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MRC_ER_mdeberta-v3-base_syl_DSC
tringuyen-uit/MRC_ER_mdeberta-v3-base_syl_DSC
MRC_ER_mdeberta-v3-base_syl_DSC is a question answering model from tringuyen-uit. Use it when the input is a question plus a passage. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Question Answering
- Library
- transformers
- License
- mit
- Model type
- deberta-v2
- Access
- Public
- Created
- Jun 5, 2024
- Updated
- Jun 6, 2024
- SHA
- dcd3661a
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
MRC_ER_mdeberta-v3-base_syl_DSC
This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1310
- Exact Match: 0.4978
- F1: 0.6128
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 |
|---|---|---|---|---|---|
| 0.7262 | 1.0 | 3182 | 1.2386 | 0.4634 | 0.5997 |
| 0.5564 | 2.0 | 6364 | 1.3796 | 0.4868 | 0.6083 |
| 0.4338 | 3.0 | 9546 | 1.5470 | 0.4864 | 0.6109 |
| 0.3148 | 4.0 | 12728 | 1.8582 | 0.5041 | 0.6141 |
| 0.2554 | 5.0 | 15910 | 2.1310 | 0.4978 | 0.6128 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Question Answering
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Jun 5, 2024
- Updated
- Jun 6, 2024