sreshtakusuru
/

the-smart-ashish-model

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: apache-2.0
+base_model:
+- openai-community/gpt2
 ---
+# Fine-Tuned GPT-2 for Sentiment Analysis
+## Model Description
+This is a fine-tuned version of the GPT-2 model for sentiment analysis on tweets. The model has been trained on the `mteb/tweet_sentiment_extraction` dataset to classify tweets into three sentiment categories: **Positive**, **Neutral**, and **Negative**. It uses the Hugging Face Transformers library and achieves an evaluation accuracy of **76%**.
+---
 ## Model Details
+### Developer/Owner
+- **Creator**: KUSURU SRESHTA
+- **Contact**: kusurusreshta@gmail.com
+### Model Type
+- **Architecture**: GPT-2
+- **Fine-Tuned Task**: Sentiment Analysis
+### Dataset
+- **Name**: `mteb/tweet_sentiment_extraction`
+- **Description**: A dataset for extracting and classifying sentiment in tweets.
+- **Language**: English
+- **Size**: 1,000 samples used for training and 1,000 for evaluation.
+### Training Configuration
+- **Tokenizer**: GPT-2 Tokenizer (with EOS token as pad token)
+- **Optimizer**: AdamW
+- **Learning Rate**: 1e-5
+- **Epochs**: 3
+- **Batch Size**: 1
+- **Hardware Used**: A100
+### Performance
+- **Accuracy**: 76%
+- **Evaluation Metric**: Accuracy
+- **Validation Split**: 10% of the 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
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("charlie1898/gpt2_finetuned_twitter_sentiment_analysis")
+model = AutoModelForSequenceClassification.from_pretrained("charlie1898/gpt2_finetuned_twitter_sentiment_analysis")
+# Example input
+text = "I love using Hugging Face models!"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+predicted_class = torch.argmax(outputs.logits).item()
+print(f"Predicted sentiment class: {predicted_class}")
+# 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