readme: ---
  language: ta
  datasets:
  - oscar
  - IndicNLP
  - Wiki-Tamil novels scrapped data
  
  widget:
  - text: 'ஆதித்த கரிகாலர் தஞ்சைக்குச் செல்ல உடனடியாக ஒப்புக்கொண்டார்.'
  
  - text: 'நந்தினி பெரிய பழுவேட்டரையரை உண்மையாக நேசித்தால் '
  
  - text: 'மதுராந்தகருக்கு இராஜ்யமாளும் விருப்பம் இருப்பதாக இல்லை '
  
  ---
  
  # GPT2-Kalki
  ## Model description
  GPT2-Kalki is a GPT-2 transformer model fine-tuned on corpus of Tamil language data from Wikipedia. Has been specifically finetuned on the works of [Kalki Krishnamurthy](https://en.wikipedia.org/wiki/Kalki_Krishnamurthy) - a Tamil writer from the 1900s.
  This model is an experimentation of "What if" scenarios using the characters of his novels. The famous movie that has been released now [Ponniyin Selvan - I](https://en.wikipedia.org/wiki/Ponniyin_Selvan:_I) is based on the novel written by the same author. 
  This model is trained on an already trained model on Tamil dataset from [GPT2-Tamil](https://huggingface.co/abinayam/gpt-2-tamil).
  
  ## 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/) and manually scrapped Wikipedia dataset specifically having stories and novels.
  The scrapped dataset will be released soon.
  
  ## Usage
  You can use this model for Tamil text generation:
  ```python
  >>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
  >>> tokenizer = AutoTokenizer.from_pretrained('tsaditya/GPT-Kalki')
  >>> model = AutoModelWithLMHead.from_pretrained('tsaditya/GPT-Kalki')
  >>> text = "ஆதித்த கரிகாலர் தஞ்சைக்குச் செல்ல உடனடியாக ஒப்புக்கொண்டார். "
  >>> encoded_text = tokenizer.encode(text, return_tensors='tf')
  >>> beam_output = model.generate(
      encoded_text,
      do_sample=True, 
      max_length=512, 
      top_k=50, 
      top_p=0.95, 
      num_return_sequences=1,
      no_repeat_ngram_size = 3,
      temperature = 0.7
      )
  >>> print(tokenizer.decode(beam_output[0], skip_special_tokens=True))
  ```
  ---