diff --git a/README.md b/README.md index eeeaca3..7657cea 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,13 @@ +# PairFact source code for ACL submission + +To run: + + pip3 install -r requirements.txt + python3 eval.py + +The results will be in the `results/` folder. + + # An evaluation framework for text-to-text generation tasks EvalBase allow easy testing of new text-to-text generation metrics. A computerprogram or a machine learning model that can generate text from text is called a **system**. Such text-to-text generation tasks can be __summarization__, __translation__, or even __question answering__. Correspondingly, the systems for such tasks are called **summarizers**, **translators**, and **answer**. diff --git a/bertscore_sentence/eval.py b/bertscore_sentence/eval.py new file mode 100644 index 0000000..b60d2e3 --- /dev/null +++ b/bertscore_sentence/eval.py @@ -0,0 +1,83 @@ +import numpy as np +import torch +from tqdm import tqdm +import functools +import dar_type +from text_preprocess import list_segmentation +import warnings + + + +def cos_sim_mat_f(cand_segments: dar_type.TextSegments, ref_segments: dar_type.TextSegments, embedder: dar_type.Embedder) -> np.ndarray: + def bert_encode(piece_segments: dar_type.TextSegments): + sent_emb_list = list() + for sent in piece_segments: + with torch.no_grad(): + sent_emb_list.append(embedder.encode(sent, convert_to_numpy=True)) + return np.stack(sent_emb_list, axis=0) + + # def bert_encode_multiprocess(piece_segments: dar_type.TextSegments): + # pool = embedder.start_multi_process_pool() + # sent_emb = embedder.encode_multi_process(sentences=piece_segments, pool=pool, batch_size=8) + # embedder.stop_multi_process_pool(pool) + # return sent_emb + + ref_sent_emb = bert_encode(ref_segments) + cand_sent_emb = bert_encode(cand_segments) + numerators = np.inner(ref_sent_emb, cand_sent_emb) + ref_sent_emb_norms = np.linalg.norm(ref_sent_emb, axis=1) + cand_sent_emb_norms = np.linalg.norm(cand_sent_emb, axis=1) + denominators = np.outer(ref_sent_emb_norms, cand_sent_emb_norms) + sim_mat = np.divide(numerators, denominators) + return sim_mat + + +def score_np(predictions: dar_type.TextList, references: dar_type.TextList, sim_mat_f: dar_type.SimilarityMatrixFunc) -> np.ndarray: + cands, refs = list_segmentation(predictions), list_segmentation(references) + all_scores = np.empty((len(cands), 3)) + + # for index in tqdm(range(len(cands)), desc="bertscore-sentence {}".format(sim_mat_f.__name__), leave=False): # all pieces, len(cands) == len(refs) + for index in range(len(cands)): + sim_mat = sim_mat_f(cand_segments=cands[index], ref_segments=refs[index]) + + def sum_max(is_r: bool) -> float: + if is_r: + return np.sum(np.max(sim_mat, axis=1)) + else: + return np.sum(np.max(sim_mat, axis=0)) # equals to np.sum(np.max(sim_mat.T, axis=1)) + + has_empty = False + if len(refs[index]) == 0: + warnings.warn("empty ref str", dar_type.DocWarning) + has_empty = True + if len(cands[index]) == 0: + warnings.warn("empty cand str", dar_type.DocWarning) + has_empty = True + if has_empty: + warnings.warn("detail: [ref] {}; [cand] {}".format(refs[index], cands[index]), dar_type.DocWarning) + + if not has_empty: + R = (1 / len(refs[index])) * sum_max(True) + P = (1 / len(cands[index])) * sum_max(False) + F = 2 * ((P * R) / (P + R)) + all_scores[index, :] = np.array([P, R, F]) + else: + all_scores[index, :] = np.zeros((3,)) + + return all_scores + + +def compute(predictions: dar_type.TextList, references: dar_type.TextList, sim_mat_f: dar_type.SimilarityMatrixFunc) -> dar_type.MetricScoreDict: + cands, refs = predictions, references # simple renaming + score_arr = score_np(predictions=cands, references=refs, sim_mat_f=sim_mat_f) + return { + "P": score_arr[:, 0].tolist(), + "R": score_arr[:, 1].tolist(), + "F": score_arr[:, 2].tolist() + } + + +def compute_cos(predictions: dar_type.TextList, references: dar_type.TextList, embedder: dar_type.Embedder) -> dar_type.MetricScoreDict: + cos_sim_mat_f_with_embedder: dar_type.SimilarityMatrixFunc = functools.partial(cos_sim_mat_f, embedder=embedder) + cos_sim_mat_f_with_embedder.__name__ = " ".join(["cos", embedder.__name__]) + return compute(predictions=predictions, references=references, sim_mat_f=cos_sim_mat_f_with_embedder) diff --git a/dar_type.py b/dar_type.py new file mode 100644 index 0000000..37a1cec --- /dev/null +++ b/dar_type.py @@ -0,0 +1,60 @@ +import typing +import numpy as np +import sentence_transformers +import transformers +from datasets.arrow_dataset import Dataset + + +MetricScoreList = typing.List[float] +MetricScoreDict = typing.Dict[str, MetricScoreList] +TextList = typing.List[str] +TextListSegments = typing.List[typing.List[str]] +TextSegments = typing.List[str] + + +class Embedder(sentence_transformers.SentenceTransformer): + __name__: str + + +class SimilarityMatrixFunc(typing.Protocol): + __name__: str + + def __call__(self, cand_segments: TextSegments, ref_segments: TextSegments) -> np.ndarray: + ... + + +Pipeline = transformers.Pipeline + + +class PipelinesList(): + __name__: str + pipelines: typing.List[Pipeline] + + +PipelinesDict = typing.Dict[str, PipelinesList] + + +class MetricComputeFunc(typing.Protocol): + def __call__(self, predictions: TextList, references: TextList) -> MetricScoreDict: + ... + + +class DocWarning(Warning): + """ + Warning raised when the document is malformed + e.g., an empty document, which is meaningless to metrics + """ + + +class MNLICategory(typing.TypedDict): + label: str + score: float + + +MNLICategories = typing.List[MNLICategory] + + +MNLISimilarityExpression = typing.Callable[[MNLICategories], float] + + +SummaryLengthExpression = typing.Callable[[typing.Optional[Dataset]], typing.Optional[int]] diff --git a/env.py b/env.py new file mode 100644 index 0000000..f0755f9 --- /dev/null +++ b/env.py @@ -0,0 +1,69 @@ +# Group 2: bertscore-sentence (cos, mnli) + +# from env_root import * + +### HARDWARE ### + +import os +os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:512" +os.environ["CUDA_VISIBLE_DEVICES"] = "0,1" + +# fix: GPU OOM (TF exhausts GPU memory, crashing PyTorch) +import tensorflow as tf +gpus = tf.config.experimental.list_physical_devices("GPU") +for gpu in gpus: + tf.config.experimental.set_memory_growth(gpu, True) + +### DATASETS ### + +import sys +# sys.path.append("/home/turx/EvalBase") +# sys.path.append("../evalbase") +import evalbase +for ds_name in evalbase.datasets: + evalbase.datasets[ds_name]["approaches"] = ["new"] +# from evalbase import newsroom, realsumm, summeval + +### GLOBAL VARS ### + +import torch + +path = os.path.dirname(os.path.abspath(__file__)) +n_gpu = torch.cuda.device_count() + +### LIBRARY VARS ### + +import datasets +datasets.disable_progress_bar() + +### METRICS ### + +corr_metrics = ["pearsonr", "kendalltau", "spearmanr"] + + +### MODELS ### + +import dar_type +import sentence_transformers + +from mnli.classifiers import mnli_classifiers +sent_embedder_mpnet: dar_type.Embedder = sentence_transformers.SentenceTransformer("all-mpnet-base-v2") +sent_embedder_mpnet.