My_Sentiment_Analysis_Model

Sentiment Analysis Model

This model is a fine-tuned version of DistilBERT for sentiment analysis. It classifies text into three categories: Positive, Neutral, and Negative.

Model Details

• Model Type: DistilBERT (DistilBERT-base-uncased)
• Fine-Tuning Task: Sentiment Analysis
• Classes: 3 (Positive, Neutral, Negative)
• Dataset: Custom sentiment dataset with labeled "Positive", "Neutral", and "Negative" text data.

Intended Use

This model can be used to classify text as Positive, Neutral, or Negative. It's ideal for applications that require sentiment classification, such as customer feedback analysis, reviews, or social media sentiment monitoring.

Model Performance

This model was fine-tuned on a custom sentiment dataset. Below are its performance metrics :

• Accuracy: 0.91
• F1-Score: 0.89
• Precision: 0.89
• Recall: 0.89

License

This model is licensed under the MIT License.

Usage

Install Hugging Face Transformers

To use this model, you need to install the transformers library. You can do so with the following command:

pip install transformers

Example Code to Use the Model

from transformers import pipeline

# Load the sentiment-analysis pipeline
sentiment_analysis = pipeline("text-classification", model="chawki17/my_sentiment_model")

# Example text
text = "I love this product!"

# Predict sentiment
result = sentiment_analysis(text)
print(result)

Inputs and Outputs

• Input: A string of text (e.g., a customer review).
• Output: A sentiment label (Positive, Neutral, or Negative) with confidence score.

Example output:

[{'label': 'POSITIVE', 'score': 0.98}]

Limitations

• The model may not perform well on text data from domains that were not part of the training set.
• It may not generalize well to very short texts or highly domain-specific language.
• The model is based on English text data and may not work well for other languages.

Model Card and Documentation

For more details on this model and its performance, visit the model page on Hugging Face.

Acknowledgements

• This model was fine-tuned using the DistilBERT architecture.
• The dataset was custom-built for this sentiment analysis task.