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.
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.
- Hebrew Twitter
- Various other sources
- 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
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()
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