| | --- |
| | tags: |
| | - sentiment-analysis |
| | - bert |
| | - pytorch |
| | - text-classification |
| | license: mit |
| | --- |
| | |
| | # SentimentBot: A Sentiment Analysis Chatbot with BERT 🤖 |
| |
|
| | ## Overview |
| | SentimentBot is a fine-tuned BERT model (bert-base-uncased) designed for sentiment analysis, trained on the SST-2 (Stanford Sentiment Treebank) dataset. This model predicts whether a given text expresses a positive or negative sentiment with high accuracy, achieving 92.3% accuracy and 92.3% F1 score on the validation set after 2 epochs. It was developed as part of an AI Engineer internship project focusing on NLP. |
| |
|
| | ## Model Details |
| | - Base Model: bert-base-uncased |
| | - Dataset: SST-2 (2 classes: 0 = negative, 1 = positive) |
| | - Training: Fine-tuned for 2 epochs with a batch size of 16 |
| | - Performance: |
| | Eval Loss: 0.311 |
| | Accuracy: 92.3% |
| | F1 Score: 92.3% |
| | |
| | ## Usage |
| | To use the SentimentBot model in your Python project, install the required libraries and load the model as follows: |
| | ``` |
| | pip install transformers torch |
| | ``` |
| | ``` |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | import torch |
| | |
| | # Load the model and tokenizer |
| | tokenizer = AutoTokenizer.from_pretrained("moraix/SentimentBot") |
| | model = AutoModelForSequenceClassification.from_pretrained("moraix/SentimentBot").to("cuda") |
| | |
| | # Example input |
| | text = "I love this movie so much!" |
| | inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to("cuda") |
| | |
| | # Predict sentiment |
| | model.eval() |
| | with torch.no_grad(): |
| | outputs = model(**inputs) |
| | predictions = torch.softmax(outputs.logits, dim=1) |
| | predicted_class = torch.argmax(predictions, dim=1).item() |
| | confidence = predictions[0, predicted_class].item() |
| | |
| | label_map = {0: "negative", 1: "positive"} |
| | sentiment = label_map[predicted_class] |
| | print(f"Text: {text}") |
| | print(f"Sentiment: {sentiment} (Confidence: {confidence:.2f})") |
| | ``` |
| | ## Intended Uses |
| | - Primary Use: Analyzing sentiment in English text (e.g., movie reviews, social media posts). |
| | - Out-of-Scope: Multi-class sentiment (e.g., neutral) or non-English text (requires further fine-tuning). |
| | ## Limitations |
| | - Trained only on SST-2, so performance may vary on other datasets. |
| | - Limited to binary classification (positive/negative). |
| | ## Future Improvements |
| | - dd support for more complex sentiments (e.g., neutral, angry). |
| | - Expand the dataset with custom data. |
| | - Deploy the chatbot as a web app using Flask or Streamlit. |
| | ## Contributing |
| | Feel free to fork this repository, open issues, or submit pull requests. Feedback is always welcome! Contact For questions, reach out via mohammadmehdiomidi95@gmail.com or connect with me on LinkedIn. |
