gpt2-tweet-sentiment-model

GPT‑2 Tweet Sentiment Classifier

This model is a fine-tuned version of GPT‑2, trained for tweet sentiment classification (negative, neutral, positive) using the MTEB Tweet Sentiment Extraction dataset.

Model Overview

• Base Model: GPT‑2 (decoder-only transformer)
• Dataset: MTEB Tweet Sentiment Extraction. A dataset containing ~27481 training tweets and 3534 test tweets, labeled as negative (0), neutral (1), or positive (2)
• Label Distribution (test set):
  • Negative (0): 1001 tweets
  • Neutral (1): 1430 tweets
  • Positive (2): 1103 tweets

Evaluation Metric

These results reflect robust performance for a GPT‑2-based classifier on this tweet sentiment task.

Training Details

• Epochs: 5
• Evaluation Metrics: Accuracy, F1 Score, Loss
• Tokenizer: GPT-2 tokenizer (GPT2Tokenizer) loaded from "gpt2" and modified to use eos_token as the pad_token

Dataset Details

• Name: MTEB Tweet Sentiment Extraction
• Source: MTEB - Massive Text Embedding Benchmark (dataset link)
• Split Sizes:
  • Train: 27481 samples
  • Test: 3534 samples
• Text Length:
  • Average: ~68 characters
  • Range: from 4 to ~142 characters
• Classes: 3 (0 = negative, 1 = neutral, 2 = positive)

License

This model is licensed under the Apache 2.0 License.

Author

Fine-tuned and published by @herrerovir using Hugging Face Transformers.

Usage Example

Loading and using the model via Hugging Face transformers:

from transformers import pipeline

classifier = pipeline("text-classification", model="herrerovir/gpt2-sentiment-tweets")

output = classifier("I can't believe how amazing this is!")
print(output)  # e.g., [{'label': 'positive', 'score': 0.94}]