This repository contains a Part-of-Speech (POS) tagging system that combines the powerful BERT (Bidirectional Encoder Representations from Transformers) model with a Conditional Random Field (CRF) layer to improve the tagging accuracy by leveraging contextual information more effectively.

The BERT-CRF model integrates a pre-trained BERT model with a CRF layer. BERT provides deep contextualized embeddings that capture complex syntactic and semantic information from text, while the CRF layer models the dependencies between tags in sequences, improving the coherence of predictions.

1. BERT Model: Extracts rich feature representations for each token.
2. Dropout Layer: Reduces overfitting during training by randomly setting input elements to zero.
3. Linear Layer: Maps the BERT output features to the space of tag labels.
4. CRF Layer: Models the tag sequence, taking into account the conditional probabilities of tags given their neighbors in the sequence.

• Python 3.8+
• PyTorch
• HuggingFace Transformers
• TorchCRF
• sklearn

Set up a virtual environment and install the required packages:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install torch transformers sklearn TorchCRF
pip install -r requirements.txt

The training and validation data should be formatted as follows, with each word and its tag separated by a tab, and sentences separated by a newline:

word2\ttag2
word3\ttag3

word1\ttag1
word2\ttag2
word3\ttag3

To train the model, run the script bert_crf.py

Modify model to change model parameters, training settings, or to integrate custom configurations for the BERT and CRF components.

The script will output the model's performance on the validation dataset including metrics such as accuracy, precision, recall, and F1-score. It will also save the trained model for later use.