Welcome to the BERT Model repository! This project demonstrates the usage of the BERT (Bidirectional Encoder Representations from Transformers) model for natural language processing tasks, specifically focusing on sentiment analysis using the IMDb movie review dataset.
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- Overview
- Features
- Technology Used
- Installation
- Usage
- Datasets
- Training
- Evaluation
- Fine-Tuning
- Results
- Contributing
- License
BERT is a pre-trained model on a large corpus of text, making it versatile for numerous NLP tasks such as:
- Sentiment Analysis
- Question Answering
- Named Entity Recognition (NER)
- Text Classification
- Language Translation
This repository provides a flexible implementation of BERT for these tasks.
- Fine-tuning BERT for text classification
- Fine-tuning BERT for named entity recognition (NER)
- Customizable for other NLP tasks
- Easy integration with different datasets
- Detailed training and evaluation scripts
To use this repository, clone it and install the necessary dependencies:
git clone https://github.com/Ramsey99/Bert_Model.git
cd Bert_Model
pip install -r requirements.txtLoading the Pre-trained BERT Model You can load the pre-trained BERT model using the following code:
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')Tokenizing Text To tokenize text data for input to the BERT model:
text = "Replace this with your text"
input_ids = tokenizer.encode(text, add_special_tokens=True)IMDb Movie Review Dataset This project uses the IMDb movie review dataset, a popular dataset for binary sentiment classification. The dataset contains 50,000 highly polar movie reviews, with 25,000 for training and 25,000 for testing.
- Classes: Positive and Negative
- Training Samples: 25,000
- Test Samples: 25,000
Ensure the dataset is properly downloaded and placed in the correct directory before running the training or evaluation scripts.
To train the BERT model on your custom dataset, use the provided training script:
python train.py --dataset_path path/to/your/dataset --output_dir path/to/save/modelEvaluate the trained model using the evaluation script:
python evaluate.py --model_path path/to/your/saved/model --dataset_path path/to/your/datasetFine-tune the BERT model for a specific task by using the fine-tuning script:
python fine_tune.py --dataset_path path/to/your/dataset --output_dir path/to/save/model
Contributions are welcome! If you have any ideas, suggestions, or issues, please open an issue or submit a pull request.
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Fork the Project:
- Click on the "Fork" button at the top right corner of the repository's page on GitHub to create your own copy of the project.
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Clone Your Forked Repository:
- Clone the forked repository to your local machine using the following command:
git clone https://github.com/Ramsey99/fest-registration
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Create a New Branch and Move to the Branch:
- Create a new branch for your changes and move to that branch using the following commands:
git checkout -b <branch-name>
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Add Your Changes:
- After you have made your changes, check the status of the changed files using the following command:
git status -s
- Add all the files to the staging area using the following command:
git add .- or add specific files using:
git add <file_name1> <file_name2>
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Commit Your Changes:
- Commit your changes with a descriptive message using the following command:
git commit -m "<EXPLAIN-YOUR_CHANGES>" -
Push Your Changes:
- Push your changes to your forked repository on GitHub using the following command:
git push origin <branch-name>
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Open a Pull Request:
- Go to the GitHub page of your forked repository, and you should see an option to create a pull request. Click on it, provide a descriptive title and description for your pull request, and then submit it.