__name__ = "all-mpnet-base-v2" +sent_embedder_roberta: dar_type.Embedder = sentence_transformers.SentenceTransformer("all-roberta-large-v1") +sent_embedder_roberta.__name__ = "all-roberta-large-v1" + +### METRICS ### + +import functools +import bertscore_sentence.eval as bertscore_sentence +import mnli.eval +import mnli.sim_expr + +metrics = { + "bertscore-sentence-cos-mpnet": functools.partial(bertscore_sentence.compute_cos, embedder=sent_embedder_mpnet), + "bertscore-sentence-cos-roberta": functools.partial(bertscore_sentence.compute_cos, embedder=sent_embedder_roberta), +} + +for mnli_name in ["roberta", "bart", "deberta"]: + for mnli_expr in [mnli.sim_expr.not_neutral, mnli.sim_expr.entail_only, mnli.sim_expr.entail_contradict]: + metrics["bertscore-sentence-mnli-{}-{}".format(mnli_name, mnli_expr.__name__)] = functools.partial(mnli.eval.bertscore_sentence_compute, classifiers=mnli_classifiers[mnli_name], expr=mnli_expr) diff --git a/eval.py b/eval.py new file mode 100644 index 0000000..e88b62c --- /dev/null +++ b/eval.py @@ -0,0 +1,28 @@ +import os +import warnings +import pandas +import sys +import datetime + +path = os.path.dirname(os.path.abspath(__file__)) +result_path = os.path.join(path, "results") + + +def create_result_path(): + if not os.path.exists(result_path): + os.makedirs(result_path) + +import factcc + +def eval(): + create_result_path() + print("==== env.factcc.qags_main(), size: 235") + factcc.qags_main() + print("==== env.factcc.frank_main(): size: 1250") + factcc.frank_main() + print("==== env.factcc.factCC_main(): size: large") + factcc.factCC_main() + + +if __name__ == '__main__': + eval() \ No newline at end of file diff --git a/eval_utils.py b/eval_utils.py index b792ff3..7758ec5 100644 --- a/eval_utils.py +++ b/eval_utils.py @@ -242,6 +242,13 @@ def write_results( if detail_df is None: detail_df = simple_df + parent_dir = os.path.dirname(simple_path) + if not os.path.exists(parent_dir): + os.makedirs(parent_dir) + parent_dir = os.path.dirname(detail_path) + if not os.path.exists(parent_dir): + os.makedirs(parent_dir) + with pandas.option_context('display.max_rows', None, 'display.max_columns', None, 'display.precision', 3, diff --git a/evalbase.py b/evalbase.py index 90f858e..450fae4 100644 --- a/evalbase.py +++ b/evalbase.py @@ -2,6 +2,7 @@ import newsroom import realsumm import summeval +import factcc path = os.path.dirname(os.path.abspath(__file__)) diff --git a/factcc.py b/factcc.py new file mode 100644 index 0000000..3518b86 --- /dev/null +++ b/factcc.py @@ -0,0 +1,241 @@ +import eval_utils +import numpy as np +import os +import pandas +import json +from tqdm import tqdm + +import json +import typing + +import pandas + +import env +import evalbase + + +# factCC + +def process_factcc_line(data, i): + fact_label = float(data["label"] == "CORRECT") + sample = {"doc": data["text"], "sum": data["claim"], + "human": fact_label, "id": i, "id0": "id0"} + return sample + + +def load_factcc(filepath, limit=None): + lines = [] + with open(filepath, "r", encoding="UTF-8") as f: + for i, line in enumerate(f): + data = json.loads(line) + processed_data = process_factcc_line(data, i) + lines.append(processed_data) + df = pandas.DataFrame(lines) + return df + + +# Frank + + +def process_frank_line(line): + _doc = line["article"] + _sum = line["summary"] + sample = {"doc": _doc, "sum": _sum, "ref": line["reference"], + "human": line["Factuality"], "id": line["hash"], "id0": "id0"} + + return sample + + +def load_frank(data_file, annotation_file): + """Return [cnndm,xsum] variants dataframes. + """ + with open(data_file, "r", encoding="UTF-8") as f: + data = json.load(f) + with open(annotation_file, "r", encoding="UTF-8") as f: + annot = json.load(f) + + data_dict = {} + + for sample in data: + data_dict[(sample['hash'], sample['model_name'])] = sample + for sample in annot: + data_dict[(sample['hash'], sample['model_name'])].update(sample) + + cnndm = [] + xsum = [] + + for line in tqdm(data_dict.values()): + processed_line = process_frank_line(line) + if line["dataset"] == "cnndm": + cnndm.append(processed_line) + elif line["dataset"] == "bbc": + xsum.append(processed_line) + + return [pandas.DataFrame(cnndm), pandas.DataFrame(xsum)] + +# qags + + +def process_qags_line(line, i): + _doc = line["article"] + + sum_texts = [] + facts = [] + for sent in line["summary_sentences"]: + sum_texts.append(sent["sentence"]) + response = [worker["response"] == + "yes" for worker in sent["responses"]] + facts.extend(response) + + _sum = " ".join(sum_texts) + sample = {"doc": _doc, "sum": _sum, + "human": np.mean(facts), "id": i, "id0": "id0"} + + return sample + + +def load_qags(filepath): + lst = [] + with open(filepath, "r", encoding="UTF-8") as f: + for i, line in enumerate(f): + data = json.loads(line) + row = process_qags_line(data, i) + lst.append(row) + df = pandas.DataFrame(lst) + return df + + +def eval_summary_level(dataset_df, config): + print(f"{config['name']} Summary-Level") + # check if results already exist + if os.path.exists(f"results/{config['name']}_summary.json"): + print("already existed, return") + return + corr_df = eval_utils.eval_summary_level( + dataset_name=config['name'], + dataset_df=dataset_df, + exp_approaches=config["approaches"], + exp_models=env.metrics, + corr_metrics=env.corr_metrics, + document_column=config["document_column"], + docID_column=config["docID_column"], + system_summary_column=config["system_summary_column"], + reference_summary_column=config["reference_summary_column"], + human_metrics=config["human_metrics"], + # pre_calculated_metrics=precalc_metrics, + debug=False + ) + eval_utils.write_results( + simple_df=corr_df['average'], + detail_df=corr_df, + simple_path=f"results/{config['name']}_summary.txt", + detail_path=f"results/{config['name']}_summary.json" + ) + + +def