Daredevil47/Verbiage_Vindicator_47

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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]

Model Sources [optional]

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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

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

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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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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Citation [optional]

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APA:

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