|
|
|
"""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()
|
|
|
|
|
