Created model card

README.md

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+---
+language: he
+
+thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg
+widget:
+- text: "עוד בימי קדם"
+- text: "קוראים לי דורון ואני מעוניין ל"
+- text: "קוראים לי איציק ואני חושב ש"
+- text: "החתול שלך מאוד חמוד ו"
+
+license: mit
+---
+
+# hebrew-distilgpt2
+
+A tiny GPT2 based Hebrew text generation model trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
+
+## 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.
+
+## 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)
+
+## Usage
+
+
+#### Simple usage sample code
+
+```python
+
+#pip install tokenizers==0.10.3 transformers==4.8.0
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+  
+tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-distilgpt2")
+model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-distilgpt2", pad_token_id=tokenizer.eos_token_id)
+
+prompt_text = "אני אוהב שוקולד ועוגות"
+max_len = 512
+sample_output_num = 3
+seed = 1000
+
+import numpy as np
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()
+
+print(f"device: {device}, n_gpu: {n_gpu}")
+
+np.random.seed(seed)
+torch.manual_seed(seed)
+if n_gpu > 0:
+    torch.cuda.manual_seed_all(seed)
+
+model.to(device)
+
+encoded_prompt = tokenizer.encode(
+    prompt_text, add_special_tokens=False, return_tensors="pt")
+
+encoded_prompt = encoded_prompt.to(device)
+
+if encoded_prompt.size()[-1] == 0:
+        input_ids = None
+else:
+        input_ids = encoded_prompt
+
+print("input_ids = " + str(input_ids))
+
+if input_ids != None:
+  max_len += len(encoded_prompt[0])
+  if max_len > 1024:
+    max_len = 1024
+
+print("Updated max_len = " + str(max_len))
+
+stop_token = "<|endoftext|>"
+new_lines = "\n\n\n"
+
+sample_outputs = model.generate(
+    input_ids,
+    do_sample=True, 
+    max_length=max_len, 
+    top_k=50, 
+    top_p=0.95, 
+    num_return_sequences=sample_output_num
+)
+
+print(100 * '-' + "\n\t\tOutput\n" + 100 * '-')
+for i, sample_output in enumerate(sample_outputs):
+
+  text = tokenizer.decode(sample_output, skip_special_tokens=True)
+  
+  # Remove all text after the stop token
+  text = text[: text.find(stop_token) if stop_token else None]
+
+  # Remove all text after 3 newlines
+  text = text[: text.find(new_lines) if new_lines else None]
+
+  print("\n{}: {}".format(i, text))
+  print("\n" + 100 * '-')
+
+```