is the model. +The neural architecture of the attention is the neural architecture of the attention. +The neural architecture of the attention is the neural architecture. +The neural architecture of the attention" +qasper,12f7fac818f0006cf33269c9eafd41bbb8979a48,"The models use a variety of models use +The models we propose in this paper are based on the following: +The models we propose are based on the following: +The models we propose a variety of models are based on the following: +The models are based on the following: +The models are based on the" +qasper,565189b672efee01d22f4fc6b73cd5287b2ee72c,"The dataset used in this study is the WMT is the parallel corpus of the Europar, which is the ENGL-10, which is the ENGL, which is the EN1, and the EN2, and the EN1, and the EN2, and the EN3. The EN4" +qasper,cfcdd73e712caf552ba44d0aa264d8dace65a589,"The data for this paper is from a variety of sources such as news articles, books, websites, and other textbooks, and other online sources. +The data is from a variety of sources, including Wikipedia, and other textbooks, and the web pages. +The data is from a variety of sources, such" +qasper,2c6b50877133a499502feb79a682f4023ddab63e,"The paper focus of this paper is on machine translation and simplification of text." +qasper,b91671715ad4fad56c67c28ce6f29e180fe08595,"We use the word20 +We use the word20202019999999999999999999999999999999999999999999999" +qasper,8c48c726bb17a17d70ab29db4d65a93030dd5382,"The amount of data needed to train the task-specific encoder is determined by the number of annotated data. +The amount of data used to train the task-specific encoder is determined by the number of annotated instances. +The amount of data used to train the task-specific encoder is the number" +qasper,31735ec3d83c40b79d11df5c34154849aeb3fb47,"The human evaluators were graduate students from the University of Pittsburgh. +The human evaluators were graduate students in the University of Education. +The summaries were graduate students from the University of Pittsburgh. +The summaries were graduate students from the University of Pittsburgh. +The summaries" +qasper,5fa36dc8f7c4e65acb962fc484989d20b8fdaeec,"The system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the system, the" +qasper,b65a83a24fc66728451bb063cf6ec50134c8bfb0,"The paper is aims of the summary. +The paper is about summarization of the summarization of large texts. +The paper is aims to summarize large texts. +The paper is to summarize large texts. +The paper is to summarize large texts. +The paper is to summarize large" +qasper,9da1e124d28b488b0d94998d32aa2fa8a5ebec51,"The state-of-of-the-the-art results are the-art +The state-of-of-the-the-art +The state-of-of-the-the-art +The state-of-of-the-the-art +The state-of-of-" +qasper,a6a48de63c1928238b37c2a01c924b852fe752f8,"The summarization of large text summarization is aims to summarize the process of text. +The summarization of text. +The summarization of text. +The summarization of text. +The summarization of text. +The summarization of text. +The summarization of text. +The summar" +qasper,adbf33c6144b2f5c40d0c6a328a92687a476f371,"The NER, we propose a model that learns NER from text and images +The model is aims to learn from both text and images. +The model is aims to learn from both text and images. +The model is aims to learn from both text and images. +The model is to" +qasper,567dc9bad8428ea9a2658c88203a0ed0f8da0dc3,"The best model is the model +The best model is the one that performs the best +The best model is the one that performs the best on the test set of all the data. +The best model is the best model is the best model. +The best model is the best model. +The best model is" +qasper,01209a3bead7c87bcdc628be2a5a26b41abde9d1,"In this paper, we use the field of natural language processing (NLP, most prevalent approach to neural +In the neural +In the field of natural approach to neural +In the field of natural approach to neural +In the field of natural language (most neural +In the neural approach to neural +In" +qasper,ed2eb4e54b641b7670ab5a7060c7b16c628699ab,"The three pretraining tasks are the most helpful +The three pretraining tasks are pretraining tasks are pretrained models: +The three pretraining tasks are pretrained models are pretrained models. +The three pretrained models are pretrained models are pretrained models. +The three pre" +qasper,aa6d956c2860f58fc9baea74c353c9d985b05605,"The evaluation metrics used for the summarization task +The evaluation metrics used in this paper are not the F1. +The evaluation metrics used in this paper are not the following: +The evaluation metrics used in this paper are the following: +The evaluation metrics used in this paper are the following: +The evaluation metrics" +qasper,71ba1b09bb03f5977d790d91702481cc406b3767,"The data: The data is a modeling. +The data is a model. +The data is a model. +The data is a. +The data is a. +The data is a. +The data is a. +The data is a. +The data is a. +The data is" +qasper,252a645af9876241fb166e5822992ce17fec6eb6,"The average length of the text is 10 +The average length of the text is 10 +The average length of the text is 1 +The average of the text is 1 +The average of the text is 1 +The average of the text is 1 +The average of the text" +qasper,de3b1145cb4111ea2d4e113f816b537d052d9814,"The baseline is a dataset of tweets. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is" +qasper,96a4091f681872e6d98d0efee777d9e820cb8dae,"Their model captures the model captures the biases in the biases of hate in the following: +The model captures the biases of hate in the model. +The model captures the biases in the hate in the model. +The model captures the biases in the hate in the" +qasper,5260cb56b7d127772425583c5c28958c37cb9bea,"The modeling the modeling of the context is a large number of the number of words in the model is the number of words in the model. +The model is the number of words in the model is the number of words in the model. +The model is the number of words in the model is the model" +qasper,8eefa116e3c3d3db751423cc4095d1c4153d3a5f,"The NER, we use the NER, we use the NER, we use the most important, we use the most common, we use the most common, we use the most common, and the most important, and the most important, and the most important. +The most important, the most important," +qasper,8060a773f6a136944f7b59758d08cc6f2a59693b,"The authors of the model is a large amount of data is 10, the model. +The model is 10. +The model is 10.5.5.5.5.5.5.5.5.5.5.5.5.5.5.5" +qasper,ef7212075e80bf35b7889dc8dd52fcbae0d1400a,"The current ELS's are not sufficiently effective in summarization is not effective in the summarization because they are not able to handle the problem of the problem of disambiguation. +The problem of disambiguation. +The problem of disambiguation is not handled by the summarization. +The problem of disambiguation is" +qasper,1dac4bc5af239024566fcb0f43bb9ff1c248ecec," + + +The paper is aims to generate a headline +The paper is aims to generate a headline. +The paper is to generate a headline. +The paper is to generate a headline. +The paper is to generate a headline. +The paper is to generate a headline" +qasper,bf3b27a4f4be1f9ae31319877fd0c75c03126fd5,"The task of text-to-models +The task of structured data +The task of generating natural descriptions of structured data (such as tables +BREF2 +BIB3 +BREF has seen the rise of models and sequence to generating tables +For the sequence of models +For