---
license: apache-2.0
datasets:
- C-MTEB/TNews-classification
metrics:
- accuracy
base_model:
- openai-community/gpt2
pipeline_tag: text-classification
library_name: transformers
---

# Model Card for Model ID

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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### 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%.



- **Developed by:** Pradeep Vepada
- **Contact:** pradeep.vepada24@gmail.com
- **Shared by [optional]:** [More Information Needed]
- **Model type:**
-    Architecture: GPT-2
     Fine-Tuned Task: Sentiment Analysis
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

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## 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.

### Performance:
   Accuracy: 76%
   Evaluation Metric: Accuracy
   Validation Split: 10% of the dataset.

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### 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


### Out-of-Scope Use

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

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

 Nvidia A100 

#### Example Code:
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


#### Hardware

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#### Software

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