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README.md

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+# SentimentBot: A Sentiment Analysis Chatbot with BERT 🤖
+
+## Overview
+SentimentBot is a sentiment analysis chatbot built using BERT, fine-tuned on the SST-2 dataset. It predicts whether a given text expresses a positive or negative sentiment and responds interactively. This project was developed to enhance my NLP skills for an AI Engineer internship.
+
+- Model: BERT (bert-base-uncased)
+- Dataset: SST-2 (Stanford Sentiment Treebank)
+- Performance: Achieved 92.3% accuracy and 92.3% F1 score on the validation set after 2 epochs.
+
+## Features
+
+- Analyzes sentiment of user input (positive/negative).
+- Interactive chatbot responses based on predicted sentiment.
+- Fine-tuned BERT model for high accuracy.
+
+## Installation
+To run this project locally, follow these steps:
+
+1. Clone the repository:
+    ```
+    git clone https://github.com/moraix/SentimentBot.git
+    cd SentimentBot
+    ```
+
+
+2. Install dependencies:
+    ```
+    pip install torch transformers datasets sklearn
+    ```
+
+
+## Usage
+
+1. Run the notebook:
+
+- Open SentimentBot.ipynb in Jupyter Notebook.
+- Follow the steps to train the model.
+
+
+2. Test the chatbot:
+
+- Run the last cell in the notebook to interact with the chatbot.
+- Enter a sentence (e.g., "I love this movie!") and the bot will predict the sentiment and respond.
+
+    Example:
+    Text: I love this movie so much!
+    Sentiment: positive (confidence: 0.95)  
+    I'm glad you liked it! 😊 Do you have any other comments?
+
+
+## Results
+After fine-tuning BERT on SST-2 for 2 epochs:
+
+- Eval Loss: 0.311
+- Accuracy: 92.3%
+- F1 Score: 92.3%
+- Eval Runtime: 3.42 seconds
+
+## Future Improvements
+
+- Add 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.

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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