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--- |
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language: |
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- en |
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thumbnail: |
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widget: |
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- text: "topic climate source washington post title " |
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--- |
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# GPT2-medium-topic-news |
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## Model description |
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GPT2-medium fine tuned on a largish news corpus conditioned on a topic, source, title |
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## Intended uses & limitations |
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#### How to use |
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To generate a news article text conditioned on a topic, source, title or some subsets, prompt model with: |
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```python |
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f"topic {topic} source" |
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f"topic {topic} source {source} title" |
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f"topic {topic} source {source} title {title} body" |
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``` |
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Try the following tags for `topic: climate, weather, vaccination`. |
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Zero shot generation works pretty well as long as `topic` is a single word and not too specific. |
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```python |
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device = "cuda:0" |
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tokenizer = AutoTokenizer.from_pretrained("ktrapeznikov/gpt2-medium-topic-small-set") |
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model = AutoModelWithLMHead.from_pretrained("ktrapeznikov/gpt2-medium-topic-small-set") |
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model.to(device) |
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topic = "climate" |
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prompt = tokenizer(f"topic {topics} source straitstimes title", return_tensors="pt") |
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out = model.generate(prompt["input_ids"].to(device), do_sample=True,max_length=500, early_stopping=True, top_p=.9) |
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print(tokenizer.decode(out[0].cpu(), skip_special_tokens=True)) |
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``` |
