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--- |
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library_name: transformers |
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datasets: |
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- youngermax/text-tagging |
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--- |
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## Model Details |
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### Model Description |
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This model identifies multiple topics related to the text in natural language. It is finetuned on youngermax/text-tagging for 3.5 epoch over ~1.3 hours on a free Kaggle P100. |
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- **Developed by:** Lincoln Maxwell |
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- **Model type:** Generative Pretrained Transformer |
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- **Language(s) (NLP):** English |
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- **Finetuned from model:** DistilGPT2 |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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```python |
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input_ids = tokenizer.encode(prompt + '<|topic|>', return_tensors='pt').to('cuda') |
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# Generate text |
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output = model.generate( |
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input_ids, |
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max_length=1024, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id, |
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pad_token_id=tokenizer.eos_token_id, |
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top_k=100, |
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top_p=0.5, |
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temperature=1 |
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) |
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# Decode the output |
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text = tokenizer.decode(output[0], skip_special_tokens=False, early_stopping=True) |
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text = text[len(prompt):text.find('<|endoftext|>')] |
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topics = list(set(list(map(lambda x: x.strip(), text.split('<|topic|>')))[1:])) |
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``` |
