imdb_roberta_large

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imdb_roberta_large

This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1728
• Accuracy: 0.9627

Model description

Train and Test Code

from datasets import load_dataset
imdb = load_dataset("imdb")

import numpy as np
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
import torch
from transformers import AutoTokenizer
from transformers import DataCollatorWithPadding
from transformers import EarlyStoppingCallback
import evaluate


# model_name = 'xlnet-large-cased'
model_name = 'roberta-large'

id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}
def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return accuracy.compute(predictions=predictions, references=labels)


tokenizer = AutoTokenizer.from_pretrained(model_name)
def preprocess_function(examples):
    return tokenizer(examples["text"], truncation=True)
tokenized_imdb = imdb.map(preprocess_function, batched=True)

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
accuracy = evaluate.load("accuracy")

model = AutoModelForSequenceClassification.from_pretrained(
    model_name, num_labels=2, id2label=id2label, label2id=label2id
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)


bts = 8
accumulated_step = 2
training_args = TrainingArguments(
    output_dir=f"5imdb_{model_name.replace('-','_')}",
    learning_rate=2e-5,
    per_device_train_batch_size=bts,
    per_device_eval_batch_size=bts,
    num_train_epochs=2,
    weight_decay=0.01,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
    push_to_hub=True,
   gradient_accumulation_steps=accumulated_step,
)
# 创建 EarlyStoppingCallback 回调
early_stopping = EarlyStoppingCallback(early_stopping_patience=3)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_imdb["train"],
    eval_dataset=tokenized_imdb["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
    callbacks=[early_stopping],
)

trainer.train()

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1
• Datasets 2.18.0
• Tokenizers 0.15.2