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# **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-roberta`** model. The fill-mask task predicts the most likely token(s) to replace the `[MASK]` token in a given sentence.
---
## **Usage**
### **1. Import Dependencies**
```python
from transformers import pipeline, AutoTokenizer
```
### **2. Initialize the Model and Tokenizer**
Load the tokenizer and model pipeline:
```python
# Initialisation
tokenizer = AutoTokenizer.from_pretrained('jefson08/kha-roberta')
fill_mask = pipeline(
"fill-mask",
model="jefson08/kha-roberta",
tokenizer=tokenizer,
device="cuda", # Use "cuda" for GPU or omit for CPU
)
```
### **3. Predict the [MASK] Token**
Provide a sentence with a `[MASK]` token for prediction:
```python
# 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:
```plaintext
"Nga dei u briew u ba [MASK] bha."
```
The model might output:
```plaintext
[{'score': 0.09230164438486099,
'token': 6086,
'token_str': 'mutlop',
'sequence': 'Nga dei u briew u ba mutlop bha.'},
{'score': 0.051360130310058594,
'token': 2059,
'token_str': 'stad',
'sequence': 'Nga dei u briew u ba stad bha.'},
{'score': 0.045497000217437744,
'token': 1864,
'token_str': 'khuid',
'sequence': 'Nga dei u briew u ba khuid bha.'},
{'score': 0.04180142655968666,
'token': 668,
'token_str': 'kham',
'sequence': 'Nga dei u briew u ba kham bha.'},
{'score': 0.027332570403814316,
'token': 2817,
'token_str': 'khlaiñ',
'sequence': 'Nga dei u briew u ba khlaiñ bha.'}]
```
---
## **Model Information**
The `jefson08/kha-roberta` 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](https://huggingface.co/docs/transformers): Provides the pipeline and model-loading utilities.
- [PyTorch](https://pytorch.org/): Backend framework for running the model.
Install the dependencies with:
```bash
pip install transformers torch
```
---
## **Acknowledgements**
- Hugging Face [Transformers](https://huggingface.co/docs/transformers) library.
- Model by [N Donald Jefferson Thabah](https://huggingface.co/jefson08/kha-roberta).
---
## **License**
This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for more details.
--- |