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---
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 |