This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
To setup the project, run the following command,
pip install -r requirements.txt
Pretrained model on Tamil language using a causal language modeling (CLM) objective.
The GTP-2 model is trained on oscar dataset - ta and IndicNLP dataset - ta
Intended uses & limitations:
You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
How to pretrain the model:
To perform training, do the following steps,
- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
- Create the config.json by running the following command,
- Create the tokenizer by running the following command,
- Once the config and tokenizer is created, run the following script to start training the flax model
How to use:
To perform language generation using the model, pipeline can be used directly.
- First convert the flax model to pytorch using the following command,
- Use the following snippet to perform language generation,
>>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline >>> model_name = 'abinayam/gpt-2-tamil' >>> model = AutoModelWithLMHead.from_pretrained(model_name) >>> tokenizer = AutoTokenizer.from_pretrained(model_name) >>> set_seed(42) >>> input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு" >>> max_len = 300 >>> no_seq = 5 >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer) >>> sequence = generator(input_text, max_length=max_len, num_return_sequences=no_seq)
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This model can be loaded on the Inference API on-demand.