Original model description:

language: ta datasets:

oscar
IndicNLP widget:
text: 'ஒரு ஊரிலே ஒரு காக்கைக்கு'

GPT2-Tamil

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.

Setup:

To setup the project, run the following command,

pip install -r requirements.txt

Model:

Pretrained model on Tamil language using a causal language modeling (CLM) objective.

Dataset Used:

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.)

>>> export MODEL_DIR=<model_dir>

Create the config.json by running the following command,

>>> python src/create_config.py

Create the tokenizer by running the following command,

>>> python src/train_tokenizer.py

Once the config and tokenizer is created, run the following script to start training the flax model

>>> python scripts/train_gpt2-oscar-tamil.sh

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,

python src/convert_flax_to_pytorch.py

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)