Downloads · 30 days
5
2% of all-time downloads
bsc-bio-ehr-es-vih-10k
Wariano/bsc-bio-ehr-es-vih-10k
bsc-bio-ehr-es-vih-10k is a text classification model from Wariano. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
295
Public
Repo size
3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin499 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- Sep 21, 2023
- Updated
- Sep 21, 2023
- SHA
- a525a06d
Try a prompt
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. -->
bsc-bio-ehr-es-vih-10k
This model is a fine-tuned version of PlanTL-GOB-ES/bsc-bio-ehr-es on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9958
- Positives Preds: 598
- Negative Preds: 402
- Positives Refs: 500
- Negative Refs: 500
- Tp: 411
- Fn: 89
- Fp: 187
- Tn: 313
- Accuracy: 0.724
- Precision: 0.6873
- Recall: 0.822
- F1: 0.7486
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 | Positives Preds | Negative Preds | Positives Refs | Negative Refs | Tp | Fn | Fp | Tn | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4309 | 1.0 | 250 | 0.4999 | 384 | 616 | 500 | 500 | 316 | 184 | 68 | 432 | 0.748 | 0.8229 | 0.632 | 0.7149 |
| 0.2849 | 2.0 | 500 | 0.6391 | 546 | 454 | 500 | 500 | 396 | 104 | 150 | 350 | 0.746 | 0.7253 | 0.792 | 0.7572 |
| 0.1931 | 3.0 | 750 | 0.7333 | 610 | 390 | 500 | 500 | 414 | 86 | 196 | 304 | 0.718 | 0.6787 | 0.828 | 0.7459 |
| 0.1255 | 4.0 | 1000 | 0.8917 | 604 | 396 | 500 | 500 | 417 | 83 | 187 | 313 | 0.73 | 0.6904 | 0.834 | 0.7554 |
| 0.0918 | 5.0 | 1250 | 0.9958 | 598 | 402 | 500 | 500 | 411 | 89 | 187 | 313 | 0.724 | 0.6873 | 0.822 | 0.7486 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Created
- Sep 21, 2023
- Updated
- Sep 21, 2023