the rise of" +qasper,7697baf8d8d582c1f664a614f6332121061f87db,"The modeling the model is aims of the model, the model, the model is the model is the modeling of the model, the modeling, the modeling, the modeling, the modeling, and the modeling, and modeling, and modeling, and modeling, and modeling" +qasper,6bf93968110c6e3e3640360440607744007a5228,"The car's physical attributes +The car's physical +The car's attributes are the car's +The car's physical +The car's +The car's physical +The car's physical +The car's physical +The car's physical +The car's physical" +qasper,2f901dab6b757e12763b23ae8b37ae2e517a2271,"The languages used in machine translation are a variety of machine translation are a variety of neural networks, including convolutional, recurrent, long-based, recurrent, and hybrid, and convolutional, and convolutional-based models. +Recurrent models. +Recurrent models are the most commonly used in machine" +qasper,d087539e6a38c42f0a521ff2173ef42c0733878e,"Recently, context-aware models such as ELREF, BIB0, BIB, BREF, GIB2, and XIB3, and BREF, and BREF have shown to outperform traditional models including Word2, Word2, BERT in NLP, GLO, and fine-" +qasper,2ee715c7c6289669f11a79743a6b2b696073805d,"We determine the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number" +qasper,cfcdd73e712caf552ba44d0aa264d8dace65a589,"The data for this paper is from a variety of sources such as news articles, books, websites, and other textbooks, and other online sources. +The data is from a variety of sources, including Wikipedia, and other textbooks, and the web pages. +The data is from a variety of sources, such" +qasper,4f253dfced6a749bf57a1b4984dc962ce9550184,"The authors find it is a big overhead to be a big overhead to choose from models +The authors +The authors find it a big overhead to choose from models +How do the claim that engineers it a big overhead +The authors find it to choose from models +It is a big overhead to choose from models +" +qasper,dac087e1328e65ca08f66d8b5307d6624bf3943f,"In this paper, we propose a novel method is an unsupervised approach to detection +In this paper, we propose a novel method. +In this paper, we propose a novel approach to detecting unsupervised approach. +In this paper, we propose an unsupervised approach +In this paper, we" +qasper,6424e442b34a576f904d9649d63acf1e4fdefdfc,"The authors evaluate on the Penn, Penn Treebank, Wall Street Journal, and Treeb, and Treebank, and Treebank, and Treeb, we use the modeling, and Penn, and Penn. +The authors use the same, Penn, Treeb, Tre" +qasper,8c852fc29bda014d28c3ee5b5a7e449ab9152d35,"The models used in this paper are a variety of models +The models used in the paper are a variety of deep learning and aims to identify offensive and social media. +The models in this paper are aims to identify offensive and offensive language. +The models in this paper are aims to identify" +qasper,7fa3c2c0cf7f559d43e84076a9113a390c5ba03a,"Their own data, the pronouns of the pronouns of the pronouns, the pronouns, the pronouns of the pronouns, the pronouns of the pronouns, the pronouns of the pronouns, the pronouns, the pronouns" +qasper,902b3123aec0f3a39319ffa9d05ab8e08a2eb567,"The algorithm used to create word embeddings +The algorithm used to create word embeddings is a supervised models is a deep learning model, which is a neural network-based approach that is trained on the word2. The model is a set of the word and its context of the word and its context." +qasper,d015faf0f8dcf2e15c1690bbbe2bf1e7e0ce3751,"The paper explored in this paper are offensive language and offensive language, abuse, bullying, hate, and offensive, and offensive language. +The paper is aims toxic, and offensive, and offensive, and offensive. +The paper is aims toxic, off" +qasper,3116453e35352a3a90ee5b12246dc7f2e60cfc59,"The proposed model +The proposed model is a baseline is compared to the state-of-of-the-the-art model, which is a deep learning for the best model in the task. +The proposed model is a deep learning-based on the best model. +The proposed model is a model is" +qasper,114934e1a1e818630ff33ac5c4cd4be6c6f75bb2,"The paper is a novel approach is a collective model that is aims to improve the efficiency of the efficiency of the best-of the best-performing models. +The paper is aims to the best-performing models. +The paper is aims to improve the best-performing models. +" +qasper,96b07373756d7854bccc3c12e8d41454ab8741f5,"The question of the task of whether RoBERT outperforms BERT in the speculation is aims to be a question of the best model. +The task of the best way to use of the best model. +The task of the best model is the best model is RoBERT, the best model" +qasper,88e5d37617e14d6976cc602a168332fc23644f19,"The modeling is a modeling is a model of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the" +qasper,220d11a03897d85af91ec88a9b502815c7d2b6f3,"In this section, we focus on the word2, we use the word2, we use the word2, we use the word're not the word's contextual representation. +In this is not the word's context. +In this section, we use the word's context. +In" +qasper,fb5fb11e7d01b9f9efe3db3417b8faf4f8d6931f,"The data is a model to the most of the data, the data. +The model is a good of the +The model is a +The model is a +The model is a +The model is a +The model is a +The model is a +The model is a +The model is a +" +qasper,cc354c952b5aaed2d4d1e932175e008ff2d801dd,"The results of the results of the analysis, we found that the difference between the system, the number of the number of submissions, the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the" +qasper,06eb9f2320451df83e27362c22eb02f4a426a018,"The paper is aims to the preprocessing of the preprocessing of the text, the most important factor in the model. +The most important factor in the modeling of the modeling of the text. +The most important factor in the modeling of the text. +The most important factor in the modeling" +qasper,41b70699514703820435b00efbc3aac4dd67560a,"The dataset is about a civil +The dataset is about a legal field +The dataset is about a legal case +The dataset is about a legal case +The dataset is about a legal case +The dataset is about a legal case +The dataset is about a legal case +The dataset is about a legal case +The" +qasper,184b0082e10ce191940c1d24785b631828a9f714,"The paper refutes the common belief that the common belief that summarization is not reliable. +The common belief that summarization is not reliable. +The common belief that summarization is not reliable. +The common belief that summarization is not reliable. +The common belief that summarization is not reliable. +The" +qasper,cf171fad0bea5ab985c53d11e48e7883c23cdc44,"The Twitter dataset is a social media analysis is a popular topic in natural language processing and sentiment analysis. +The dataset is aims to identify and categorise opinions expressed in social media. +The dataset is used to sentiment analysis. +The dataset is mainly for sentiment analysis and social media analysis. +The dataset is mainly" +qasper,5bc1dc6ebcb88fd0310b21d2a74939e35a4c1a11,"The languages used in the experiment are English, Spanish, Finnish, English, French, German, and Spanish, and Chinese. +The languages, and Chinese. +The languages used in the experiment are English, French, Spanish, German, and Chinese, and Chinese. +The languages used in the experiment are English" +qasper,348886b4762db063711ef8b7a10952375fbdcb57,"We build and test our MT models on Multi3K