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kaniskaZoro/
sentiment-analyzer

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Sentiment Analyzer

Overview

This repository contains a sentiment analysis model trained on the IMDb dataset. The model is based on distilbert-base-uncased and fine-tuned for binary sentiment classification (positive/negative).

Files

sentiment_model/ : Contains the trained model files.
results/checkpoint-125/ : Checkpoint directory from training.
sample_data/ : Sample dataset files used for training and evaluation.
wandb/ : Weights & Biases logs and run data.

Installation

pip install transformers datasets sentence-transformers

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained('kaniskaZoro/sentiment-analyzer')
model = AutoModelForSequenceClassification.from_pretrained('kaniskaZoro/sentiment-analyzer')

text = "The movie was fantastic!"
inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=256)
outputs = model(**inputs)

Training

The model was trained using the Trainer API from Hugging Face Transformers with the following settings:

Dataset: IMDb (subset of 2000 train, 500 test samples for demonstration)
Batch size: 16
Epochs: 1
Logging and checkpointing integrated with Weights & Biases

License

Specify license here (e.g., MIT, Apache 2.0).