eval_system_level(dataset_df, config): + print(f"{config['name']} System-Level") + # check if results already exist + if os.path.exists(f"results/{config['name']}_system.json"): + print("already existed, return") + return + corr_df = eval_utils.eval_system_level( + dataset_name=config['name'], + dataset_df=dataset_df, + exp_approaches=config["approaches"], + exp_models=env.metrics, + corr_metrics=env.corr_metrics, + document_column=config["document_column"], + docID_column=config["docID_column"], + system_summary_column=config["system_summary_column"], + reference_summary_column=config["reference_summary_column"], + human_metrics=config["human_metrics"], + # pre_calculated_metrics=precalc_metrics, + debug=False + ) + eval_utils.write_results( + simple_df=corr_df, + simple_path=f"results/{config['name']}_system.txt", + detail_path=f"results/{config['name']}_system.json" + ) + + print("End") + + +#################### +# The main functions + +def factCC_main(): + DATA_ROOT = "/home/hebi/git/factRel/data/dataset/" + FACTCC_PATH = os.path.join( + DATA_ROOT, "factCC/data_pairing/data/generated_data/data-clipped") + + FACTCC_SPLIT = { + "train": "data-train.jsonl", + "dev": "data-dev.jsonl", + "test": "data-test.jsonl" + } + config = { + 'human_metrics': ['human'], + 'docID_column': 'id', + 'document_column': 'doc', + 'system_summary_column': 'sum', + # FIXME only one summary is available + 'reference_summary_column': 'sum', + 'approaches': ['new'], + } + # TODO load the other splits. + filepath = os.path.join(FACTCC_PATH, FACTCC_SPLIT["test"]) + df = load_factcc(filepath) + eval_system_level(df, {**config, "name": "factcc"}) + eval_summary_level( + df, {**config, "name": "factcc-pooled", "docID_column": "id0"}) + + +def frank_main(): + DATA_ROOT = "/home/hebi/git/factRel/data/dataset/" + FRANK_FOLDER = os.path.join(DATA_ROOT, 'frank/data') + FRANK_DATA = 'benchmark_data.json' + FRANK_ANNOTATION = 'human_annotations.json' + + config = { + 'human_metrics': ['human'], + 'docID_column': 'id', + 'document_column': 'doc', + 'system_summary_column': 'sum', + 'reference_summary_column': 'ref', + 'approaches': ['new'], + } + cnndm, xsum = load_frank(os.path.join(FRANK_FOLDER, FRANK_DATA), + os.path.join(FRANK_FOLDER, FRANK_ANNOTATION)) + # System level + eval_system_level(cnndm, {**config, 'name': 'frank-cnndm'}) + eval_system_level(xsum, {**config, 'name': 'frank-xsum'}) + # Summary level + eval_summary_level(cnndm, {**config, 'name': 'frank-cnndm'}) + eval_summary_level(xsum, {**config, 'name': 'frank-xsum'}) + + +def qags_main(): + # TODO parameterize the path + DATA_ROOT = "/home/hebi/git/factRel/data/dataset/" + QGAS_FOLDER = os.path.join(DATA_ROOT, "qags/data") + QGAS_FILES = ["mturk_cnndm.jsonl", "mturk_xsum.jsonl"] + config = { + 'human_metrics': ['human'], + 'docID_column': 'id', + 'document_column': 'doc', + 'system_summary_column': 'sum', + # FIXME only one summary is available + 'reference_summary_column': 'sum', + 'approaches': ['new'], + } + cnndm = load_qags(os.path.join(QGAS_FOLDER, "mturk_cnndm.jsonl")) + xsum = load_qags(os.path.join(QGAS_FOLDER, "mturk_xsum.jsonl")) + # DEBUG using part of the data + eval_system_level(cnndm, {**config, "name": "qags-cnndm"}) + eval_system_level(xsum, {**config, "name": "qags-xsum"}) + eval_summary_level( + cnndm, {**config, "name": "qags-cnndm-pooled", "docID_column": "id0"}) + eval_summary_level( + xsum, {**config, "name": "qags-xsum-pooled", "docID_column": "id0"}) diff --git a/mnli/classifiers.py b/mnli/classifiers.py new file mode 100644 index 0000000..39e6436 --- /dev/null +++ b/mnli/classifiers.py @@ -0,0 +1,22 @@ +import sys +from os import path +file_path = path.abspath(__file__) +sys.path.append(path.dirname(path.dirname(file_path))) + +from transformers import pipeline +import env +import dar_type + + +mnli_models = { + "roberta": "roberta-large-mnli", + "bart": "facebook/bart-large-mnli", + "deberta": "microsoft/deberta-xlarge-mnli" +} +mnli_classifiers: dar_type.PipelinesDict = dict() +for model_name, model in mnli_models.items(): + mnli_model_classifiers: dar_type.PipelinesList = list() + classifiers = dar_type.PipelinesList() + classifiers.pipelines = [pipeline("text-classification", model=model, top_k=None, device=device, truncation=True) for device in range(env.n_gpu)] + classifiers.__name__ = model_name + mnli_classifiers[model_name] = classifiers diff --git a/mnli/eval.py b/mnli/eval.py new file mode 100644 index 0000000..7c0f5cf --- /dev/null +++ b/mnli/eval.py @@ -0,0 +1,23 @@ +import sys +from os import path +file_path = path.abspath(__file__) +sys.path.append(path.dirname(path.dirname(file_path))) + +from bertscore_sentence import eval +import numpy as np +from mnli.sim import similarity +import functools +import dar_type + + +def mnli_sim_mat(cand_segments: dar_type.TextSegments, ref_segments: dar_type.TextSegments, classifiers: dar_type.PipelinesList, expr: dar_type.MNLISimilarityExpression) -> np.ndarray: + sent_pairs = ["[SEP]".join([x, y]) for x in ref_segments for y in cand_segments] + sim_mat = np.empty((len(ref_segments), len(cand_segments))) + sim_mat.flat = np.array(similarity(sent_pairs, classifiers, expr)) + return sim_mat + + +def bertscore_sentence_compute(predictions: dar_type.TextList, references: dar_type.TextList, classifiers: dar_type.PipelinesList, expr: dar_type.MNLISimilarityExpression) -> dar_type.MetricScoreDict: + sim_mat_f: dar_type.SimilarityMatrixFunc = functools.partial(mnli_sim_mat, classifiers=classifiers, expr=expr) + sim_mat_f.__name__ = " ".join(["mnli", classifiers.