dataset BREF1 IB2 +We report on Multi3K2 +We build and test our models on Multi3K dataset BREF2 +We build our models on Multi3K2 +We build our models on the Multi3K +We build" +qasper,0ab3df10f0b7203e859e9b62ffa7d6d79ffbbe50,"The evaluation metric used in this paper is the F10 metric is the accuracy. +The paper uses the accuracy. +The paper uses the accuracy of the model. +The paper uses the accuracy of the model. +The paper uses the accuracy of the model. +The paper is the accuracy of the model." +qasper,3e1829e96c968cbd8ad8e9ce850e3a92a76b26e4,"The dataset used in this work is a large-scale Twitter dataset of 10,0,0000000000 tweets from Twitter accounts,000000000,0000 tweets000000000000" +qasper,2ceced87af4c8fdebf2dc959aa700a5c95bd518f,"The data was collected from different corpora +The data was collected from different corpora and the Portuguese, the +The data was collected from different corpora and the Portuguese, the Portuguese, the +The data was collected from the Portuguese, the Portuguese, the Portuguese, the Portuguese, the Portuguese, the Portuguese, the" +qasper,cb6a8c642575d3577d1840ca2f4cd2cc2c3397c5,"The table of the table above is a table of the table of the table of the table. +The table of the table is a table of the table. +The table of the table is a table of the table. +The table of the table is a table of the table. +The table of the table" +qasper,8b0abc1907c2bf3e0256f8cf85e0ba66a839bd92,"The ontology, the text of the text of the text is aims of the text is a system of the text, the text. +The text of the text is aims of the text is a text. +The text of the text is aims of the text is a text. +The text of" +qasper,78292bc57ee68fdb93ed45430d80acca25a9e916,"The LA framework for negation +The LA framework is aims to evaluate the performance of language models to negation. +The LA framework for negation is aims to evaluate the performance of language models. +The framework for negation is to evaluate the model. +The framework for negation is to evaluate the" +qasper,e431661f17347607c3d3d9764928385a8f3d9650,"The paper is aims to improve the performance of the performance of the state-of the state-of-the-art. +The paper is the best-art. +The paper is the best-performances of the best-performances of the best-performances of the best-performances of the" +qasper,545e92833b0ad4ba32eac5997edecf97a366a244,"The component of the component that has the greatest impact on performance is the model. +The component that has the greatest impact on performance is the model. +The component that has the greatest impact on performance is the model. +The component that has the greatest impact on performance. +The component that has the greatest impact on" +qasper,67e9e147b2cab5ba43572ce8a17fc863690172f0,"The approach is demonstrated in the approach is highly effective in detecting events, as a modeling the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the" +qasper,111afb77cfbf4c98e0458606378fa63a0e965e36,"In this section, we propose a Bayesian modeling is not only a semantic role in the most of the most of the most of the most of the most of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the" +qasper,12f7fac818f0006cf33269c9eafd41bbb8979a48,"The models use a variety of models use +The models we propose in this paper are based on the following: +The models we propose are based on the following: +The models we propose a variety of models are based on the following: +The models are based on the following: +The models are based on the" +qasper,9cf070d6671ee4a6353f79a165aa648309e01295,"The size of the model is the parallel corpus used to train the model. +The model is the number of the model is the number of the number of the number of the number of the number of sentences. +The number of sentences in the number of the number of the number of the number of the number of" +qasper,76121e359dfe3f16c2a352bd35f28005f2a40da3,"The authors consider a variety of natural language processing tasks, including text classification, sentiment analysis, text categorization, and sentiment analysis, and topic classification, and sentiment analysis, and emotion classification, and sentiment analysis, and topic classification. +The authors of the paper considers the following tasks: +The paper considers" +qasper,f161e6d5aecf8fae3a26374dcb3e4e1b40530c95,"The modeling the modeling is a doctor's transcripts, we use the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling of the modeling" +qasper,1d9aeeaa6efa1367c22be0718f5a5635a73844bd,"The GRU-based models are able to capture + + + + + +The GRU-based models are able to detect emotions + + +The GRU models are able to detect +The models are able to detect +The models are able to detect +The models are able to detect +The models are" +qasper,c5abe97625b9e1c8de8208e15d59c704a597b88c,"The proposed methods in this section are a set of text-based reinforcement algorithms for relatively small or continuous or discrete action spaces and text-based games. +The results from these proposed methods are not only for discrete or continuous action spaces. +The results from these strategies are not for discrete or text-based" +qasper,4c07c33dfaf4f3e6db55e377da6fa69825d0ba15,"The dimension of the embeddings +The dimension of the embeddings +The dimension of the embeddings +The dimension of the embeddings is the dimension of the embeddings is the dimension of the dimension of the embeddings is the dimension of the dimension of the dimension of the dimension of the" +qasper,584af673429c7f8621c6bf83362a37048daa0e5d,"The storytelling is aims to be a textual, and visual +The Visual Processing +The Visual Processing +The Visual Processing +The Visual Processing +The Visual Processing +The Visual Processing +The Visual Processing +The Visual Processing +The Visual Processing +The Visual Process" +qasper,ba28ce9a2f7e8524243adf288cc3f11055e667bb,"Their owners and the data, we are, we are not, we are not, we are not, we are not. +Their owners of the data, and the data, and the data. +Their and the data, and the data of the data, and the data. +The" +qasper,36ae003c7cb2a1bbfa90b89c671bc286bd3b3dfd,"The model's performance is a large-based on the character's performance. +The model's performance is the model's performance. +The model's performance is the model's performance is the model's performance. +The model's performance is the model's performance. +The" +qasper,e025061e199b121f2ac8f3d9637d9bf987d65cd5,"Performance appraisal (PA) is important for modern organizations and the process of the process of the organization. +Performance is an important process of the organization. +Performance is an evaluation of the employee. +Performance is an important process of the organization. +Performance is an important process of the" +qasper,fb2b536dc8e442dffab408db992b971e86548158,"The interannotator agreement refers to the degree to which the consistency of the human evaluator's evaluation of the summaries generated summaries and the model's performance. +The interannotator's agreement refers to the model's performance. +The interannotator's agreement refers to the model" +qasper,28067da818e3f61f8b5152c0d42a531bf0f987d4,"The table-to-to-texts are not the best-texts are the best-texts of the best-texts in the best-texts in the best-texts. +The best-texts +The best-texts +The best-texts +The best-text" +qasper,26126068d72408555bcb52977cd669faf660bdf7,"The benchmarks are the results of the results of the word20, the results of the word2, the results of the word2, the results