Quantization made by Richard Erkhov.
distilgpt2-base-pretrained-he - GGUF
- Model creator: https://huggingface.co/Norod78/
- Original model: https://huggingface.co/Norod78/distilgpt2-base-pretrained-he/
Original model description:
language: he
thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg widget:
- text: "ืืืืฉ ืืืืจืื ืขืื ืืืืืช ืืฉื ืืื ืืืืจื ืืฉืืคืชืข ื ืฉืืขื ื ืงืืฉื"
- text: "ืฉืืื, ืงืจืืืื ืื"
- text: "ืืืจื ืคืืืจ ืืืื ืืืื ื ืืื"
- text: "ืืืชืื ืฉืื ืืืื ืืืื ื"
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 Program. Then was further fine-tuned on GPU.
Dataset
oscar (unshuffled deduplicated he) - Homepage | Dataset Permalink
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
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
- 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
- Further training was performed on GPU
Usage
Simple usage sample code
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
def main():
model_name="Norod78/distilgpt2-base-pretrained-he"
prompt_text = "ืฉืืื, ืงืืจืืื ืื"
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()
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