usef310/flan-t5-small-sentiment

FLAN-T5 Small - Sentiment Analysis

Fine-tuned version of google/flan-t5-small for sentiment analysis on IMDB reviews.

Model Details

• Base Model: google/flan-t5-small
• Task: Binary sentiment classification (positive/negative)
• Dataset: IMDB movie reviews (300 training samples)
• Accuracy: 85.00%

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("usef310/flan-t5-small-sentiment")
model = AutoModelForSeq2SeqLM.from_pretrained("usef310/flan-t5-small-sentiment")

text = "This movie was amazing!"
inputs = tokenizer("sentiment: " + text, return_tensors="pt")
outputs = model.generate(**inputs)
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(prediction)  # Output: positive or negative

Training Details

• Epochs: 3
• Batch size: 4 (with gradient accumulation)
• Learning rate: 5e-5
• Optimizer: AdamW