JCCHH/imdb-bert-sentiment

BERT Fine-tuned on IMDB for Sentiment Analysis

Fine-tuned from bert-base-uncased on the Stanford IMDB dataset for binary sentiment classification.

Training Details

Parameter	Value
Base model	bert-base-uncased
Learning rate	2e-5
Batch size	4
Epochs	2
Max sequence length	512

Usage

from transformers import BertForSequenceClassification, BertTokenizer

tokenizer = BertTokenizer.from_pretrained("COMP6713bert/imdb-bert-sentiment")
model = BertForSequenceClassification.from_pretrained("COMP6713bert/imdb-bert-sentiment")

inputs = tokenizer("This movie was great!", return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    outputs = model(**inputs)
    predicted = torch.argmax(outputs.logits, dim=-1).item()

print("Positive" if predicted == 1 else "Negative")

Labels

• 0: Negative
• 1: Positive