Update README.md

README.md

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 # Tamil-Transformer
-
-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,
-```python
-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](https://huggingface.co/datasets/oscar) and [IndicNLP dataset - ta](https://indicnlp.ai4bharat.org/corpora/)
-
-## 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](https://huggingface.co/models?filter=gpt2) 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.)
-```python
->>> export MODEL_DIR=<model_dir>
-```
-- Create the config.json by running the following command,
-```python
->>> python src/create_config.py
-```
-- Create the tokenizer by running the following command,
-```python
->>> python src/train_tokenizer.py
-```
-- Once the config and tokenizer is created, run the following script to start training the flax model
-```python
->>> 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
-python src/convert_flax_to_pytorch.py
-```
-- Use the following snippet to perform language generation,
-```python
- >>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
- >>> model_name = 'Abinesh/Tamil-Transformer'
- >>> 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)
-```