of the word, the results of the results of the results of the results of the results of the results of the results of the results of the results of the results" +qasper,1dc2da5078a7e5ea82ccd1c90d81999a922bc9bf,"The authors conduct experiments on the tasks mentioned +The authors conduct experiments +The authors do not mention the tasks in the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention" +qasper,14b74ad5a6f5b0506511c9b454e9c464371ef8c4,"The paper explores the language pairs in this paper +The paper +The paper explores neural machine translation (NMT in the last few years as a very successful paradigm BIB , BIB0 , BREF , BREF , BIB2 . NMT . While neural machine translation generally more than statistical" +qasper,deb89bca0925657e0f91ab5daca78b9e548de2bd,"The five binary classification tasks are: +1. Decoding intended speech from brain activity from brain signals +2 +2. +3. Speech activity in the brain Interface +4. +5. BCI +3. BCI +4. BCI +5. BCI +5. BCI +" +qasper,e5c8e9e54e77960c8c26e8e238168a603fcdfcc6,"The proposed algorithm is a Bayesian classifier is a good model for text classification of text classification. The lexicon is trained to predict the text, the specific language. The lexicon is used to predict the language. The lexicon is trained to predict the specific language. The classifier is a good performance of text" +qasper,8bb0011ad1d63996d5650770f3be18abdd9f7fc6,"The paper is aims to be the first model, the model, the model is aided by the question, the question is the answer to stem, the question is the corresponding to the passage. The model is a neural-based model. The model is a neural-based model. The model is a network-" +qasper,ce2b921e4442a21555d65d8ce4ef7e3bde931dfc,"The modeling the results of the results of zero-shot, we present the results of the zero-shot, we compare the results of the zero-shot, the results of the zero-shot, the zero-shot results of the zero-shot results, the zero-shot results, the zero-shot results" +qasper,9c33b340aefbc1f15b6eb6fb3e23ee615ce5b570,"The spatial aspect of the EEG signal was computed by using electroence of the EEG is a combination of the brain activity in the brain, which is a non-invasive, which is a non-invasive, which is a non-invasive, which is a non-invasive" +qasper,12d7055baf5bffb6e9e95e977c000ef2e77a4362,"We use the modeling the modeling of the most difficult task, we use the most difficult instances. +We use the most difficult instances. +We use the most difficult instances. +We use the most difficult instances. +We use the most difficult instances. +We use the most difficult instances. +We use" +qasper,50cb50657572e315fd452a89f3e0be465094b66f,"In this paper, we experiment on the model is the model is not only one of the most important. +In this paper, we focus on the model. +In this paper, we focus on the model is the model. +In this paper, we focus on the model. +In this paper, we focus" +qasper,f0b2289cb887740f9255909018f400f028b1ef26,"In the era of social media and networking, harassment towards women +In the era of social media, online harassment towards women +In the era of social media, online harassment has become very common. Online harassment has become a problem. Online harassment has become a form of Twitter" +qasper,e587559f5ab6e42f7d981372ee34aebdc92b646e,"Conclusion +In this paper, we present a new state-of-of-of-the-the-art results on Libech results on Libech tasks, we show a new state-of-of-the-the-art results on Libech, Jasper, a new state-of-of" +qasper,68df324e5fa697baed25c761d0be4c528f7f5cf7,"The Macaw is a conversational +The Macaw is a conversational interface is a user-based, which is a user-based, which is a user-based, which is a user-based, and is a user-based, and is a user-based, and a user-based, and" +qasper,6a31db1aca57a818f36bba9002561724655372a7,"The dataset is 1. +The dataset is a large-scale, multi-lingual, multi-topic, multi-label classification, and multi-label classification task. It includes 10,0,0,00000000000,0000,0" +qasper,66c96c297c2cffdf5013bab5e95b59101cb38655,"The performance of BERT on the task +The performance of BERT on the task is the task is the most important. +The performance of BERT on the task is the task is the most important. +The performance of the task is the task. +The performance of the task is the task is the most" +qasper,b3307d5b68c57a074c483636affee41054be06d1,"The artifacts are the results of the results of the translation, the results of the translation, the results of the results of the results, the results of the results of the results, the results of the results of the results of the results of the results of the results of the results of the results of the results of" +qasper,3fff37b9f68697d080dbd9d9008a63907137644e,"The results of the proposed methods are compared to state-of-the-art +The results are compared to the state-of-art methods in this work. The results of the proposed methods are compared to the state-of-art. The results are compared to the state-of-art. The results of" +qasper,1269c5d8f61e821ee0029080c5ba2500421d5fa6,"In this paper, we use the data is the data-based on the data of the data of the data of the data. +In this paper, we use the data of the data of the data of the data of the data of the data of the data of the data of the data of the data of the" +qasper,4e1a67f8dc68b55a5ce18e6cd385ae9ab90d891f,"The Quora is a community of the most of the questions, the questions, the most of the questions, the most of the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the" +qasper,869feb7f47606105005efdb6bea1c549824baea0,"The dataset is a total of the dataset is 10,0000000000,0000,00000 tweets,0000000000 tweets,00000000000,00" +qasper,fff5c24dca92bc7d5435a2600e6764f039551787,"The StackExperformance +The StackExchange is aims to be a StackExchange is a StackExchange is a Stack of Stack of Stack, and the Stack is a Stack. +The Stack is a Stack of Stack of Stack. +The Stack is a Stack of Stack of Stacks. +The" +qasper,c49ee6ac4dc812ff84d255886fd5aff794f53c39,"The authors report on English data. +The authors do not report on English data. +The paper is based on English data." +qasper,149da739b1c19a157880d9d4827f0b692006aa2c,"The paper evaluates the performance of the best-of-scope +The best-performing models are BERT, BERT, BERT, out-scope, and TAB1, and REF, and REF2, and REF. +The best-based on-scope, and out-" +qasper,2236386729105f5cf42f73cc055ce3acdea2d452,"A large of the model to the data, the data, the data is +The model is a +The model is a +The model is a +The model is a +The model is a +The model is a +The model is a +The model is a +The model is a +The model is" +qasper,37a79be0148e1751ffb2daabe4c8ec6680036106,"The Chinese data used in this study is a large-scale text classification task is aims to analyze the user-based classification of Facebook posts on the Chinese language, including the user-generated content, such as posts, comments, posts, and the user-generated content, and the user's, and their lik" +qasper,55c8f7acbfd4f5cde634aaecd775b3bb32e9ffa3,"The metrics used in this study were the phoneme-to-to-to-proneme conversion of the model's performance of the model's performance. +The model's performance in the model's performance in the model's +The model's performance in the model's +" +qasper,8d4ac4afbf5b14f412171729ceb5e822afcfa3f4,"The most of the most of the most of the most of the most of the most of the most of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the" +qasper,051df74dc643498e95d16e58851701628fdfd43e,"The