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--- |
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language: |
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- da |
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pipeline_tag: text-generation |
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widget: |
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- text: "### Bruger:\nAnders\n\n### Anmeldelse:\nUmuligt at komme igennem på telefonen.\n\n### Svar:\nKære Anders\n" |
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--- |
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# What is this? |
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A fine-tuned GPT-2 model (small version, 124 M parameters) for generating responses to customer reviews in Danish. |
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# How to use |
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The model is based on the [gpt2-small-danish model](https://huggingface.co/KennethTM/gpt2-small-danish). Supervised fine-tuning is applied to adapt the model to generate responses to customer reviews in Danish. A prompting template is applied to the examples used to train (see the example below). |
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Test the model using the pipeline from the [🤗 Transformers](https://github.com/huggingface/transformers) library: |
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```python |
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from transformers import pipeline |
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generator = pipeline("text-generation", model = "KennethTM/gpt2-small-danish-review-response") |
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def prompt_template(user, review): |
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return f"### Bruger:\n{user}\n\n### Anmeldelse:\n{review}\n\n### Svar:\nKære {user}\n" |
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prompt = prompt_template(user = "Anders", review = "Umuligt at komme igennem på telefonen.") |
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text = generator(prompt) |
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print(text[0]["generated_text"]) |
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``` |
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Or load it using the Auto* classes: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-small-danish-review-response") |
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model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-small-danish-review-response") |
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``` |
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# Notes |
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The model may get the sentiment of the review wrong resulting in a mismatch between the review and response. The model would probably benefit from sentiment tuning. |
