Add new example based on more modern example

integrations/model-training/transformers/transformers-google-bert-fine-tuning/requirements.txt

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+comet_ml
+datasets
+evaluate
+numpy
+scikit-learn
+torch
+transformers[torch]

integrations/model-training/transformers/transformers-google-bert-fine-tuning/transformers-google-bert-fine-tuning.py

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+# coding: utf-8
+import comet_ml
+
+import evaluate
+import numpy as np
+from datasets import load_dataset
+
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    Trainer,
+    TrainingArguments,
+    enable_full_determinism,
+)
+
+SEED = 42
+
+enable_full_determinism(SEED)
+
+# Login to Comet if needed
+comet_ml.init(project_name="comet-example-transformers-google-bert-fine-tuning")
+
+
+tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased")
+
+
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+
+dataset = load_dataset("yelp_review_full")
+dataset["train"] = dataset["train"].shuffle(seed=SEED).select(range(100))
+dataset["test"] = dataset["test"].shuffle(seed=SEED).select(range(100))
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+small_train_dataset = tokenized_datasets["train"]
+small_eval_dataset = tokenized_datasets["test"]
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "google-bert/bert-base-cased", num_labels=5
+)
+
+metric = evaluate.load("accuracy")
+
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)
+
+    experiment = comet_ml.get_running_experiment()
+    if experiment:
+        experiment.log_confusion_matrix(predictions, labels)
+
+    return metric.compute(predictions=predictions, references=labels)
+
+
+EPOCHS = 5
+
+training_args = TrainingArguments(
+    seed=SEED,
+    output_dir="./results",
+    overwrite_output_dir=True,
+    num_train_epochs=EPOCHS,
+    eval_strategy="epoch",
+    do_train=True,
+    do_eval=True,
+    report_to=["all"],
+)
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=small_train_dataset,
+    eval_dataset=small_eval_dataset,
+    compute_metrics=compute_metrics,
+)
+
+trainer.train()