__name__]) + return eval.compute(predictions=predictions, references=references, sim_mat_f=sim_mat_f) diff --git a/mnli/facebook-roberta-reproduction.py b/mnli/facebook-roberta-reproduction.py new file mode 100644 index 0000000..f622944 --- /dev/null +++ b/mnli/facebook-roberta-reproduction.py @@ -0,0 +1,31 @@ +# https://github.com/facebookresearch/fairseq/tree/main/examples/roberta + +import sim +import sim_expr +import env +import dar_type + + +def label_row(row: str, classifier: dar_type.Pipeline) -> str: + classes = classifier(row) + return classes[0][0]["label"] + + +if __name__ == "__main__": + batch_of_pairs = [ + ['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'], + ['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'], + ['potatoes are awesome.', 'I like to run.'], + ['Mars is very far from earth.', 'Mars is very close.'], + ] + sent_pairs = ["[SEP]".join([row[0], row[1]]) for row in batch_of_pairs] + sim_results = sim.similarity( + sent_pairs=sent_pairs, + classifiers=env.mnli_classifiers["roberta"], + expr=sim_expr.not_neutral + ) + print(len(sim_results)) + print(sim_results) + label_results = [label_row(row, classifier=env.mnli_classifiers["roberta"].pipelines[0]) for row in sent_pairs] + print(len(label_results)) + print(label_results) diff --git a/mnli/sim.py b/mnli/sim.py new file mode 100644 index 0000000..d1ccb7e --- /dev/null +++ b/mnli/sim.py @@ -0,0 +1,75 @@ +import sys +from os import path +file_path = path.abspath(__file__) +sys.path.append(path.dirname(path.dirname(file_path))) + +import env +import dar_type +from datasets.arrow_dataset import Dataset +from transformers.pipelines.pt_utils import KeyDataset +import typing +import math +import threading +from mnli import sim_expr +import text_preprocess + + +def similarity_thread(dataset: Dataset, classifier: dar_type.Pipeline, scores: dar_type.MetricScoreList, expr: dar_type.MNLISimilarityExpression) -> None: + for out in classifier(KeyDataset(dataset, "text"), batch_size=8): + scores.append(expr(out)) + + +def similarity_ngpu(text_dataset: Dataset, classifiers: dar_type.PipelinesList, n_pairs: int, expr: dar_type.MNLISimilarityExpression): + # split dataset to n_gpu, batch 8 on every gpu at same time + # ds[0]: [0, n_pairs / n_gpu); ds[1]: [n_pairs / n_gpu, 2 * (n_pairs / n_gpu)) + ds_batch_sz = int(math.ceil(n_pairs / env.n_gpu)) + thread_pool: typing.List[threading.Thread] = [] + results: typing.List[dar_type.MetricScoreList] = [] + for i_gpu in range(env.n_gpu): + lower_bound = ds_batch_sz * i_gpu + upper_bound = ds_batch_sz * (i_gpu + 1) + if upper_bound > n_pairs: + upper_bound = n_pairs + if lower_bound >= upper_bound: + break + scores_gpu: dar_type.MetricScoreList = [] + results.append(scores_gpu) + thread_pool.append(threading.Thread(target=similarity_thread, kwargs={ + "dataset": text_dataset.select(range(lower_bound, upper_bound, 1)), + "classifier": classifiers.pipelines[i_gpu], + "scores": scores_gpu, + "expr": expr + })) + for thread in thread_pool: + thread.start() + for thread in thread_pool: + thread.join() + scores = text_preprocess.flatten(results) + if len(scores) != n_pairs: + raise Exception("missing score") + return scores + + +def similarity(sent_pairs: dar_type.TextList, classifiers: dar_type.PipelinesList, expr: dar_type.MNLISimilarityExpression) -> dar_type.MetricScoreList: + text_dataset = Dataset.from_dict({"text": sent_pairs}) + n_pairs = len(sent_pairs) + if n_pairs < env.n_gpu: + scores = [] + similarity_thread(dataset=text_dataset, classifier=classifiers.pipelines[0], expr=expr, scores=scores) + return scores + return similarity_ngpu(text_dataset, classifiers, n_pairs, expr) + + +if __name__ == "__main__": + sample_a = "Each computer program uses a region of memory called the stack to enable functions to work properly." + sample_b = "From the outside, Les 4G, a Lyonnais bouchon (traditional restaurant), looked much like the nondescript cafe-cum-tobacco shops that can be found in most small French towns, but inside the decor was as warm and inviting as a country pub." + sent_pairs = ["[SEP]".join([sample_a, sample_b])] + for i in range(100): + sent_pairs.append(sent_pairs[0]) + results = similarity( + sent_pairs=sent_pairs, + classifiers=env.mnli_classifiers["deberta"], + expr=sim_expr.not_neutral + ) + print(len(results)) + print(results) diff --git a/mnli/sim_expr.py b/mnli/sim_expr.py new file mode 100644 index 0000000..76564aa --- /dev/null +++ b/mnli/sim_expr.py @@ -0,0 +1,32 @@ +import dar_type + + +def not_neutral(categories: dar_type.MNLICategories) -> float: + for category in categories: + if category["label"].lower() == "neutral": + return 1 - category["score"] + raise Exception("no neutral category") + + +def entail_only(categories: dar_type.MNLICategories) -> float: + for category in categories: + if category["label"].lower() == "entailment": + return category["score"] + raise 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bertscore-sentence-mnli-deberta-entail_only P 0.534 + R 0.374 + F 0.385 + bertscore-sentence-mnli-deberta-entail_contradict P 0.534 + R 0.270 + F -0.003 +spearmanr human new bertscore-sentence-cos-mpnet P 0.164 + R -0.054 + F -0.024 + bertscore-sentence-cos-roberta P 0.172 + R -0.045 + F -0.010 + bertscore-sentence-mnli-roberta-not_neutral P 0.021 + R -0.121 + F -0.124 + bertscore-sentence-mnli-roberta-entail_only P 0.565 + R 0.352 + F 0.384 + bertscore-sentence-mnli-roberta-entail_contradict P 0.568 + R 0.345 + F 0.027 + bertscore-sentence-mnli-bart-not_neutral P -0.110 + R -0.082 + F -0.081 + bertscore-sentence-mnli-bart-entail_only P 0.589 + R 0.481 + F 0.492 + bertscore-sentence-mnli-bart-entail_contradict P 0.578 + R 0.282 + F -0.057 + bertscore-sentence-mnli-deberta-not_neutral P 0.290 + R -0.093 + F -0.081 + bertscore-sentence-mnli-deberta-entail_only P 0.653 + R 0.457 + F 0.471 + bertscore-sentence-mnli-deberta-entail_contradict P 0.654 + R 0.330 + F -0.004 \ No newline at end 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bertscore-sentence-mnli-roberta-entail_contradict P 0.367 + R 0.255 + F 0.251 + bertscore-sentence-mnli-bart-not_neutral P -0.057 + R -0.145 + F -0.144 + bertscore-sentence-mnli-bart-entail_only P 0.401 + R 0.292 + F 0.317 + bertscore-sentence-mnli-bart-entail_contradict P 0.415 + R 0.328 + F 0.270 + bertscore-sentence-mnli-deberta-not_neutral P 0.278 + R -0.081 + F -0.036 + bertscore-sentence-mnli-deberta-entail_only P 0.446 + R 0.272 + F 0.301 + bertscore-sentence-mnli-deberta-entail_contradict P 0.455 + R 0.347 + F 0.295 +spearmanr human new bertscore-sentence-cos-mpnet P 0.424 + R 0.358 + F 0.429 + bertscore-sentence-cos-roberta P 0.432 + R 0.353 + F 0.442 + bertscore-sentence-mnli-roberta-not_neutral P 0.112 + R -0.153 + F -0.133 + bertscore-sentence-mnli-roberta-entail_only P 0.407 + R 0.236 + F 0.279 + bertscore-sentence-mnli-roberta-entail_contradict P 0.419 + R 0.293 + F 0.288 + bertscore-sentence-mnli-bart-not_neutral P -0.066 + R -0.172 + F -0.166 + 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bertscore-sentence-mnli-roberta-entail_only P 0.184 + R 0.049 + F 0.080 + bertscore-sentence-mnli-roberta-entail_contradict P 0.222 + R 0.187 + F 0.164 + bertscore-sentence-mnli-bart-not_neutral P -0.057 + R -0.171 + F -0.163 + bertscore-sentence-mnli-bart-entail_only P 0.200 + R 0.132 + F 0.155 + bertscore-sentence-mnli-bart-entail_contradict P 0.322 + R 0.244 + F 0.076 + bertscore-sentence-mnli-deberta-not_neutral P 0.081 + R -0.061 + F -0.058 + bertscore-sentence-mnli-deberta-entail_only P 0.299 + R 0.227 + F 0.228 + bertscore-sentence-mnli-deberta-entail_contradict P 0.387 + R 0.307 + F 0.014 +spearmanr human new bertscore-sentence-cos-mpnet P 0.142 + R 0.228 + F 0.227 + bertscore-sentence-cos-roberta P 0.135 + R 0.150 + F 0.153 + bertscore-sentence-mnli-roberta-not_neutral P 0.085 + R -0.071 + F -0.051 + bertscore-sentence-mnli-roberta-entail_only P 0.202 + R 0.055 + F 0.088 + bertscore-sentence-mnli-roberta-entail_contradict P 0.242 + R 0.206 + F 0.181 + bertscore-sentence-mnli-bart-not_neutral P -0.063 + R -0.191 + F -0.182 + bertscore-sentence-mnli-bart-entail_only P 0.220 + R 0.146 + F 0.171 + bertscore-sentence-mnli-bart-entail_contradict P 0.354 + R 0.270 + F 0.084 + bertscore-sentence-mnli-deberta-not_neutral P 0.089 + R -0.066 + F -0.063 + bertscore-sentence-mnli-deberta-entail_only P 0.326 + R 0.247 + F 0.249 + bertscore-sentence-mnli-deberta-entail_contradict P 0.422 + R 0.336 + F 0.015 \ No newline at end of file diff --git a/results/frank-xsum_system.json b/results/frank-xsum_system.json new file mode 100644 index 0000000..8dc4366 --- /dev/null +++ b/results/frank-xsum_system.json @@ -0,0 +1,101 @@ +{ + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-mpnet', 'P')": 0.0956640748, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-mpnet', 'R')": 0.0946057362, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-mpnet', 'F')": 0.1009987222, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-roberta', 'P')": 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bertscore precision 0.684 + recall 0.803 + f1 0.749 + trad bertscore-sentence-cos-roberta P 0.212 + R 0.332 + F 0.267 + new bertscore-sentence-cos-roberta P 0.704 + R 0.724 + F 0.742 + trad bertscore-sentence-mnli-roberta-non-neutral P -0.144 + R 0.028 + F -0.077 + new bertscore-sentence-mnli-roberta-non-neutral P 0.527 + R 0.327 + F 0.391 + trad bertscore-sentence-mnli-roberta-entail-only P -0.172 + R -0.071 + F -0.133 + new bertscore-sentence-mnli-roberta-entail-only P 0.508 + R 0.329 + F 0.401 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.133 + R -0.013 + F -0.085 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.494 + R 0.345 + F 0.255 + trad bertscore-sentence-mnli-deberta-non-neutral P -0.292 + R -0.147 + F -0.237 + new bertscore-sentence-mnli-deberta-non-neutral P 0.407 + R 0.170 + F 0.227 + trad bertscore-sentence-mnli-deberta-entail-only P -0.207 + R -0.133 + F -0.180 + new bertscore-sentence-mnli-deberta-entail-only P 0.479 + R 0.310 + F 0.370 + trad 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-0.193 + new bertscore-sentence-mnli-deberta-non-neutral P 0.450 + R 0.133 + F 0.191 + trad bertscore-sentence-mnli-deberta-entail-only P -0.179 + R -0.120 + F -0.152 + new bertscore-sentence-mnli-deberta-entail-only P 0.516 + R 0.251 + F 0.318 + trad bertscore-sentence-mnli-deberta-entail-contradict P -0.087 + R -0.008 + F -0.005 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.543 + R 0.423 + F 0.170 + CoherenceRating trad bertscore precision -0.044 + recall 0.242 + f1 0.075 + new bertscore precision 0.646 + recall 0.714 + f1 0.688 + trad bertscore-sentence-cos-roberta P 0.152 + R 0.239 + F 0.192 + new bertscore-sentence-cos-roberta P 0.648 + R 0.593 + F 0.629 + trad bertscore-sentence-mnli-roberta-non-neutral P -0.167 + R -0.034 + F -0.119 + new bertscore-sentence-mnli-roberta-non-neutral P 0.454 + R 0.218 + F 0.275 + trad bertscore-sentence-mnli-roberta-entail-only P -0.163 + R -0.080 + F -0.132 + new bertscore-sentence-mnli-roberta-entail-only P 0.496 + R 0.295 + F 0.369 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.105 + R -0.014 + F -0.059 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.492 + R 0.343 + F 0.277 + trad bertscore-sentence-mnli-deberta-non-neutral P -0.295 + R -0.183 + F -0.253 + new bertscore-sentence-mnli-deberta-non-neutral P 0.382 + R 0.075 + F 0.129 + trad bertscore-sentence-mnli-deberta-entail-only P -0.210 + R -0.147 + F -0.186 + new bertscore-sentence-mnli-deberta-entail-only P 0.502 + R 0.274 + F 0.334 + trad bertscore-sentence-mnli-deberta-entail-contradict P -0.117 + R -0.052 + F -0.061 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.520 + R 0.450 + F 0.184 + FluencyRating trad bertscore precision -0.032 + recall 0.214 + f1 0.068 + new bertscore precision 0.590 + recall 0.660 + f1 0.630 + trad bertscore-sentence-cos-roberta P 0.133 + R 0.208 + F 0.168 + new bertscore-sentence-cos-roberta P 0.624 + R 0.533 + F 0.577 + trad bertscore-sentence-mnli-roberta-non-neutral P -0.164 + R -0.045 + F -0.117 + new bertscore-sentence-mnli-roberta-non-neutral P 0.447 + R 0.154 + F 0.205 + trad bertscore-sentence-mnli-roberta-entail-only P -0.146 + R -0.078 + F -0.119 + new bertscore-sentence-mnli-roberta-entail-only P 0.503 + R 0.275 + F 0.343 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.081 + R -0.006 + F -0.042 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.501 + R 0.342 + F 0.246 + trad bertscore-sentence-mnli-deberta-non-neutral P -0.258 + R -0.177 + F -0.227 + new bertscore-sentence-mnli-deberta-non-neutral P 0.386 + R 0.048 + F 0.105 + trad bertscore-sentence-mnli-deberta-entail-only P -0.191 + R -0.140 + F -0.170 + new bertscore-sentence-mnli-deberta-entail-only P 0.485 + R 0.246 + F 0.295 + trad bertscore-sentence-mnli-deberta-entail-contradict P -0.110 + R -0.060 + F -0.069 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.502 + R 0.406 + F 0.175 +kendalltau InformativenessRating trad bertscore precision -0.030 + recall 0.258 + f1 0.122 + new bertscore precision 0.509 + recall 0.652 + f1 0.587 + trad bertscore-sentence-cos-roberta P 0.175 + R 0.259 + F 0.226 + new bertscore-sentence-cos-roberta P 0.434 + R 0.509 + F 0.551 + trad bertscore-sentence-mnli-roberta-non-neutral P -0.058 + R 0.043 + F -0.023 + new bertscore-sentence-mnli-roberta-non-neutral P 0.279 + R 0.263 + F 0.302 + trad bertscore-sentence-mnli-roberta-entail-only P -0.109 + R -0.026 + F -0.069 + new bertscore-sentence-mnli-roberta-entail-only P 0.311 + R 0.287 + F 0.354 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.047 + R 0.061 + F 0.004 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.296 + R 0.313 + F 0.241 + trad bertscore-sentence-mnli-deberta-non-neutral