---
license: cc-by-4.0
base_model: kxx-kkk/FYP_sq2_mrqa_adqa_synqa
tags:
- generated_from_trainer
model-index:
- name: FYP_qa_final
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad_v2
      type: squad_v2
      config: squad_v2
      split: validation
    metrics:
    - type: exact_match
      value: 82.3
      name: Exact Match
    - type: f1
      value: 85.7701063996245 
      name: F1
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: squad
      type: squad
      config: plain_text
      split: validation
    metrics:
    - type: exact_match
      value: 89.9
      name: Exact Match
    - type: f1
      value: 93.57935153408677
      name: F1
datasets:
- rajpurkar/squad_v2
- mrqa
- UCLNLP/adversarial_qa
- mbartolo/synQA
language:
- en
pipeline_tag: question-answering
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# FYP_qa_final

This model is a fine-tuned version of [deepset/deberta-v3-base-squad2](https://huggingface.co/deepset/deberta-v3-base-squad2) on an [MRQA](https://huggingface.co/datasets/mrqa) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7493

## Model description

This model is trained for performing extractive question-answering tasks for academic essays. 

## Intended uses & limitations

More information needed

## Training and evaluation data

The dataset used for training is listed below according to training sequences:
1. [MRQA(train split)](https://huggingface.co/datasets/mrqa)
2. [UCLNLP/adversarial_qa](https://huggingface.co/datasets/UCLNLP/adversarial_qa)
3. [mbartolo/synQA](https://huggingface.co/datasets/mbartolo/synQA)
4. [MRQA(test split)](https://huggingface.co/datasets/mrqa)*This model

## Training procedure

The training approach uses the fine-tuning approach of transfer learning on the pre-trained model to perform NLP QA tasks. 

Each time a model was trained with one dataset only and saved as the PTMs for the next training. 

This model is the last model that trained with [MRQA(test split)](https://huggingface.co/datasets/mrqa). 

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.8084        | 0.48  | 300  | 3.1468          |
| 2.5707        | 0.96  | 600  | 2.9035          |
| 2.5187        | 1.44  | 900  | 2.7175          |
| 2.4463        | 1.91  | 1200 | 2.7497          |
| 2.4328        | 2.39  | 1500 | 2.7229          |
| 2.3839        | 2.87  | 1800 | 2.7493          |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2