| |
| """Finetune.ipynb |
| |
| Automatically generated by Colab. |
| |
| Original file is located at |
| https://colab.research.google.com/drive/1b_AA5GHhblSKrQymYs_uYYDEqvqklfrV |
| """ |
| from datasets import load_dataset |
|
|
| dataset = load_dataset("yelp_review_full") |
| dataset["train"][100] |
|
|
| from transformers import AutoTokenizer |
|
|
| tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") |
|
|
|
|
| def tokenize_function(examples): |
| return tokenizer(examples["text"], padding="max_length", truncation=True) |
|
|
|
|
| tokenized_datasets = dataset.map(tokenize_function, batched=True) |
|
|
| small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(100)) |
| small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(100)) |
|
|
| from transformers import AutoModelForSequenceClassification |
|
|
| model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5) |
|
|
| from transformers import TrainingArguments |
|
|
| training_args = TrainingArguments(output_dir="test_trainer") |
|
|
| import numpy as np |
| import evaluate |
|
|
| metric = evaluate.load("accuracy") |
|
|
| def compute_metrics(eval_pred): |
| logits, labels = eval_pred |
| predictions = np.argmax(logits, axis=-1) |
| return metric.compute(predictions=predictions, references=labels) |
|
|
| from transformers import TrainingArguments, Trainer |
|
|
| training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch") |
|
|
| trainer = Trainer( |
| model=model, |
| args=training_args, |
| train_dataset=small_train_dataset, |
| eval_dataset=small_eval_dataset, |
| compute_metrics=compute_metrics, |
| ) |
|
|
| trainer.train() |
|
|
| trainer.push_to_hub() |