P -0.208 + R -0.109 + F -0.168 + new bertscore-sentence-mnli-deberta-non-neutral P 0.246 + R 0.146 + F 0.176 + trad bertscore-sentence-mnli-deberta-entail-only P -0.215 + R -0.142 + F -0.186 + new bertscore-sentence-mnli-deberta-entail-only P 0.284 + R 0.229 + 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bertscore-sentence-mnli-deberta-entail-only P 0.303 + R 0.030 + F 0.051 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.309 + R 0.266 + F 0.373 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.327 + R 0.113 + F 0.137 + RelevanceRating trad bertscore precision 0.487 + recall 0.301 + f1 0.430 + new bertscore precision 0.259 + recall 0.122 + f1 0.202 + trad bertscore-sentence-cos-roberta P 0.416 + R 0.402 + F 0.432 + new bertscore-sentence-cos-roberta P 0.558 + R 0.301 + F 0.401 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.173 + R 0.164 + F 0.164 + new bertscore-sentence-mnli-roberta-non-neutral P 0.134 + R -0.206 + F -0.199 + trad bertscore-sentence-mnli-roberta-entail-only P 0.145 + R 0.143 + F 0.132 + new bertscore-sentence-mnli-roberta-entail-only P 0.193 + R -0.267 + F -0.252 + trad bertscore-sentence-mnli-roberta-entail-contradict P 0.054 + R 0.065 + F 0.140 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.250 + R -0.187 + F -0.229 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.118 + R 0.109 + F 0.122 + new bertscore-sentence-mnli-deberta-non-neutral P -0.002 + R -0.359 + F -0.363 + trad bertscore-sentence-mnli-deberta-entail-only P 0.279 + R 0.271 + F 0.311 + new bertscore-sentence-mnli-deberta-entail-only P 0.276 + R -0.126 + F -0.117 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.279 + R 0.249 + F 0.347 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.301 + R 0.052 + F -0.053 + CoherenceRating trad bertscore precision 0.407 + recall 0.149 + f1 0.298 + new bertscore precision 0.132 + recall -0.181 + f1 -0.053 + trad bertscore-sentence-cos-roberta P 0.326 + R 0.298 + F 0.333 + new bertscore-sentence-cos-roberta P 0.405 + R -0.037 + F 0.037 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.137 + R 0.127 + F 0.130 + new bertscore-sentence-mnli-roberta-non-neutral P 0.177 + R -0.219 + F -0.186 + trad bertscore-sentence-mnli-roberta-entail-only P 0.089 + R 0.033 + F 0.058 + new bertscore-sentence-mnli-roberta-entail-only P 0.148 + R -0.347 + F -0.314 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.009 + R -0.009 + F 0.100 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.173 + R -0.259 + F -0.097 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.127 + R 0.123 + F 0.135 + new bertscore-sentence-mnli-deberta-non-neutral P 0.148 + R -0.340 + F -0.302 + trad bertscore-sentence-mnli-deberta-entail-only P 0.237 + R 0.174 + F 0.232 + new bertscore-sentence-mnli-deberta-entail-only P 0.266 + R -0.289 + F -0.276 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.201 + R 0.144 + F 0.259 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.291 + R -0.149 + F 0.075 + FluencyRating trad bertscore precision 0.348 + recall 0.149 + f1 0.269 + new bertscore precision 0.107 + recall -0.226 + f1 -0.096 + trad bertscore-sentence-cos-roberta P 0.244 + R 0.220 + F 0.247 + new bertscore-sentence-cos-roberta P 0.405 + R -0.079 + F -0.005 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.075 + R 0.028 + F 0.058 + new bertscore-sentence-mnli-roberta-non-neutral P 0.148 + R -0.235 + F -0.206 + trad bertscore-sentence-mnli-roberta-entail-only P -0.029 + R -0.121 + F -0.081 + new bertscore-sentence-mnli-roberta-entail-only P -0.053 + R -0.453 + F -0.430 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.118 + R -0.155 + F -0.043 + new bertscore-sentence-mnli-roberta-entail-contradict P -0.030 + R -0.397 + F -0.225 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.068 + R 0.012 + F 0.056 + new bertscore-sentence-mnli-deberta-non-neutral P 0.112 + R -0.339 + F -0.299 + trad bertscore-sentence-mnli-deberta-entail-only P 0.157 + R 0.081 + F 0.141 + new bertscore-sentence-mnli-deberta-entail-only P 0.097 + R -0.366 + F -0.366 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.144 + R 0.076 + F 0.259 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.113 + R -0.266 + F 0.018 +kendalltau InformativenessRating trad bertscore precision 0.347 + recall 0.178 + f1 0.271 + new bertscore precision 0.310 + recall 0.216 + f1 0.266 + trad bertscore-sentence-cos-roberta P 0.326 + R 0.331 + F 0.344 + new bertscore-sentence-cos-roberta P 0.343 + R 0.217 + F 0.265 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.130 + R 0.113 + F 0.130 + new bertscore-sentence-mnli-roberta-non-neutral P 0.062 + R -0.035 + F 0.021 + trad bertscore-sentence-mnli-roberta-entail-only P 0.109 + R 0.107 + F 0.105 + new bertscore-sentence-mnli-roberta-entail-only P 0.117 + R -0.070 + F -0.048 + trad bertscore-sentence-mnli-roberta-entail-contradict P 0.035 + R 0.029 + F 0.058 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.136 + R -0.070 + F -0.094 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.082 + R 0.110 + F 0.109 + new bertscore-sentence-mnli-deberta-non-neutral P 0.061 + R -0.045 + F -0.020 + trad bertscore-sentence-mnli-deberta-entail-only P 0.209 + R 0.185 + F 0.189 + new bertscore-sentence-mnli-deberta-entail-only P 0.204 + R 0.009 + F 0.031 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.228 + R 0.165 + F 0.256 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.204 + R 0.025 + F 0.115 + RelevanceRating trad bertscore precision 0.332 + recall 0.203 + f1 0.289 + new bertscore precision 0.204 + recall 0.226 + f1 0.211 + trad bertscore-sentence-cos-roberta P 0.280 + R 0.289 + F 0.300 + new bertscore-sentence-cos-roberta P 0.384 + R 0.248 + F 0.297 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.094 + R 0.046 + F 0.092 + new bertscore-sentence-mnli-roberta-non-neutral P 0.101 + R -0.092 + F -0.051 + trad bertscore-sentence-mnli-roberta-entail-only P 0.028 + R 0.030 + F 0.028 + new bertscore-sentence-mnli-roberta-entail-only P 0.098 + R -0.138 + F -0.129 + trad bertscore-sentence-mnli-roberta-entail-contradict P 0.047 + R 0.010 + F 0.105 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.126 + R -0.126 + F -0.125 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.031 + R 0.025 + F 0.039 + new bertscore-sentence-mnli-deberta-non-neutral P -0.004 + R -0.177 + F -0.158 + trad bertscore-sentence-mnli-deberta-entail-only P 0.160 + R 0.141 + F 0.147 + new