OSGiven the OS dataset was obtained from the OS was obtained from the Internet. +The OS was obtained from the Internet. +The OS was obtained from the Internet. +The OS was obtained from the online. +The OS was obtained from the online. +The OS was obtained from the online. +" +qasper,55569d0a4586d20c01268a80a7e31a17a18198e2,"The zero-shot setting +The zero-shot setting is aims to learn the model in zero-setting is a zero-shot setting, which is a zero-shot setting, which is a zero-shot setting, which is a zero-shot, which is a zero-shot, which is a zero-" +qasper,a1645d0ba50e4c29f0feb806521093e7b1459081,"The benchmark dataset is a large amount of tweets and spammers on the platform. +The benchmark dataset is a large amount of tweeting on Twitter, which is a large number of users on the platform. The number of spammers use. Unfortunately, spammers also use the tool to post to post" +qasper,e5c8e9e54e77960c8c26e8e238168a603fcdfcc6,"The proposed algorithm is a Bayesian classifier is a good model for text classification of text classification. The lexicon is trained to predict the text, the specific language. The lexicon is used to predict the language. The lexicon is trained to predict the specific language. The classifier is a good performance of text" +qasper,a5e49cdb91d9fd0ca625cc1ede236d3d4672403c,"The architecture of the span-detector is aims to read and comprehend and comprehend a given passage/paragraph and its corresponding questions is a task of natural language and important for natural language understanding, which is one of intelligent and applications in e" +qasper,219af68afeaecabdfd279f439f10ba7c231736e4,"The Japanese-to-Vietnamese is a language is a language spoken and written language spoken and written in Japan. It is spoken and written in Japan. It is one of the most spoken and written in Japan. The Japanese is a language of the most spoken and written in Japan. The Japanese is the most spoken" +qasper,e3a2d8886f03e78ed5e138df870f48635875727e,"The authors find irony is a kind of figurative language +The authors find irony is widely used on social media. Irony is a kind of language. Irony is defined as the meaning of the difference between the literal meaning of the intended meaning. Irony plays an important role in sentiment and opinion. Although" +qasper,a4a1fcef760b133e9aa876ac28145ad98a609927,"In this type of research, other hyperparameters, other than number of clusters are typically evaluated in this type of research. +In this type of research, other hyperparameters, other than number of clusters are typically evaluated in this type of research. +In this type of research, other hyperparameters, other than number of" +qasper,df5a4505edccc0ee11349ed6e7958cf6b84c9ed4,"The task of the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the task, the" +qasper,1dc2da5078a7e5ea82ccd1c90d81999a922bc9bf,"The authors conduct experiments on the tasks mentioned +The authors conduct experiments +The authors do not mention the tasks in the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention the tasks +The authors mention" +qasper,a87a009c242d57c51fc94fe312af5e02070f898b,"We use the modeling the model, we use the model, we're not, we're able to predictive of dogm, we're able to predictive of dogmat. +We're able to predictive. +We're able to predictive of dogmat, we're" +qasper,da55bd769721b878dd17f07f124a37a0a165db02,"Introduction: +Robotic Processing Automation (PA) is a type of hand-based on-handled activities +The kind of repetitive and time-consuming activities +The kind of repetitive and time-consuming activities +The kind of robotic (PA +Introduction: +Introduction: +The" +qasper,1591068b747c94f45b948e12edafe74b5e721047,"The Snapshots are a large dataset is a collection of 10,00000000000,0000,0000s of social media posts,00000000000000000000" +qasper,adbf33c6144b2f5c40d0c6a328a92687a476f371,"The NER, we propose a model that learns NER from text and images +The model is aims to learn from both text and images. +The model is aims to learn from both text and images. +The model is aims to learn from both text and images. +The model is to" +qasper,197b276d0610ebfacd57ab46b0b29f3033c96a40,"The methods used in this paper are the following: +Performance appraisal (PA) is an important process, particularly the process of modern organizations and the skills of their workforce. The process depends on the organization to evaluate every employee's performance. It appraisal process an organization to measure and PAREF" +qasper,15cdd9ea4bae8891c1652da2ed34c87bbbd0edb8,"The corpus is a collection of texts in this paper is from Twitter. +The texts in the corpus is from Twitter. +The corpus is from Twitter. +The corpus is from Twitter. +The corpus is from Twitter. +The corpus is from Twitter. +The corpus is from" +qasper,3cf1edfa6d53a236cf4258afd87c87c0a477e243,"The recipes are written in English. + + + + +The recipes are written in English." +qasper,dca86fbe1d57b44986055b282a03c15ef7882e51,"1. The ground truth for fake news is established by the meta-data +1 +1. +2. Characterizing Fake News in Twitter +2. +2. Political +3. Fake News in the analysis of Twitter +3. +4. Meta-data +5. +5. The" +qasper,d3dbb5c22ef204d85707d2d24284cc77fa816b6c,"The proposed model is a model is a simple, the state-of-of-the-the-art in the-art in terms of the task of reading and comprehension and answer. +The proposed model is one of natural language and applications in intelligent and applications. +Ing for machine learning and intellig" +qasper,e35c2fa99d5c84d8cb5d83fca2b434dcd83f3851,"The dataset was obtained from Twitter. +The dataset was obtained from Twitter. +The dataset was obtained from Twitter. +The dataset was obtained from Twitter. +The dataset was obtained from Twitter. +The dataset was obtained from Twitter. +The dataset was obtained from Twitter. +The dataset was obtained from Twitter. +" +qasper,9132d56e26844dc13b3355448d0f14b95bd2178a,"The results are not the same, the same as the same as the same as the baseline. +The results are not the same as the same as the baseline. +The results are the same as the baseline. +The results are the same as the baseline. +The results are the same as" +qasper,d9980676a83295dda37c20cfd5d58e574d0a4859,"The simulation techniques used in this study include a large-scale of the modeling, we are the most of the data, which is the best-domain and the best-of-domain, which is the best-domain, the best-domain, and the best-domain, and the best-domain, the" +qasper,2c7494d47b2a69f182e83455fe4c75ae3b2893e9,"In this section, we present the models are not. +In this section, we present the models are not. +In this section, we present the models are not. +In this section, the models are not. +In this section, the models are not. +In this section, the models are not" +qasper,2439b6b92d73f660fe6af8d24b7bbecf2b3a3d72,"The paper is aims to the most important aspect of the model, we propose a novel approach to the most important aspect of the paper. +The paper is the most important aspect of the model. +The paper is the most important aspect of the model. +The most important aspect of the model. +The most" +qasper,b6ae8e10c6a0d34c834f18f66ab730b670fb528c," + + +1. The opinions of Reddit is a model, we's +2 +2. +2. +3. +3. +4. +5. +5. +6. +6. +6. +7. +7. +8. +8. +9." +qasper,bf52c01bf82612d0c7bbf2e6a5bb2570c322936f,"The results of ROU-based summarization +The results of the results of the summaries generated text are not only the same