Update README.md

README.md

@@ -4,4 +4,42 @@ language:
 pipeline_tag: text-generation
 widget:
   - text: "### Bruger:\nAnders\n\n### Anmeldelse:\nUmuligt at komme igennem på telefonen.\n\n### Svar:\nKære Anders\n"
----
+---
+
+# What is this?
+
+A fine-tuned GPT-2 model (small version, 124 M parameters) for generating responses to customer reviews in Danish.
+
+# How to use
+
+The model is based on the [gpt2-small-danish model](https://huggingface.co/KennethTM/gpt2-small-danish). Then 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).
+
+Test the model using the pipeline from the [🤗 Transformers](https://github.com/huggingface/transformers) library:
+
+```python
+from transformers import pipeline
+
+generator = pipeline("text-generation", model = "KennethTM/gpt2-small-danish-review-response")
+
+def prompt_template(user, review):
+return f"### Bruger:\n{user}\n\n### Anmeldelse:\n{review}\n\n### Svar:\nKære {user}\n"
+
+prompt = prompt_template(user = "Anders", review = "Umuligt at komme igennem på telefonen.")
+
+text = generator(prompt)
+
+print(text[0]["generated_text"])
+```
+
+Or load it using the Auto* classes:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("KennethTM/gpt2-small-danish-review-response")
+model = AutoModelForCausalLM.from_pretrained("KennethTM/gpt2-small-danish-review-response")
+```
+
+# Notes
+
+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.