Update README.md

README.md

@@ -10,16 +10,34 @@ tags:
 
 This model is a finetuned version of ```gpt2-medium``` using ```databricks/databricks-dolly-15k dataset```
 
+## 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,
+shifted one token (word or piece of word) to the right. The model uses a mask mechanism to make sure the
+predictions for the token `i` only uses 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.
+
 
 ```python
->>> from transformers import pipeline, set_seed
->>> generator = pipeline('text-generation', model='Sharathhebbar24/Instruct_GPT')
->>> set_seed(42)
->>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
-
-[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
- {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
- {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
- {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
- {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
+>>> from transformers import AutoTokenizer, AutoModelForCausalLM
+>>> model_name = "Sharathhebbar24/Instruct_GPT"
+>>> model = AutoModelForCausalLM.from_pretrained(model_name)
+>>> tokenizer = AutoTokenizer.from_pretrained("gpt2-medium")
+>>> def generate_text(prompt):
+>>>  inputs = tokenizer.encode(prompt, return_tensors='pt')
+>>>  outputs = mod1.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]
+
+>>> generate_text("Should I Invest in stocks")
+
+Should I Invest in stocks? Investing in stocks is a great way to diversify your portfolio.  You can invest in stocks based on the market's performance, or you can invest in stocks based on the company's performance."
 ```