Doron Adler
Added inference examples using the .pt and .onnx models
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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()