Update README.md

README.md

@@ -9,4 +9,35 @@ tags:
 - medical
 ---
 
-This is a finetuned version of [gamino/wiki_medical_terms](https://huggingface.co/datasets/gamino/wiki_medical_terms)
+This is a finetuned version of [gamino/wiki_medical_terms](https://huggingface.co/datasets/gamino/wiki_medical_terms)
+
+## Model description
+
+GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This
+means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
+of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
+it was trained to guess the next word in sentences.
+
+More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
+shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
+predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens.
+
+This way, the model learns an inner representation of the English language that can then be used to extract features
+useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
+prompt.
+
+### To use this model
+
+```python
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM
+>>> model_name = "Sharathhebbar24/chat_gpt2_dpo"
+>>> model = AutoModelForCausalLM.from_pretrained(model_name)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+>>> def generate_text(prompt):
+>>>  inputs = tokenizer.encode(prompt, return_tensors='pt')
+>>>  outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
+>>>  generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+>>>  return generated[:generated.rfind(".")+1]
+>>> prompt = "What is Paracetemol"
+>>> res = generate_text(prompt)
+>>> res