Text Classification, Named Entity Recognition, and Transformers Overview Documentation

NLP/Algorithms/Named_Entity_Recognition/README.md

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+# Named Entity Recognition (NER) Project
+
+This project demonstrates a basic Named Entity Recognition (NER) algorithm using Python and the `spacy` library. The goal is to identify named entities in text and classify them into predefined categories.
+
+## Directory Structure
+
+```
+ner_project/
+├── data/
+│   └── ner_data.txt
+├── models/
+│   └── ner_model
+├── preprocess.py
+├── train.py
+└── recognize.py
+└── README.md
+```
+
+- **data/ner_data.txt**: Contains the dataset used for training the NER model.
+- **models/ner_model**: Stores the trained NER model.
+- **preprocess.py**: Contains the code for preprocessing the text data.
+- **train.py**: Script for training the NER model.
+- **recognize.py**: Script for recognizing named entities in new text using the trained model.
+- **README.md**: Project documentation.
+
+## Dataset
+
+The dataset (`ner_data.txt`) contains sentences and their corresponding entity labels in the IOB format. Each line contains a word and its label, separated by a space. Sentences are separated by blank lines.
+
+## Preprocessing
+
+The `preprocess.py` file contains functions to preprocess the text data. It reads the dataset and converts it into a format suitable for training with `spacy`.
+
+## Training the Model
+
+The `train.py` script is used to train the NER model. It performs the following steps:
+
+1. Load a blank English model.
+2. Create the NER pipeline component and add it to the pipeline.
+3. Add labels to the NER component.
+4. Load the training data.
+5. Train the model using the training data.
+6. Save the trained model to `models/ner_model`.
+
+### Running the Training Script
+
+To train the model, run:
+```bash
+python train.py
+```
+
+## Recognizing Named Entities
+
+The `recognize.py` script is used to recognize named entities in new text using the trained model. It performs the following steps:
+
+1. Load the trained model.
+2. Process the input text.
+3. Print the recognized entities and their labels.
+
+### Running the Recognition Script
+
+To recognize named entities in new text, run:
+```bash
+python recognize.py
+```
+
+## Dependencies
+
+The project requires the following Python libraries:
+
+- spacy
+
+You can install the dependencies using:
+```bash
+pip install spacy
+```
+
+## Example Usage
+
+```python
+# Example usage of the recognize.py script
+if __name__ == "__main__":
+    text = "I love programming in Python. Machine learning is fascinating. Spacy is a useful library."
+    recognize_entities(text)
+```
+
+This project provides a basic implementation of Named Entity Recognition using the `spacy` library. You can expand it by using more advanced models or preprocessing techniques based on your requirements.