metadata

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 - 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 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.

Dataset Used:

The GTP-2 model is trained on oscar dataset - ta and IndicNLP dataset - ta 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))