readme: ---
  language: he
  
  thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
  widget:
  - text: "האיש האחרון עלי אדמות ישב לבד בחדרו כשלפתע נשמעה נקישה"
  - text: "שלום, קרואים לי"
  - text: "הארי פוטר חייך חיוך נבוך"
  - text: "החתול שלך מאוד חמוד ו"
  
  license: mit
  ---
  
  # distilgpt2-base-pretrained-he
  
  A tiny GPT2 based Hebrew text generation model initially trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. Then was further fine-tuned on GPU.
  
  ## Dataset
  
  ### oscar (unshuffled deduplicated he) - [Homepage](https://oscar-corpus.com) | [Dataset Permalink](https://huggingface.co/datasets/viewer/?dataset=oscar&config=unshuffled_deduplicated_he)
  
  The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.
  
  ### CC-100 (he) - [HomePage](https://data.statmt.org/cc-100/)
  
  This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.
  
  ### Misc
  * Hebrew Twitter
  * Wikipedia
  * Various other sources
  
  ## Training
  
  * Done on a TPUv3-8 VM using [Huggingface's clm-flax example script](https://github.com/huggingface/transformers/blob/master/examples/flax/language-modeling/run_clm_flax.py) <BR>
  * I have made a list of items which might make it easier for other to use this script. The list was posted to [This discussion forum](https://discuss.huggingface.co/t/ideas-for-beginner-friendlier-tpu-vm-clm-training/8351)
  * Further training was performed on GPU
  
  ## Usage
  
  
  #### Simple usage sample code
  
  ```python
  
  from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
  
  def main():
      model_name="Norod78/distilgpt2-base-pretrained-he"
  
      prompt_text = "שלום, קוראים לי"
      generated_max_length = 192
  
      print("Loading model...")
      model =  AutoModelForCausalLM.from_pretrained(model_name)
      print('Loading Tokenizer...')
      tokenizer = AutoTokenizer.from_pretrained(model_name)
      text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
  
      print("Generating text...")
      result = text_generator(prompt_text, num_return_sequences=1, batch_size=1, do_sample=True, top_k=40, top_p=0.92, temperature = 1, repetition_penalty=5.0, max_length = generated_max_length)
  
      print("result = " + str(result))
  
  if __name__ == '__main__':
      main()
  ```
  