Named Entity Recognition In Malayalam

This repo implements and compare two models, namely a Bidirectional LSTM and TENER (Transformer Encoder for Named Entity Recognition) on the ai4bharat-IndicNER dataset,

Find live demo at https://tener-malayalam-k2i4cw0x9c.streamlit.app/

Pretrained Models

Please find the pretrained weights at: https://drive.google.com/drive/folders/13DQ7zTz8fiSTkwmScpd8ZuO5mVtC4y_C?usp=sharing

File Structure

• modeling_TENER.ipynb has the code for training and contains some results as well.
• models/ contains the code definitions for both the models.
• malayalam_ner.py implements a helper class that makes it easier to predict with either of the models.
• predict.py contains code for running inference on a single string.

Tokenization & Embedding

• Byte Pair Encoding has been used here.
• It was chosen after a comparison between it and fasttext.
• The tokens were also vectorized using it's vectorizer.
• It was taken from BPEmb, which has pretrained embedding models for over 275 languages.
• More details can be found here and for the specific one I used

The Models

1. Bidirectional LSTM

   • Uses 3 layers, with hidden size of 200.
   • Uses ReLU as the activation funcion.
   • Combines manually initialized weights and LayerNorm layers for numerical stability.

2. TENER

   • Employs an adaptation of TENER
   • Compared to the paper, the CRF layer at the end has been dropped.
   • Here, I have set d_model = 512 and n_heads = 16
   • A weight vector has been used in the loss function to address for the imbalance of tags in the dataset

Results

• The highest f1-score obtained with BiLSTM is 0.96 and lowest val_loss of 0.09

• The highest f1-score obtained with TENER is 0.98 and lowest val_loss of 0.05.

NOTE: This was on the test set provided with the dataset.

Caveats

• While testing I have observed that the model performs better on sentences from the test that the headlines or the title.