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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/distemist-85-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/distemist-85-ner
type: Rodrigo1771/distemist-85-ner
config: DisTEMIST NER
split: validation
args: DisTEMIST NER
metrics:
- name: Precision
type: precision
value: 0.803175344384777
- name: Recall
type: recall
value: 0.8048666354702855
- name: F1
type: f1
value: 0.8040201005025126
- name: Accuracy
type: accuracy
value: 0.9764853694371592
---
<!-- 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/distemist-85-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1424
- Precision: 0.8032
- Recall: 0.8049
- F1: 0.8040
- Accuracy: 0.9765
## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.9990 | 499 | 0.0739 | 0.7271 | 0.7953 | 0.7596 | 0.9731 |
| 0.105 | 2.0 | 999 | 0.0908 | 0.7436 | 0.7890 | 0.7656 | 0.9729 |
| 0.0448 | 2.9990 | 1498 | 0.0930 | 0.7676 | 0.7990 | 0.7830 | 0.9744 |
| 0.0255 | 4.0 | 1998 | 0.1052 | 0.7806 | 0.7983 | 0.7894 | 0.9757 |
| 0.0164 | 4.9990 | 2497 | 0.1100 | 0.7756 | 0.8007 | 0.7879 | 0.9750 |
| 0.0112 | 6.0 | 2997 | 0.1266 | 0.7869 | 0.8124 | 0.7994 | 0.9768 |
| 0.0073 | 6.9990 | 3496 | 0.1288 | 0.7929 | 0.8009 | 0.7969 | 0.9763 |
| 0.0054 | 8.0 | 3996 | 0.1424 | 0.8032 | 0.8049 | 0.8040 | 0.9765 |
| 0.0038 | 8.9990 | 4495 | 0.1455 | 0.7901 | 0.8042 | 0.7971 | 0.9765 |
| 0.0028 | 9.9900 | 4990 | 0.1497 | 0.7898 | 0.8072 | 0.7984 | 0.9768 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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