| | --- |
| | language: en |
| | tags: |
| | - token-classification |
| | - named-entity-recognition |
| | - bert |
| | - transformers |
| | license: mit |
| | datasets: |
| | - conll2003 |
| | --- |
| | |
| | # Token Classification Model |
| |
|
| | ## Description |
| | This project involves developing a machine learning model for token classification, specifically for Named Entity Recognition (NER). Using a fine-tuned BERT model from the Hugging Face library, this system classifies tokens in text into predefined categories like names, locations, and dates. |
| |
|
| | The model is trained on a dataset annotated with entity labels to accurately classify each token. This token classification system is useful for information extraction, document processing, and conversational AI applications. |
| |
|
| | ## Technologies Used |
| |
|
| | ### Dataset |
| | - **Source:** Kaggle: conll2003 |
| | - **Purpose:** Contains text data with annotated entities for token classification. |
| |
|
| | ### Model |
| | - **Base Model:** BERT (bert-base-uncased) |
| | - **Library:** Hugging Face transformers |
| | - **Task:** Token Classification (Named Entity Recognition) |
| |
|
| | ### Approach |
| |
|
| | #### Preprocessing: |
| | - Load and preprocess the dataset. |
| | - Tokenize the text data and align labels with tokens. |
| |
|
| | #### Fine-Tuning: |
| | - Fine-tune the BERT model on the token classification dataset. |
| |
|
| | #### Training: |
| | - Train the model to classify each token into predefined entity labels. |
| |
|
| | #### Inference: |
| | - Use the trained model to predict entity labels for new text inputs. |
| |
|
| | ### Key Technologies |
| | - **Deep Learning (BERT):** For advanced token classification and contextual understanding. |
| | - **Natural Language Processing (NLP):** For text preprocessing, tokenization, and entity recognition. |
| | - **Machine Learning Algorithms:** For model training and prediction tasks. |
| |
|
| | ## Streamlit App |
| | You can view and interact with the Streamlit app for token classification [here](https://huggingface.co/spaces/AdilHayat173/token_classifcation). |
| |
|
| | ## Examples |
| | Here are some examples of outputs from the model: |
| |
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| |  |
| |  |
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|
| | ## Google Colab Notebook |
| | You can view and run the Google Colab notebook for this project [here](https://colab.research.google.com/drive/1GYVlIToQ_lnT8XEjGrR2WFkUQWpWXgQi#scrollTo=ZlyX1Lgn8gjj). |
| |
|
| | ## Acknowledgements |
| | - Hugging Face for transformer models and libraries. |
| | - Streamlit for creating the interactive web interface. |
| | - [Your Dataset Provider] for the token classification dataset. |
| |
|
| | ## Author |
| | - AdilHayat |
| | - [Hugging Face Profile](https://huggingface.co/AdilHayat173) |
| | - [GitHub Profile](https://github.com/AdilHayat21173) |
| |
|
| | ## Feedback |
| | If you have any feedback, please reach out to us at hayatadil300@gmail.com. |
| |
|
