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---
library_name: transformers
license: apache-2.0
base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/cantemist-fasttext-75-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/cantemist-fasttext-75-ner
type: Rodrigo1771/cantemist-fasttext-75-ner
config: CantemistNer
split: validation
args: CantemistNer
metrics:
- name: Precision
type: precision
value: 0.8462436745815493
- name: Recall
type: recall
value: 0.8562426152028357
- name: F1
type: f1
value: 0.8512137823022711
- name: Accuracy
type: accuracy
value: 0.991867253328732
---
<!-- 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. -->
# output
This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the Rodrigo1771/cantemist-fasttext-75-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0478
- Precision: 0.8462
- Recall: 0.8562
- F1: 0.8512
- Accuracy: 0.9919
## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0571 | 0.9992 | 616 | 0.0266 | 0.7767 | 0.8492 | 0.8113 | 0.9906 |
| 0.018 | 2.0 | 1233 | 0.0304 | 0.8075 | 0.8476 | 0.8271 | 0.9914 |
| 0.0101 | 2.9992 | 1849 | 0.0356 | 0.8159 | 0.8468 | 0.8311 | 0.9906 |
| 0.0057 | 4.0 | 2466 | 0.0365 | 0.8239 | 0.8460 | 0.8348 | 0.9910 |
| 0.0027 | 4.9992 | 3082 | 0.0396 | 0.8211 | 0.8480 | 0.8343 | 0.9916 |
| 0.0018 | 6.0 | 3699 | 0.0435 | 0.8306 | 0.8633 | 0.8467 | 0.9915 |
| 0.0013 | 6.9992 | 4315 | 0.0478 | 0.8462 | 0.8562 | 0.8512 | 0.9919 |
| 0.0008 | 8.0 | 4932 | 0.0469 | 0.8347 | 0.8614 | 0.8478 | 0.9915 |
| 0.0004 | 8.9992 | 5548 | 0.0515 | 0.8414 | 0.8610 | 0.8511 | 0.9919 |
| 0.0002 | 9.9919 | 6160 | 0.0520 | 0.8386 | 0.8598 | 0.8491 | 0.9918 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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