Fine-Tuned GPT-2 Model for Sentiment Analysis

Model Description

This fine-tuned GPT-2 model is designed for sentiment analysis of tweets. Trained on the mteb/tweet_sentiment_extraction dataset, the model classifies tweets into three sentiment categories Positive, Neutral, and Negative.

Developed by: Pranav Sethi
Model type: gpt2
Language(s) (NLP): Large Language Model
License: Apachee License 2.0

Uses

This model is intended for sentiment analysis of tweets or other short social media texts. It is designed for researchers, developers, and data scientists who want to classify textual data into three sentiment categories: Positive, Neutral, or Negative. End users analyzing sentiment trends based on this model's predictions

Training Data

The model is fine-tuned on the Tweet Sentiment Extraction dataset

Dataset: mteb/tweet_sentiment_extraction

Training Procedure

This model was fine-tuned using the Hugging Face Transformers library with the following training configuration:

Training Hyperparameters

Batch size: 1
Training epochs: 3
Base model: GPT-2
gradient_accumulation_steps: 4

Evaluation

Testing Data, Factors & Metrics

Testing Data

The testing was performed on the Tweet Sentiment Extraction dataset

Metrics

Base Model: GPT-2
Task: Sentiment Classification (Positive, Neutral, Negative)
Dataset: Tweet Sentiment Extraction
Evaluation Metric: Accuracy

Results

The model achieved an accuracy of 73% on the test dataset.

Usage

This model is designed for sentiment analysis of tweets or other short social media text. Given an input text, it predicts the sentiment as Positive, Neutral, or Negative.

Example Code

from datasets import load_dataset
import pandas as pd
from transformers import GPT2ForSequenceClassification
from transformers import TrainingArguments, Trainer
import evaluate
import torch

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("pranavsethi01/yps1382-allin")
model = AutoModelForSequenceClassification.from_pretrained("pranavsethi01/yps1382-allin")

# Perform sentiment analysis
inputs = tokenizer("I love Hugging Face!", return_tensors="pt")
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=-1)

# Output sentiment (0: Negative, 1: Neutral, 2: Positive)
print("Sentiment:", predictions.item())

# Limitations
- ** Bias **: The dataset may contain biased or harmful text, potentially influencing predictions.
- ** Domain Limitations **: Optimized for English tweets; performance may degrade on other text types or languages.

# Ethical Considerations
This model should be used responsibly. Be aware of biases in the training data and avoid deploying the model in sensitive or high-stakes applications without further validation.

# Acknowledgments
- Hugging Face Transformers library
- mteb/tweet_sentiment_extraction dataset