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---
license: mit
base_model: hongpingjun98/BioMedNLP_DeBERTa
tags:
- generated_from_trainer
datasets:
- sem_eval_2024_task_2
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: BioMedNLP_DeBERTa_all_updates
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sem_eval_2024_task_2
type: sem_eval_2024_task_2
config: sem_eval_2024_task_2_source
split: validation
args: sem_eval_2024_task_2_source
metrics:
- name: Accuracy
type: accuracy
value: 0.655
- name: Precision
type: precision
value: 0.6551396256630968
- name: Recall
type: recall
value: 0.655
- name: F1
type: f1
value: 0.6549223575304444
---
<!-- 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. -->
# BioMedNLP_DeBERTa_all_updates
This model is a fine-tuned version of [hongpingjun98/BioMedNLP_DeBERTa](https://huggingface.co/hongpingjun98/BioMedNLP_DeBERTa) on the sem_eval_2024_task_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5118
- Accuracy: 0.655
- Precision: 0.6551
- Recall: 0.655
- F1: 0.6549
## 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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 9 | 0.6482 | 0.62 | 0.6403 | 0.62 | 0.6058 |
| 0.7604 | 2.0 | 18 | 0.6376 | 0.635 | 0.6515 | 0.635 | 0.6248 |
| 0.7485 | 3.0 | 27 | 0.6256 | 0.655 | 0.6672 | 0.655 | 0.6486 |
| 0.7114 | 4.0 | 36 | 0.6188 | 0.675 | 0.6790 | 0.675 | 0.6732 |
| 0.6906 | 5.0 | 45 | 0.6181 | 0.705 | 0.7050 | 0.705 | 0.7050 |
| 0.5355 | 6.0 | 54 | 0.6257 | 0.68 | 0.6803 | 0.6800 | 0.6799 |
| 0.5411 | 7.0 | 63 | 0.6258 | 0.675 | 0.6754 | 0.675 | 0.6748 |
| 0.4849 | 8.0 | 72 | 0.6376 | 0.665 | 0.6670 | 0.665 | 0.6640 |
| 0.4386 | 9.0 | 81 | 0.6507 | 0.68 | 0.6826 | 0.6800 | 0.6788 |
| 0.3565 | 10.0 | 90 | 0.6631 | 0.685 | 0.6850 | 0.685 | 0.6850 |
| 0.3565 | 11.0 | 99 | 0.7089 | 0.66 | 0.6616 | 0.6600 | 0.6591 |
| 0.2992 | 12.0 | 108 | 0.7791 | 0.67 | 0.6717 | 0.6700 | 0.6692 |
| 0.2092 | 13.0 | 117 | 0.8224 | 0.68 | 0.6803 | 0.6800 | 0.6799 |
| 0.1643 | 14.0 | 126 | 0.9128 | 0.675 | 0.6750 | 0.675 | 0.6750 |
| 0.0811 | 15.0 | 135 | 1.0458 | 0.67 | 0.6701 | 0.67 | 0.6700 |
| 0.0502 | 16.0 | 144 | 1.2061 | 0.67 | 0.6701 | 0.67 | 0.6700 |
| 0.011 | 17.0 | 153 | 1.3763 | 0.655 | 0.6558 | 0.655 | 0.6546 |
| 0.0261 | 18.0 | 162 | 1.4862 | 0.655 | 0.6558 | 0.655 | 0.6546 |
| 0.0057 | 19.0 | 171 | 1.5609 | 0.665 | 0.6651 | 0.665 | 0.6649 |
| 0.0026 | 20.0 | 180 | 1.6435 | 0.655 | 0.6550 | 0.655 | 0.6550 |
| 0.0026 | 21.0 | 189 | 1.7122 | 0.655 | 0.6550 | 0.655 | 0.6550 |
| 0.0019 | 22.0 | 198 | 1.7682 | 0.655 | 0.6550 | 0.655 | 0.6550 |
| 0.0016 | 23.0 | 207 | 1.8163 | 0.655 | 0.6550 | 0.655 | 0.6550 |
| 0.0013 | 24.0 | 216 | 1.8590 | 0.655 | 0.6550 | 0.655 | 0.6550 |
| 0.0012 | 25.0 | 225 | 1.8883 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.001 | 26.0 | 234 | 1.9199 | 0.665 | 0.6651 | 0.665 | 0.6649 |
| 0.0008 | 27.0 | 243 | 1.9548 | 0.665 | 0.6651 | 0.665 | 0.6649 |
| 0.0007 | 28.0 | 252 | 1.9958 | 0.665 | 0.6658 | 0.665 | 0.6646 |
| 0.0007 | 29.0 | 261 | 2.0427 | 0.665 | 0.6658 | 0.665 | 0.6646 |
| 0.0006 | 30.0 | 270 | 2.0890 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0006 | 31.0 | 279 | 2.1265 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0005 | 32.0 | 288 | 2.1537 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0077 | 33.0 | 297 | 2.1871 | 0.655 | 0.6550 | 0.655 | 0.6550 |
| 0.0004 | 34.0 | 306 | 2.2152 | 0.66 | 0.66 | 0.66 | 0.66 |
| 0.0004 | 35.0 | 315 | 2.2393 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0003 | 36.0 | 324 | 2.2641 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0003 | 37.0 | 333 | 2.2881 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0008 | 38.0 | 342 | 2.3215 | 0.645 | 0.6462 | 0.645 | 0.6443 |
| 0.0005 | 39.0 | 351 | 2.3445 | 0.665 | 0.6650 | 0.665 | 0.6650 |
| 0.0426 | 40.0 | 360 | 2.3033 | 0.68 | 0.6818 | 0.6800 | 0.6792 |
| 0.0426 | 41.0 | 369 | 2.3582 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0005 | 42.0 | 378 | 2.3550 | 0.66 | 0.6603 | 0.66 | 0.6599 |
| 0.0402 | 43.0 | 387 | 2.3575 | 0.665 | 0.6654 | 0.665 | 0.6648 |
| 0.0003 | 44.0 | 396 | 2.3372 | 0.675 | 0.6752 | 0.675 | 0.6749 |
| 0.0135 | 45.0 | 405 | 2.3467 | 0.66 | 0.6603 | 0.66 | 0.6599 |
| 0.0007 | 46.0 | 414 | 2.3033 | 0.685 | 0.6859 | 0.685 | 0.6846 |
| 0.0003 | 47.0 | 423 | 2.2770 | 0.675 | 0.6764 | 0.675 | 0.6743 |
| 0.0003 | 48.0 | 432 | 2.3131 | 0.68 | 0.6807 | 0.6800 | 0.6797 |
| 0.0002 | 49.0 | 441 | 2.4371 | 0.66 | 0.6601 | 0.66 | 0.6600 |
| 0.0004 | 50.0 | 450 | 2.5118 | 0.655 | 0.6551 | 0.655 | 0.6549 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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