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DISO_bsc_test16
roscazo/DISO_bsc_test16
DISO_bsc_test16 is a token classification model from roscazo. Use it when you need labels on individual words, such as names. 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. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- roberta
- Access
- Public
- Created
- Jan 3, 2023
- Updated
- Jan 3, 2023
- SHA
- 153075e3
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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. -->
DISO_bsc_test16
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: 0.1732
- Diso Precision: 0.7577
- Diso Recall: 0.7757
- Diso F1: 0.7666
- Diso Number: 4552
- Overall Precision: 0.7577
- Overall Recall: 0.7757
- Overall F1: 0.7666
- Overall Accuracy: 0.9732
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: 8e-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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0948 | 1.0 | 1400 | 0.0766 | 0.7157 | 0.7594 | 0.7369 | 4552 | 0.7157 | 0.7594 | 0.7369 | 0.9710 |
| 0.0631 | 2.0 | 2800 | 0.0818 | 0.7442 | 0.7599 | 0.7520 | 4552 | 0.7442 | 0.7599 | 0.7520 | 0.9726 |
| 0.0454 | 3.0 | 4200 | 0.0842 | 0.7544 | 0.7654 | 0.7599 | 4552 | 0.7544 | 0.7654 | 0.7599 | 0.9728 |
| 0.0311 | 4.0 | 5600 | 0.1113 | 0.7678 | 0.7700 | 0.7689 | 4552 | 0.7678 | 0.7700 | 0.7689 | 0.9732 |
| 0.0217 | 5.0 | 7000 | 0.1231 | 0.7745 | 0.7687 | 0.7716 | 4552 | 0.7745 | 0.7687 | 0.7716 | 0.9743 |
| 0.015 | 6.0 | 8400 | 0.1482 | 0.7651 | 0.7733 | 0.7691 | 4552 | 0.7651 | 0.7733 | 0.7691 | 0.9735 |
| 0.0101 | 7.0 | 9800 | 0.1498 | 0.7576 | 0.7709 | 0.7642 | 4552 | 0.7576 | 0.7709 | 0.7642 | 0.9730 |
| 0.0073 | 8.0 | 11200 | 0.1732 | 0.7577 | 0.7757 | 0.7666 | 4552 | 0.7577 | 0.7757 | 0.7666 | 0.9732 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Created
- Jan 3, 2023
- Updated
- Jan 3, 2023