as the same as the human-based on the gold-standard. +The ROu20, the effectiveness of summarization is the effectiveness of summarization is the same as" +qasper,f1e90a553a4185a4b0299bd179f4f156df798bce,"The baselines +The baselines are the results in Table 1. +The results in Table 10, 1, 2, 2, 3, 3, 4, 5, 6, 7, 8, 9, 10, 10" +qasper,ef7212075e80bf35b7889dc8dd52fcbae0d1400a,"The current ELS's are not sufficiently effective in summarization is not effective in the summarization because they are not able to handle the problem of the problem of disambiguation. +The problem of disambiguation. +The problem of disambiguation is not handled by the summarization. +The problem of disambiguation is" +qasper,32a3c248b928d4066ce00bbb0053534ee62596e7,"The CoLLORPHON 1 is aims to generate a word, the context of the lemma and context of the word, and inflection from the context of the given word. +The CoLLON 2 task is to estimate the morphological properties of a given word. +The CoN-form" +qasper,49764eee7fb523a6a28375cc699f5e0220b81766,"We use the modeling the modeling of the modeling of the modeling, we use of the modeling, we use the modeling, we use the modeling of the modeling, we use the modeling of the modeling, we use the modeling of the modeling, we use the model" +qasper,2cd37743bcc7ea3bd405ce6d91e79e5339d7642e,"The modeling the model is not only the model, the model is not the modeling of the data, the modeling, the modeling of the modeling, the modeling of the modeling, the modeling of the modeling, the modeling of the modeling, the modeling of the model" +qasper,127d5ddfabec5c58832e5865cbd8ed0978c25a13,"The word-level baseline is a word-level is a term used to describe the word-level of the word-level of the word-level of the word-level of the word-level of the word-level. It is the word-level of the word-level of the word-level of" +qasper,aa6d956c2860f58fc9baea74c353c9d985b05605,"The evaluation metrics used for the summarization task +The evaluation metrics used in this paper are not the F1. +The evaluation metrics used in this paper are not the following: +The evaluation metrics used in this paper are the following: +The evaluation metrics used in this paper are the following: +The evaluation metrics" +qasper,968b7c3553a668ba88da105eff067d57f393c63f,"1. +1. The study of tweets are fake news on Twitter +2. +2. The number of tweets containing fake +3. +3. The number of tweets +4. tweets +5. tweets +3. tweets +4. tweets +5. tweets" +qasper,5e324846a99a5573cd2e843d1657e87f4eb22fa6,"The focus of the word disambiguation (SD) task is aims, i. having several meanings. Two common examples are financial institution and bassis (fish or musical) but the word meanings a word of a group. may refer to a group (a word. The word to study or meetings deal" +qasper,1beb4a590fa6127a138f4ed1dd13d5d51cc96809,"The paper is a large-based on the dataset, we use the best-performance of the best-performing model, the best-performing model. +The best-performing model is the best-performing model. +The best-performing model is the best-performing model. +" +qasper,fa527becb8e2551f4fd2ae840dbd4a68971349e0,"The CoLLOR1–SIPH2 is aims to the CoN +The CoN-Normative +The CoNormative +The CoNormative +The CoNormative +The CoNormative +The CoNormative +The CoNormative +The CoN" +qasper,55139fcfe04ce90aad407e2e5a0067a45f31e07e,"The authors translated the lexicon-based model is a model is a lexicon-based model of the modeling. +The authors translated model is a lexicon-based model. +The authors translated model is a lexicon. +The authors translated model is a lexicon. +The authors translated model is a" +qasper,25c1c4a91f5dedd4e06d14121af3b5921db125e9,"A car-speak, we are the car-speak, we are the car-speak, and the car-speak, and the car-speak, and the car-speak. +The car-speak, and the car-speak. +The car-speak," +qasper,4e748cb2b5e74d905d9b24b53be6cfdf326e8054,"The study of the study of arc, the text, the text, the arc, the arc, and the arc, and the arc, and the arc, and the arc. +The arc, the arc, the arc, the arc, the arc, the arc, the arc, the arc, the arc," +qasper,a8f51b4e334a917702422782329d97304a2fe139,"1. +1. The threshold for determining that tweet has gone viral +2. +1. +1. Characterizing Fake News in Twitter by Meta-Data +2. +Julio LópezAxel Díaz-Mol" +qasper,a130306c6662ff489df13fb3f8faa7cba8c52a21,"The pooling function used in recurrent models are convolutional layers, including recurrent models, such as convolutional layers, including the recurrent layers, are used to the hidden layers, and the architecture of the sequence. +The pooling function of the modeling of the recurrent layers, the recurrent layers" +qasper,dc1cec824507fc85ac1ba87882fe1e422ff6cffb,"The questioning the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the questioning of the" +qasper,2c85865a65acd429508f50b5e4db9674813d67f2,"The data used in this study is the conversations and the conversations and the conversations. +The data is the conversations. +The data is the conversations. +The data is the data. +The data is the data. +The data is the data. +The data is the data. +" +qasper,307e8ab37b67202fe22aedd9a98d9d06aaa169c5,"The paper reports the performance of the baseline model on South African languages +The paper reports the performance of the baseline model on South African languages +The paper on South African languages +The paper on South African languages +The performance of the baseline on South African languages +The performance of South African languages +The performance" +qasper,111afb77cfbf4c98e0458606378fa63a0e965e36,"In this section, we propose a Bayesian modeling is not only a semantic role in the most of the most of the most of the most of the most of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the of the" +qasper,82642d3111287abf736b781043d49536fe48c350,"The dataset is a collection of tweets from Twitter, which is annotated with a variety of tweets that are classified as depression, depression, such as “depression, “feeling, “depressed”, “disappointment, “disappointment, “, “disappoint" +qasper,0d34c0812f1e69ea33f76ca8c24c23b0415ebc8d,"In this paper, we propose a novel approach to sentiment analysis of word2 +In this paper, we use the word2 +In this paper, we propose a novel approach to sentiment analysis. +In this paper, we propose a novel approach to sentiment analysis. +In this paper, we propose a novel approach to" +qasper,0f12dc077fe8e5b95ca9163cea1dd17195c96929,"The criteria for selecting the 8 sentences are not only the most important factors that are the following: +1. +1. The quality of the model's performance +2. +2. The model's ability to generalizability +3. +3. The model's ability to generalizability" +qasper,63a1cbe66fd58ff0ead895a8bac1198c38c008aa,"The evaluation of captioning models are evaluated on the captioning models are evaluated on the image, the caption and reference, the captioning of the model is a model and reference. +The caption is a model and the reference. +The caption is a model and the reference. +The" +qasper,bb3267c3f0a12d8014d51105de5d81686afe5f1b,"The following are the following datasets: +The following are used in this paper: +1. +1. ENTit is a large-B0 +2 +2. The 1. The 1000000000000000000-1-" +qasper,5fda8539a97828e188ba26aad5cda1b9dd642bc8,"The model is a model is a neural network of the model of the model. +The model is a neural network of the model. +The model is a neural network of the model. +The model is a neural network of the model. +The model is a neural network of the model. +The model is" +qasper,f225a9f923e4cdd836dd8fe097848da06ec3e0cc,"The paper is performed on the SQuAD3 dataset. +The paper is based on the Quora corpus of Quora, which is a large-scale, which is a question-answer and answer-answer pairs, and the Quora, and Quora, and Quora, and Quora, Qu" +qasper,cc354c952b5aaed2d4d1e932175e008ff2d801dd,"The results of the results of the analysis, we found that the difference between the system, the number of the number of submissions, the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the number of the" +qasper,2c7e94a65f5f532aa31d3e538dcab0468a43b264,"The dataset was annotated by a large number of the dataset +The dataset was annotated by a large number of people from the Annotated by a large number of people. +The dataset was annotated by a large number of people. +The dataset was annotated by a large number of people. +The" +qasper,de3b1145cb4111ea2d4e113f816b537d052d9814,"The baseline is a dataset of tweets. