chat_gpt2 / README.md

Adding Evaluation Results (#1)

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- gpt2
	datasets:
	- HuggingFaceH4/ultrachat_200k
	pipeline_tag: text-generation
	model-index:
	- name: chat_gpt2
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: AI2 Reasoning Challenge (25-Shot)
	type: ai2_arc
	config: ARC-Challenge
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: acc_norm
	value: 23.04
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HellaSwag (10-Shot)
	type: hellaswag
	split: validation
	args:
	num_few_shot: 10
	metrics:
	- type: acc_norm
	value: 30.76
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU (5-Shot)
	type: cais/mmlu
	config: all
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 24.39
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: TruthfulQA (0-shot)
	type: truthful_qa
	config: multiple_choice
	split: validation
	args:
	num_few_shot: 0
	metrics:
	- type: mc2
	value: 39.81
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Winogrande (5-shot)
	type: winogrande
	config: winogrande_xl
	split: validation
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 49.96
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GSM8k (5-shot)
	type: gsm8k
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 0.0
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2
	name: Open LLM Leaderboard
	---

	This model is a finetuned version of ```gpt2``` using ```HuggingFaceH4/ultrachat_200k```

	## 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"
	>>> 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 = """
	>>> user: what are you?
	>>> assistant: I am a Chatbot intended to give a python program
	>>> user: hmm, can you write a python program to print Hii Heloo
	>>> assistant: Sure Here is a python code.\n print("Hii Heloo")
	>>> user: Can you write a Linear search program in python
	>>> """
	>>> res = generate_text(prompt)
	>>> res
	```
	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__chat_gpt2)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|27.99\|
	\|AI2 Reasoning Challenge (25-Shot)\|23.04\|
	\|HellaSwag (10-Shot) \|30.76\|
	\|MMLU (5-Shot) \|24.39\|
	\|TruthfulQA (0-shot) \|39.81\|
	\|Winogrande (5-shot) \|49.96\|
	\|GSM8k (5-shot) \| 0.00\|