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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/drugtemist-85-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: Rodrigo1771/drugtemist-85-ner
type: Rodrigo1771/drugtemist-85-ner
config: DrugTEMIST NER
split: validation
args: DrugTEMIST NER
metrics:
- name: Precision
type: precision
value: 0.9461187214611873
- name: Recall
type: recall
value: 0.9522058823529411
- name: F1
type: f1
value: 0.9491525423728814
- name: Accuracy
type: accuracy
value: 0.9989426998228679
---
<!-- 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/drugtemist-85-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Precision: 0.9461
- Recall: 0.9522
- F1: 0.9492
- Accuracy: 0.9989
## 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 | 1.0 | 466 | 0.0031 | 0.9292 | 0.9412 | 0.9352 | 0.9989 |
| 0.0199 | 2.0 | 932 | 0.0031 | 0.9212 | 0.9568 | 0.9387 | 0.9989 |
| 0.0026 | 3.0 | 1398 | 0.0040 | 0.9365 | 0.9357 | 0.9361 | 0.9989 |
| 0.0011 | 4.0 | 1864 | 0.0052 | 0.9400 | 0.9219 | 0.9309 | 0.9987 |
| 0.001 | 5.0 | 2330 | 0.0048 | 0.9461 | 0.9522 | 0.9492 | 0.9989 |
| 0.0005 | 6.0 | 2796 | 0.0046 | 0.9376 | 0.9522 | 0.9448 | 0.9989 |
| 0.0004 | 7.0 | 3262 | 0.0050 | 0.9328 | 0.9568 | 0.9446 | 0.9990 |
| 0.0002 | 8.0 | 3728 | 0.0055 | 0.9423 | 0.9449 | 0.9436 | 0.9989 |
| 0.0001 | 9.0 | 4194 | 0.0057 | 0.9399 | 0.9485 | 0.9442 | 0.9989 |
| 0.0001 | 10.0 | 4660 | 0.0058 | 0.9348 | 0.9485 | 0.9416 | 0.9989 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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