bertscore-sentence-mnli-deberta-entail-only P 0.140 + R -0.076 + F -0.066 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.226 + R 0.117 + F 0.236 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.142 + R 0.042 + F -0.002 + CoherenceRating trad bertscore precision 0.281 + recall 0.099 + f1 0.192 + new bertscore precision 0.114 + recall -0.015 + f1 0.033 + trad bertscore-sentence-cos-roberta P 0.186 + R 0.185 + F 0.208 + new bertscore-sentence-cos-roberta P 0.266 + R 0.003 + F 0.055 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.090 + R 0.075 + F 0.092 + new bertscore-sentence-mnli-roberta-non-neutral P 0.116 + R -0.128 + F -0.082 + trad bertscore-sentence-mnli-roberta-entail-only P 0.044 + R 0.019 + F 0.033 + new bertscore-sentence-mnli-roberta-entail-only P 0.067 + R -0.219 + F -0.192 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.003 + R -0.005 + F 0.075 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.088 + R -0.184 + F -0.205 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.088 + R 0.090 + F 0.096 + new bertscore-sentence-mnli-deberta-non-neutral P 0.114 + R -0.197 + F -0.156 + trad bertscore-sentence-mnli-deberta-entail-only P 0.148 + R 0.124 + F 0.138 + new bertscore-sentence-mnli-deberta-entail-only P 0.170 + R -0.216 + F -0.206 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.136 + R 0.086 + F 0.156 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.166 + R -0.102 + F 0.019 + FluencyRating trad bertscore precision 0.293 + recall 0.122 + f1 0.217 + new bertscore precision 0.081 + recall -0.033 + f1 0.015 + trad bertscore-sentence-cos-roberta P 0.189 + R 0.190 + F 0.216 + new bertscore-sentence-cos-roberta P 0.304 + R -0.005 + F 0.051 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.034 + R 0.008 + F 0.033 + new bertscore-sentence-mnli-roberta-non-neutral P 0.130 + R -0.153 + F -0.099 + trad bertscore-sentence-mnli-roberta-entail-only P -0.073 + R -0.106 + F -0.083 + new bertscore-sentence-mnli-roberta-entail-only P -0.042 + R -0.321 + F -0.293 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.126 + R -0.128 + F -0.086 + new bertscore-sentence-mnli-roberta-entail-contradict P -0.015 + R -0.277 + F -0.265 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.022 + R -0.003 + F 0.025 + new bertscore-sentence-mnli-deberta-non-neutral P 0.081 + R -0.226 + F -0.180 + trad bertscore-sentence-mnli-deberta-entail-only P 0.099 + R 0.072 + F 0.085 + new bertscore-sentence-mnli-deberta-entail-only P 0.067 + R -0.265 + F -0.263 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.117 + R 0.050 + F 0.194 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.069 + R -0.174 + F -0.045 +spearmanr InformativenessRating trad bertscore precision 0.505 + recall 0.248 + f1 0.392 + new bertscore precision 0.440 + recall 0.311 + f1 0.360 + trad bertscore-sentence-cos-roberta P 0.467 + R 0.489 + F 0.512 + new bertscore-sentence-cos-roberta P 0.485 + R 0.306 + F 0.372 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.183 + R 0.177 + F 0.191 + new bertscore-sentence-mnli-roberta-non-neutral P 0.087 + R -0.026 + F 0.046 + trad bertscore-sentence-mnli-roberta-entail-only P 0.159 + R 0.180 + F 0.163 + new bertscore-sentence-mnli-roberta-entail-only P 0.176 + R -0.089 + F -0.055 + trad bertscore-sentence-mnli-roberta-entail-contradict P 0.046 + R 0.049 + F 0.078 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.185 + R -0.106 + F -0.146 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.127 + R 0.164 + F 0.165 + new bertscore-sentence-mnli-deberta-non-neutral P 0.092 + R -0.054 + F -0.027 + trad bertscore-sentence-mnli-deberta-entail-only P 0.321 + R 0.288 + F 0.308 + new bertscore-sentence-mnli-deberta-entail-only P 0.295 + R 0.031 + F 0.047 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.338 + R 0.262 + F 0.403 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.298 + R 0.048 + F 0.174 + RelevanceRating trad bertscore precision 0.474 + recall 0.276 + f1 0.404 + new bertscore precision 0.304 + recall 0.323 + f1 0.293 + trad bertscore-sentence-cos-roberta P 0.377 + R 0.400 + F 0.410 + new bertscore-sentence-cos-roberta P 0.541 + R 0.360 + F 0.424 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.142 + R 0.078 + F 0.134 + new bertscore-sentence-mnli-roberta-non-neutral P 0.155 + R -0.140 + F -0.085 + trad bertscore-sentence-mnli-roberta-entail-only P 0.051 + R 0.047 + F 0.053 + new bertscore-sentence-mnli-roberta-entail-only P 0.147 + R -0.202 + F -0.189 + trad bertscore-sentence-mnli-roberta-entail-contradict P 0.068 + R 0.018 + F 0.147 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.188 + R -0.181 + F -0.180 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.061 + R 0.040 + F 0.069 + new bertscore-sentence-mnli-deberta-non-neutral P -0.007 + R -0.263 + F -0.228 + trad bertscore-sentence-mnli-deberta-entail-only P 0.240 + R 0.201 + F 0.223 + new bertscore-sentence-mnli-deberta-entail-only P 0.203 + R -0.120 + F -0.100 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.328 + R 0.184 + F 0.337 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.208 + R 0.067 + F 0.019 + CoherenceRating trad bertscore precision 0.410 + recall 0.139 + f1 0.290 + new bertscore precision 0.166 + recall -0.026 + f1 0.043 + trad bertscore-sentence-cos-roberta P 0.280 + R 0.282 + F 0.313 + new bertscore-sentence-cos-roberta P 0.381 + R -0.005 + F 0.073 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.108 + R 0.105 + F 0.119 + new bertscore-sentence-mnli-roberta-non-neutral P 0.185 + R -0.181 + F -0.121 + trad bertscore-sentence-mnli-roberta-entail-only P 0.063 + R 0.027 + F 0.047 + new bertscore-sentence-mnli-roberta-entail-only P 0.093 + R -0.327 + F -0.297 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.014 + R -0.014 + F 0.100 + new bertscore-sentence-mnli-roberta-entail-contradict P 0.116 + R -0.263 + F -0.286 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.118 + R 0.126 + F 0.129 + new bertscore-sentence-mnli-deberta-non-neutral P 0.172 + R -0.285 + F -0.231 + trad bertscore-sentence-mnli-deberta-entail-only P 0.217 + R 0.176 + F 0.200 + new bertscore-sentence-mnli-deberta-entail-only P 0.235 + R -0.309 + F -0.298 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.184 + R 0.118 + F 0.230 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.243 + R -0.158 + F 0.023 + FluencyRating trad bertscore precision 0.434 + recall 0.172 + f1 0.325 + new bertscore precision 