README.md

# Cosmos QA (gpt2) 
> This is part of the
[Flax/Jax Community Week](https://discuss.huggingface.co/t/train-a-gpt2-model-for-contextual-common-sense-reasoning-using-the-cosmos-qa-dataset/7463), organized by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google.

## Team Members
-Rohan V Kashyap ([Rohan](https://huggingface.co/Rohan))
-Vivek V Kashyap ([Vivek](https://huggingface.co/Vivek))

##Dataset

[Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning](https://huggingface.co/datasets/cosmos_qa).This dataset contains a set of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions.Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly.

###Example

```json
{"Context":["It's a very humbling experience when you need someone
to dress you every morning, tie your shoes, and put your hair
up. Every menial task takes an unprecedented amount of effort.
It made me appreciate Dan even more. But anyway I shan't
dwell on this (I'm not dying after all) and not let it detract from
my lovely 5 days with my friends visiting from Jersey."],

"Question":["What's a possible reason the writer needed someone to
dress him every morning?"],

"Multiple Choice":["A: The writer doesn't like putting effort into these tasks.",
"B: The writer has a physical disability.",
"C: The writer is bad at doing his own hair.",
"D: None of the above choices."]
"link":"https://arxiv.org/pdf/1909.00277.pdf"
}
```

##How to use

```bash
# Installing requirements
pip install transformers
pip install datasets 
```

```python
from model_file import FlaxGPT2ForMultipleChoice
from datasets import Dataset
model_path="flax-community/gpt2-Cosmos"
model = FlaxGPT2ForMultipleChoice.from_pretrained(model_path,input_shape=(1,4,1))

dataset=Dataset.from_csv('./')

def preprocess(example):
    example['context&question']=example['context']+example['question']
    example['first_sentence']=[example['context&question'],example['context&question'],example['context&question'],example['context&question']]
    example['second_sentence']=example['answer0'],example['answer1'],example['answer2'],example['answer3']
    return example
    
dataset=dataset.map(preprocess)

def tokenize(examples):
    a=tokenizer(examples['first_sentence'],examples['second_sentence'],padding='max_length',truncation=True,max_length=256,return_tensors='jax')
    a['labels']=examples['label']
    return a
    
dataset=dataset.map(tokenize)

input_id=jnp.array(dataset['input_ids'])
att_mask=jnp.array(dataset['attention_mask'])

outputs=model(input_id,att_mask)

final_output=jnp.argmax(outputs,axis=-1)

print(f"the predction of the dataset : {final_output}")
```

```
The Correct answer:-Option 1 
```

## Evaluation

The following tables summarize the scores obtained by the **GPT2-CosmosQA**.

|      Model      |  Dev Acc | Test Acc  | 
|:---------------:|:-----:|:-----:|
|  GPT-FT        |  54.0 | 54.4. |
| GPT2-CosmosQA  | 60.3 | 59.7 |