FYP_qa_final
This model is a fine-tuned version of deepset/deberta-v3-base-squad2 on an 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:
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).
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
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Finetuned from
Datasets used to train kxx-kkk/FYP_qa_final
Space using kxx-kkk/FYP_qa_final 1
Evaluation results
- Exact Match on squad_v2validation set self-reported82.300
- F1 on squad_v2validation set self-reported85.770
- Exact Match on squadvalidation set self-reported89.900
- F1 on squadvalidation set self-reported93.579