---
datasets:
- mc4
language:
- ka
library_name: transformers
tags:
- general
widget:
- text: "ქართული [MASK] სწავლა საკმაოდ რთულია"
  example_title: "Georgian Language"
- text: "საქართველოს [MASK] ნაკრები ერთა ლიგაზე კარგად ასპარეზობს"
  example_title: "Football"
- text: "ქართული ღვინო განთქმულია [MASK] მსოფლიოში"
  example_title: "Wine"
---

# General Georgian Language Model

This language model is a pretrained model specifically designed to understand and generate text in the Georgian language. It is based on the DistilBERT-base-uncased architecture and has been trained on the MC4 dataset, which contains a large collection of Georgian web documents.

## Model Details

- **Architecture**: DistilBERT-base-uncased
- **Pretraining Corpus**: MC4 (Multilingual Crawl Corpus)
- **Language**: Georgian

## Pretraining

The model has undergone a pretraining phase using the DistilBERT architecture, which is a distilled version of the original BERT model. DistilBERT is known for its smaller size and faster inference speed while still maintaining a high level of performance.

During pretraining, the model was exposed to a vast amount of preprocessed Georgian text data from the MC4 dataset.

## Usage

To use the General Georgian Language Model, you can utilize the model through various natural language processing (NLP) tasks, such as:

- Text classification
- Named entity recognition
- Sentiment analysis
- Language generation

You can fine-tune this model on specific downstream tasks using task-specific datasets or use it as a feature extractor for transfer learning.

## Example Code

Here is an example of how to use the General Georgian Language Model using the Hugging Face `transformers` library in Python:

```python
from transformers import AutoTokenizer, TFAutoModel
from transformers import pipeline

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Davit6174/georgian-distilbert-mlm")
model = TFAutoModel.from_pretrained("Davit6174/georgian-distilbert-mlm")

# Build pipeline
mask_filler = pipeline(
    "fill-mask", model=model, tokenizer=tokenizer
)

text = 'ქართული [MASK] სწავლა საკმაოდ რთულია'

# Generate model output
preds = mask_filler(text)

# Print top 5 predictions
for pred in preds:
    print(f">>> {pred['sequence']}")
```

## Limitations and Considerations
- The model's performance may vary across different downstream tasks and domains.
- The model's understanding of context and nuanced meanings may not always be accurate.
- The model may generate plausible-sounding but incorrect or nonsensical Georgian text.
- Therefore, it is recommended to evaluate the model's performance and fine-tune it on task-specific datasets when necessary.

## Acknowledgments
The Georgian Language Model was pretrained using the Hugging Face transformers library and trained on the MC4 dataset, which is maintained by the community. I would like to express my gratitude to the contributors and maintainers of these valuable resources.