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Model: Norod78/distilgpt2-base-pretrained-he
Source: Original Platform

README.md

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+---
+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()
+```