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Pre-CoFactv3-Question-Answering

Question Answering
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deberta-v2
Pre-CoFactv3
Question Answering
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Pre-CoFactv3-Question-Answering
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---
license: mit
language: en
tags:
- Pre-CoFactv3
- Question Answering
datasets:
- FACTIFY5WQA
metrics:
- bleu
pipeline_tag: question-answering
library_name: transformers
base_model: microsoft/deberta-v3-large
widget:
- text: "Who spent an entire season at aston vila without playing a single game?"
  context: "Micah Richards spent an entire season at Aston Vila without playing a single game."
  example_title: "Claim"
- text: "Who spent an entire season at aston vila without playing a single game?"
  context: "Despite speculation that Richards would leave Aston Villa before the transfer deadline for the 2018~19 season , he remained at the club , although he is not being considered for first team selection."
  example_title: "Evidence"
---

# Pre-CoFactv3-Question-Answering

## Model description

This is a Question Answering model for **AAAI 2024 Workshop Paper: “Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning”**

Its input are question and context, and output is the answers derived from the context. It is fine-tuned by **FACTIFY5WQA** dataset based on [**microsoft/deberta-v3-large**](https://huggingface.co/microsoft/deberta-v3-large) model.

For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/Pre-CoFactv3).

## How to use?

1. Download the model by hugging face transformers.
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model = AutoModelForQuestionAnswering.from_pretrained("AndyChiang/Pre-CoFactv3-Question-Answering")
tokenizer = AutoTokenizer.from_pretrained("AndyChiang/Pre-CoFactv3-Question-Answering")
```

2. Create a pipeline.
```python
QA = pipeline("question-answering", model=model, tokenizer=tokenizer)
```

3. Use the pipeline to answer the question by context.
```python
QA_input = {
    'context': "Micah Richards spent an entire season at Aston Vila without playing a single game.",
    'question': "Who spent an entire season at aston vila without playing a single game?",
}
answer = QA(QA_input)
print(answer)
```

## Dataset

We utilize the dataset FACTIFY5WQA provided by the AAAI-24 Workshop Factify 3.0.

This dataset is designed for fact verification, with the task of determining the veracity of a claim based on the given evidence.

- **claim:** the statement to be verified.
- **evidence:** the facts to verify the claim.
- **question:** the questions generated from the claim by the 5W framework (who, what, when, where, and why).
- **claim_answer:** the answers derived from the claim.
- **evidence_answer:** the answers derived from the evidence.
- **label:** the veracity of the claim based on the given evidence, which is one of three categories: Support, Neutral, or Refute.

|  | Training | Validation | Testing | Total |
| --- | --- | --- | --- | --- |
| Support | 3500 | 750 | 750 | 5000 |
| Neutral | 3500 | 750 | 750 | 5000 |
| Refute | 3500 | 750 | 750 | 5000 |
| Total | 10500 | 2250 | 2250 | 15000 |

## Fine-tuning

Fine-tuning is conducted by the Hugging Face Trainer API on the [Question Answering](https://huggingface.co/docs/transformers/tasks/question_answering) task.

### Training hyperparameters

The following hyperparameters were used during training:

- Pre-train language model: [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)
- Optimizer: adam
- Learning rate: 0.00001
- Max length of input: 3200
- Batch size: 4
- Epoch: 3
- Device: NVIDIA RTX A5000

## Testing

We employ BLEU scores for both claim answer and evidence answer, taking the average of the two as the metric.

| Claim Answer | Evidence Answer | Average |
| ----- | ----- | ----- |
| 0.5248 | 0.3963 | 0.4605 |

## Other models

[AndyChiang/Pre-CoFactv3-Text-Classification](https://huggingface.co/AndyChiang/Pre-CoFactv3-Text-Classification)

## Citation