Updated model card

README.md

@@ -1,13 +1,58 @@
 # GPT2-Tamil
 
-This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pre-train a language model using GPT-2 specifically for Tamil language.
+This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to train a language model using GPT-2 specifically for Tamil language.
 
-## Setup [Todo]:
+language:
+- ta
+tags:
+- text-generation
+license: MIT
+datasets:
+- OSCAR
+- IndicNLP
+metrics:
+- Preplexity
+widget:
+- text: 'ஒரு ஊரிலே ஒரு காக்கைக்கு'
+
+## Setup:
+To setup the project, run the following command,
+``` pip install -r requirements.txt
+```
  
-## Dataset Used [Todo]:
+## Dataset Used:
+The GTP-2 model is trained using OSCAR (Tamil) and IndicNLP (Tamil) dataset
+
+## Train model:
+To perform training, do the following steps,
 
-## Preprocess Data [Todo]:
+- 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
+```
 
-## Train (Flax) [Todo]:
+## Inference:
+To perform language generation using the model,
 
-## Demo [Todo]:
+- 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)
+input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு"
+max_len = 300
+generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+sequence = generator(input_text, max_length=max_len)
+```