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is a dataset of emotions. +The dataset is" +qasper,beac555c4aea76c88f19db7cc901fa638765c250,"The attention is a modeling the model of attention +The model of attention +The model of attention +The model of attention +The model of attention +The model of attention +The model of attention +The model of attention +The model of attention +The model of attention +The model of attention +The model of" +qasper,ee417fea65f9b1029455797671da0840c8c1abbe,"The system is a system is a type of hand-based on-the-the-the-shelf +The system is a-based on-the-the- +The system is a-the-the- +The system is a-the-the-the- +The system is-the-" +qasper,d53299fac8c94bd0179968eb868506124af407d1,"The car-speak, we are the car-speak of the car-based on the car. +The car-based on the car-based on the car. +The car-based on the car-based on the car. +The car-based on the car-based on the car." +qasper,ffa7f91d6406da11ddf415ef094aaf28f3c3872d,"The table-generation is a new metric is a metric that is a metric that is not a measure of the quality of the text-based on the text. The best way to compare the text. The best way to compare the text-based model is the model. The best way to compare the text is the" +qasper,277a7e916e65dfefd44d2d05774f95257ac946ae,"The baselines are the baselines are the following: +The baselines are the following: +The baselines are the following: +The baselines are the following: +The baselines are the following: +The baselines are the following: +The baselines are the following: +The baselines are" +qasper,f7ed3b9ed469ed34f46acde86b8a066c52ecf430,"The most popular word2-level models use the word2-level models are the word2 context and context-based models, which are the most common word2, which are the most widely used in N-based models, such as PMI, PMI, PMI, GloV, and PMI," +qasper,9c44df7503720709eac933a15569e5761b378046,"The word embeddings are used in NLP systems +The word representations are used in modern NLP systems for parsing, classification, sentiment, and classification, and many of which embedd embeddings are derived from the word-based on the context, which word embedd, which are derived from the word," +qasper,08b57deb237f15061e4029b6718f1393fa26acce,"The crowdworkers are individuals who are individuals who use social media, such as Twitter to be the political tweets, and are to be the data of the data of the data of the data. They are the data of the data of the data of the data of the data of the data of the data of the" +qasper,d3dbb5c22ef204d85707d2d24284cc77fa816b6c,"The proposed model is a model is a simple, the state-of-of-the-the-art in the-art in terms of the task of reading and comprehension and answer. +The proposed model is one of natural language and applications in intelligent and applications. +Ing for machine learning and intellig" +qasper,d9b6c61fc6d29ad399d27b931b6cb7b1117b314a,"In this section, we use a questioning is a question. +In this paper, we use a question. +In this paper, we use a question. +In this paper, we use a question. +In this paper, we use a question +In this paper, we use a question +In this" +qasper,52f8a3e3cd5d42126b5307adc740b71510a6bdf5,"A large of the human knowledge is recorded through text. That is a text. Why a system to automatically infer from text without any structured data has become a major task to automatically +Answer a given data has become a proxy for information from any +Introduction a document has a task a given +The task a of text" +qasper,f9f59c171531c452bd2767dc332dc74cadee5120,"The data comes from 1. +The data is from 10 +The data is from 1 +The data is from 1 +The data is from 1 +The data is from 1 +The data is from 1 +The data is from 1 +The data is from 1" +qasper,4625cfba3083346a96e573af5464bc26c34ec943,"The paper is not provided. +The paper is not provided." +qasper,ddf5e1f600b9ce2e8f63213982ef4209bab01fd8,"The paper uses the Spoken-S, the Spoken-S, QuAD, the Spoken-S, and Text-S, and Text-S, and Text-S. +The Spoken-S. +The Spoken-S +The Spoken-S +The Spoken-S" +qasper,71ba1b09bb03f5977d790d91702481cc406b3767,"The data: The data is a modeling. +The data is a model. +The data is a model. +The data is a. +The data is a. +The data is a. +The data is a. +The data is a. +The data is a. +The data is" +qasper,81d193672090295e687bc4f4ac1b7a9c76ea35df,"The baseline method used in this study is a supervised machine learning. +The baseline method is sentiment analysis." +qasper,22744c3bc68f120669fc69490f8e539b09e34b94,"The findings of this paper are not generalizable to a general-purpose. +The findings of this paper are not generalizable to a general-purpose. +The findings of this paper are generalizable. +The findings of this paper are generalizable. +The findings of this paper" +qasper,7705dd04acedaefee30d8b2c9978537afb2040dc,"The modeling is a question-level questions +The model is a question-answering +The model is a question-answering +The model is a question-answering +The model is a question-answering +The model is a question-answering +The model is a question-answering +" +qasper,9ca447c8959a693a3f7bdd0a2c516f4b86f95718,"The tweets were annotated with the tweets were annotated with the tweets in the tweet id, the corresponding data, the target, the target, the target, and the target, and the target, and the target. +The tweet. +The tweet, the target, the target," +qasper,62ea141d0fb342dfb97c69b49d1c978665b93b3c,"The modeling the model is not only the same, the same as the model, the model is not the same as the model, the model is not the same as the model. +The model is not the same as the model is not the model. +The model is not the same as the model is not" +qasper,e025061e199b121f2ac8f3d9637d9bf987d65cd5,"Performance appraisal (PA) is important for modern organizations and the process of the process of the organization. +Performance is an important process of the organization. +Performance is an evaluation of the employee. +Performance is an important process of the organization. +Performance is an important process of the" +qasper,a712718e6596ba946f29a99838d82f95b9ebb1ce,"The paper is an important topic-based on the model, we propose a model for the abbreviation task of the modeling. We have a model that is a new-based on the number of the number of the data. We have a model-based on the number of the number of the number of the number" +qasper,01e2d10178347d177519f792f86f25575106ddc7,"The system is evaluated on the Tables are typically formulated as three-step, the