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roberta-clinical-wl-es-finetuned-ner
mrojas/roberta-clinical-wl-es-finetuned-ner
roberta-clinical-wl-es-finetuned-ner is a token classification model from mrojas. 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
- Nov 10, 2022
- Updated
- Nov 10, 2022
- SHA
- 6d9a3310
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Datasets
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. -->
roberta-clinical-wl-es-finetuned-ner
This model is a fine-tuned version of plncmm/roberta-clinical-wl-es on the wl dataset. It achieves the following results on the evaluation set:
- Loss: 0.6227
- Precision: 0.6865
- Recall: 0.7355
- F1: 0.7102
- Accuracy: 0.8268
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: 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.028 | 1.0 | 500 | 0.6870 | 0.6558 | 0.6855 | 0.6703 | 0.8035 |
| 0.5923 | 2.0 | 1000 | 0.6248 | 0.6851 | 0.7235 | 0.7038 | 0.8244 |
| 0.4928 | 3.0 | 1500 | 0.6227 | 0.6865 | 0.7355 | 0.7102 | 0.8268 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- apache-2.0
- Created
- Nov 10, 2022
- Updated
- Nov 10, 2022