0.131 + recall -0.054 + f1 0.012 + trad bertscore-sentence-cos-roberta P 0.279 + R 0.294 + F 0.312 + new bertscore-sentence-cos-roberta P 0.445 + R -0.001 + F 0.073 + trad bertscore-sentence-mnli-roberta-non-neutral P 0.047 + R 0.017 + F 0.045 + new bertscore-sentence-mnli-roberta-non-neutral P 0.196 + R -0.228 + F -0.156 + trad bertscore-sentence-mnli-roberta-entail-only P -0.094 + R -0.131 + F -0.114 + new bertscore-sentence-mnli-roberta-entail-only P -0.064 + R -0.445 + F -0.420 + trad bertscore-sentence-mnli-roberta-entail-contradict P -0.190 + R -0.188 + F -0.115 + new bertscore-sentence-mnli-roberta-entail-contradict P -0.024 + R -0.406 + F -0.389 + trad bertscore-sentence-mnli-deberta-non-neutral P 0.014 + R -0.004 + F 0.020 + new bertscore-sentence-mnli-deberta-non-neutral P 0.120 + R -0.318 + F -0.262 + trad bertscore-sentence-mnli-deberta-entail-only P 0.144 + R 0.117 + F 0.126 + new bertscore-sentence-mnli-deberta-entail-only P 0.092 + R -0.391 + F -0.390 + trad bertscore-sentence-mnli-deberta-entail-contradict P 0.169 + R 0.083 + F 0.284 + new bertscore-sentence-mnli-deberta-entail-contradict P 0.103 + R -0.266 + F -0.067 \ No newline at end of file diff --git a/results/qags-cnndm-pooled_summary.json b/results/qags-cnndm-pooled_summary.json new file mode 100644 index 0000000..dbaf1bb --- /dev/null +++ b/results/qags-cnndm-pooled_summary.json @@ -0,0 +1,398 @@ +{ + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-mpnet', 'P')": { + "0": 0.6427466543, + "average": 0.6427466543 + }, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-mpnet', 'R')": { + "0": 0.3377246938, + "average": 0.3377246938 + }, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-mpnet', 'F')": { + "0": 0.4929092465, + "average": 0.4929092465 + }, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-roberta', 'P')": { + "0": 0.6724701716, + "average": 0.6724701716 + }, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-roberta', 'R')": { + "0": 0.3802072463, + "average": 0.3802072463 + }, + "('pearsonr', 'human', 'new', 'bertscore-sentence-cos-roberta', 'F')": { + "0": 0.5433682591, + "average": 0.5433682591 + }, + "('pearsonr', 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-0.1206352105, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-not_neutral', 'P')": 0.0474895444, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-not_neutral', 'R')": -0.0294316045, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-not_neutral', 'F')": -0.0240571257, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-entail_only', 'P')": 0.4655575253, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-entail_only', 'R')": 0.3899811311, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-entail_only', 'F')": 0.4061095614, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-entail_contradict', 'P')": 0.5149053009, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-entail_contradict', 'R')": 0.2783624055, + "('spearmanr', 'human', 'new', 'bertscore-sentence-mnli-deberta-entail_contradict', 'F')": -0.1636251384 +} \ No newline at end of file diff --git a/results/qags-xsum_system.txt b/results/qags-xsum_system.txt new file mode 100644 index 0000000..6f8e9ab --- /dev/null +++ b/results/qags-xsum_system.txt @@ -0,0 +1,100 @@ +corr_metric aspect approach model score_name +pearsonr human new bertscore-sentence-cos-mpnet P 0.166 + R 0.065 + F 0.102 + bertscore-sentence-cos-roberta P 0.163 + R 0.093 + F 0.127 + bertscore-sentence-mnli-roberta-not_neutral P -0.062 + R -0.036 + F -0.039 + bertscore-sentence-mnli-roberta-entail_only P 0.324 + R 0.222 + F 0.257 + bertscore-sentence-mnli-roberta-entail_contradict P 0.351 + R 0.201 + F 0.078 + bertscore-sentence-mnli-bart-not_neutral P -0.023 + R -0.026 + F -0.028 + bertscore-sentence-mnli-bart-entail_only P 0.418 + R 0.351 + F 0.366 + bertscore-sentence-mnli-bart-entail_contradict P 0.422 + R 0.179 + F 0.018 + bertscore-sentence-mnli-deberta-not_neutral P 0.002 + R -0.006 + F -0.012 + bertscore-sentence-mnli-deberta-entail_only P 0.458 + R 0.342 + F 0.360 + bertscore-sentence-mnli-deberta-entail_contradict P 0.478 + R 0.223 + F -0.123 +kendalltau human new bertscore-sentence-cos-mpnet P 0.146 + R 0.053 + F 0.094 + bertscore-sentence-cos-roberta P 0.141 + R 0.082 + F 0.111 + bertscore-sentence-mnli-roberta-not_neutral P -0.021 + R -0.023 + F -0.027 + bertscore-sentence-mnli-roberta-entail_only P 0.240 + R 0.162 + F 0.181 + bertscore-sentence-mnli-roberta-entail_contradict P 0.281 + R 0.182 + F 0.019 + bertscore-sentence-mnli-bart-not_neutral P -0.030 + R -0.019 + F -0.016 + bertscore-sentence-mnli-bart-entail_only P 0.357 + R 0.307 + F 0.316 + bertscore-sentence-mnli-bart-entail_contradict P 0.366 + R 0.169 + F -0.085 + bertscore-sentence-mnli-deberta-not_neutral P 0.034 + R -0.022 + F -0.019 + bertscore-sentence-mnli-deberta-entail_only P 0.357 + R 0.294 + F 0.307 + bertscore-sentence-mnli-deberta-entail_contradict P 0.396 + R 0.207 + F -0.122 +spearmanr human new bertscore-sentence-cos-mpnet P 0.191 + R 0.071 + F 0.125 + bertscore-sentence-cos-roberta P 0.187 + R 0.110 + F 0.147 + bertscore-sentence-mnli-roberta-not_neutral P -0.028 + R -0.030 + F -0.035 + bertscore-sentence-mnli-roberta-entail_only P 0.319 + R 0.216 + F 0.243 + bertscore-sentence-mnli-roberta-entail_contradict P 0.370 + R 0.245 + F 0.026 + bertscore-sentence-mnli-bart-not_neutral P -0.039 + R -0.024 + F -0.020 + bertscore-sentence-mnli-bart-entail_only P 0.469 + R 0.407 + F 0.419 + bertscore-sentence-mnli-bart-entail_contradict P 0.480 + R 0.230 + F -0.121 + bertscore-sentence-mnli-deberta-not_neutral P 0.047 + R -0.029 + F -0.024 + bertscore-sentence-mnli-deberta-entail_only P 0.466 + R 0.390 + F 0.406 + bertscore-sentence-mnli-deberta-entail_contradict P 0.515 + R 0.278 + F -0.164 \ No newline at end of file diff --git a/text_preprocess.py b/text_preprocess.py new file mode 100644 index 0000000..586d3f4 --- /dev/null +++ b/text_preprocess.py @@ -0,0 +1,19 @@ +import spacy +import dar_type +import typing + +nlp_spacy = spacy.load("en_core_web_lg") + + +def list_segmentation(raw_list: dar_type.TextList) -> dar_type.TextListSegments: + return [text_segmentation(raw) for raw in raw_list] + + +def text_segmentation(piece: str) -> dar_type.TextSegments: + doc = nlp_spacy(piece) + doc_sents = [sent.text for sent in doc.sents] + return doc_sents + + +def flatten(l: typing.List[typing.List]): + return [item for sublist in l for item in sublist]