topic identification, where speech is performed by three-labeled documents a set of speech, where the predefined set of labels. First, a topic is formulated as three-step, the topic is tokenized by a process" +qasper,891c2001d6baaaf0da4e65b647402acac621a7d2,"The application of deep learning is aims to represent words in a continuous space +The representation of words in a low-dimensional space. +The representation of words is aims to be a static, typically a vector, and the most words are trained on the contextual, and the contextual models are trained on the" +qasper,d28d86524292506d4b24ae2d486725a6d57a3db3,"In this paper, we propose a novel model, we design a two-stage summarization model, which is the highest performing model. +In this paper, we propose a novel approach to the best performing model. +In this paper, we propose a novel approach to the best performing model. +In this paper," +qasper,ad08b215dca538930ef1f50b4e49cd25527028ad,"The paper is aims to be the most important baseline. +The paper is aims to be the most important baseline. +The paper is aims to be the most important baseline. +The paper is the most important baseline. +The paper is the most important baseline. +The paper" +qasper,05887a8466e0a2f0df4d6a5ffc5815acd7d9066a,"The best results are obtained from the classifiers are the best results of the best results in the best results in the best results. +The best results are the best results are the best results. +The best results are the best results. +The best results are the best results. +The best results are the best" +qasper,5bc1dc6ebcb88fd0310b21d2a74939e35a4c1a11,"The languages used in the experiment are English, Spanish, Finnish, English, French, German, and Spanish, and Chinese. +The languages, and Chinese. +The languages used in the experiment are English, French, Spanish, German, and Chinese, and Chinese. +The languages used in the experiment are English" +qasper,6bf5620f295b5243230bc97b340fae6e92304595,"The baseline model is a supervised model +The baseline model is a supervised model. +The baseline model is a supervised model. +The baseline model is a supervised model. +The baseline model is a supervised model. +The baseline model is a supervised model." +qasper,5cb610d3d5d7d447b4cd5736d6a7d8262140af58,"This paper describes our approach and results for Task 2 of CoLL" +qasper,133eb4aa4394758be5f41744c60c99901b2bc01c,"The results of the results of the paper are not provided in the paper are not provided. +The paper is not provided. +The paper is not provided. +The paper is not provided. +The paper is not provided. +The paper is not provided. +The paper is not provided. +The paper is" +qasper,113d791df6fcfc9cecfb7b1bebaf32cc2e4402ab,"The compactness is a measure of a measure of how well a model performs in the model's ability to generalize the data. +The model's performance in a language. The model's performance is the better the better the more the more the better the better the model is, the more the better the" diff --git a/evaluation/zeroscrolls/run.sh b/evaluation/zeroscrolls/run.sh new file mode 100644 index 0000000..4786e1e --- /dev/null +++ b/evaluation/zeroscrolls/run.sh @@ -0,0 +1,38 @@ +#scrolls (200 validation samples) +# python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_32K_interpolate" --flash --dataset qasper --ratio 16 +# python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_7b_16K" --flash --dataset qasper +# python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_13b_16K" --flash --dataset qasper +# +# python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_32K_interpolate" --flash --dataset narrative_qa --ratio 16 +# python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_7b_16K" --flash --dataset narrative_qa +# python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_13b_16K" --flash --dataset narrative_qa + +#python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-7b-v1.3" --flash --dataset qasper +#python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-7b-v1.3" --flash --dataset narrative_qa +# +#python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-13b-v1.3" --flash --dataset qasper +#python eval.py --dataset-version "tau/scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-13b-v1.3" --flash --dataset narrative_qa +# + +# zero scrolls +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_32K_interpolate" --flash --dataset qasper +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_7b_16K" --flash --dataset qasper +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_13b_16K" --flash --dataset qasper +# +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_32K_interpolate" --flash --dataset narrative_qa +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_7b_16K" --flash --dataset narrative_qa +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/dacheng-data/longchat_13b_16K" --flash --dataset narrative_qa +# +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-7b-v1.3" --flash --dataset qasper +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-7b-v1.3" --flash --dataset narrative_qa + +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-13b-v1.3" --flash --dataset qasper +# python eval.py --dataset-version "tau/zero_scrolls" --model-name-or-path "/home/haozhang/LongChat/data/vicuna-13b-v1.3" --flash --dataset narrative_qa + + +# GPT-3.5/GPT-4 +python eval_api.py --dataset-version "tau/scrolls" --model-name-or-path "gpt-4" --dataset qasper +python eval_api.py --dataset-version "tau/scrolls" --model-name-or-path "gpt-4" --dataset narrative_qa + +#python eval_api.py --dataset-version "tau/zero_scrolls" --model-name-or-path "gpt-4" --dataset qasper +#python eval_api.py --dataset-version "tau/zero_scrolls" --model-name-or-path "gpt-4" --dataset narrative_qa diff --git a/longchat/train/monkey_patch/llama_interpolate_monkey_patch.py b/longchat/train/monkey_patch/llama_interpolate_monkey_patch.py index 5a62dd3..431be36 100644 --- a/longchat/train/monkey_patch/llama_interpolate_monkey_patch.py +++ b/longchat/train/monkey_patch/llama_interpolate_monkey_patch.py @@ -15,7 +15,7 @@ def __init__(self, dim, ratio, max_position_embeddings=2048, base=10000, device= # Build here to make `torch.jit.trace` work. self.ratio = ratio max_position_embeddings *= ratio - rank0_print(f"building interpolation to {max_position_embeddings}") + # rank0_print(f"building interpolation to {max_position_embeddings}") self.max_seq_len_cached = max_position_embeddings t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype) / ratio freqs = torch.einsum("i,j->ij", t, self.inv_freq) diff --git a/utils.py b/utils.py index b2c904b..3d6d512 100644 --- a/utils.py +++ b/utils.py @@ -19,14 +19,18 @@ def load_model(path, dtype=torch.bfloat16, device="cuda", num_gpus=1): kwargs = {"torch_dtype": torch.bfloat16} if num_gpus != 1: kwargs["device_map"] = "auto" - kwargs["device_map"] = "sequential" # This is important for not the same VRAM sizes + #kwargs["device_map"] = "sequential" # This is important for not the same VRAM sizes # Hard code for A100s - available_gpu_memory = [2.5] * num_gpus + available_gpu_memory = [16] * num_gpus kwargs["max_memory"] = { i: str(int(available_gpu_memory[i] * 0.85)) + "GiB" for i in range(num_gpus) } - model = AutoModelForCausalLM.from_pretrained(path, **kwargs).cuda() + print(kwargs) + exit() + print(num_gpus) + exit() + model = AutoModelForCausalLM.from_pretrained(path, **kwargs) tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False) return model, tokenizer