Khasi Fill-Mask Model

This project demonstrates how to use the Hugging Face Transformers library to perform a fill-mask task using the jefson08/kha-bert model. The fill-mask task predicts the most likely token(s) to replace the [MASK] token in a given sentence.

Usage

1. Import Dependencies

from transformers import pipeline

2. Initialize the Model and Tokenizer

Load the tokenizer and model pipeline:

# Initialisation
fill_mask = pipeline(
    "fill-mask",
    model="jefson08/kha-bert",
    tokenizer='jefson08/kha-bert'
)

3. Predict the [MASK] Token

Provide a sentence with a [MASK] token for prediction:

# Predict [MASK] token
sentence = "Nga dei u briew u ba [MASK] bha."
predictions = fill_mask(sentence)

# Display predictions
for prediction in predictions:
    print(f"{prediction['sequence']} (score: {prediction['score']:.4f})")

Example Output

Given the input sentence:

"Nga dei u briew u ba [MASK] bha."

The model might output:

[{'score': 0.05552137270569801,
  'token': 668,
  'token_str': 'kham',
  'sequence': 'Nga dei u briew u ba  kham bha.'},
 {'score': 0.03611050173640251,
  'token': 2318,
  'token_str': 'kmen',
  'sequence': 'Nga dei u briew u ba  kmen bha.'},
 {'score': 0.029321255162358284,
  'token': 3612,
  'token_str': 'tbit',
  'sequence': 'Nga dei u briew u ba  tbit bha.'},
 {'score': 0.028406640514731407,
  'token': 1860,
  'token_str': 'ieit',
  'sequence': 'Nga dei u briew u ba  ieit bha.'},
 {'score': 0.027690021321177483,
  'token': 4187,
  'token_str': 'sarong',
  'sequence': 'Nga dei u briew u ba  sarong bha.'}]

Model Information

The jefson08/kha-bert model is fine-tuned for Khasi text tasks. It uses the fill-mask pipeline to predict and replace [MASK] tokens in sentences, providing insights into contextual language understanding.

Dependencies

Transformers: Provides the pipeline and model-loading utilities.
PyTorch: Backend framework for running the model.

Install the dependencies with:

pip install transformers torch

Acknowledgements

Hugging Face Transformers library.
Model by N Donald Jefferson Thabah.

License

This project is licensed under the MIT License. See the LICENSE file for more details.