---
license: apache-2.0
base_model: PlanTL-GOB-ES/bsc-bio-ehr-es
tags:
- token-classification
- generated_from_trainer
datasets:
- Rodrigo1771/drugtemist-ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: output
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Rodrigo1771/drugtemist-ner
      type: Rodrigo1771/drugtemist-ner
      config: DrugTEMIST NER
      split: validation
      args: DrugTEMIST NER
    metrics:
    - name: Precision
      type: precision
      value: 0.9430379746835443
    - name: Recall
      type: recall
      value: 0.9586397058823529
    - name: F1
      type: f1
      value: 0.95077484047402
    - name: Accuracy
      type: accuracy
      value: 0.9990319324961724
---

<!-- 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-ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0058
- Precision: 0.9430
- Recall: 0.9586
- F1: 0.9508
- Accuracy: 0.9990

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0055        | 0.9997 | 1701  | 0.0039          | 0.9182    | 0.8869 | 0.9023 | 0.9984   |
| 0.0037        | 2.0    | 3403  | 0.0057          | 0.9195    | 0.8814 | 0.9000 | 0.9982   |
| 0.0024        | 2.9997 | 5104  | 0.0036          | 0.9293    | 0.9540 | 0.9415 | 0.9990   |
| 0.0013        | 4.0    | 6806  | 0.0053          | 0.9477    | 0.9329 | 0.9403 | 0.9989   |
| 0.0012        | 4.9997 | 8507  | 0.0063          | 0.9419    | 0.9393 | 0.9406 | 0.9989   |
| 0.0007        | 6.0    | 10209 | 0.0048          | 0.9403    | 0.9559 | 0.9480 | 0.9989   |
| 0.0005        | 6.9997 | 11910 | 0.0066          | 0.9490    | 0.9403 | 0.9446 | 0.9989   |
| 0.0002        | 8.0    | 13612 | 0.0058          | 0.9430    | 0.9586 | 0.9508 | 0.9990   |
| 0.0001        | 8.9997 | 15313 | 0.0063          | 0.9413    | 0.9577 | 0.9494 | 0.9990   |
| 0.0           | 9.9971 | 17010 | 0.0065          | 0.9437    | 0.9559 | 0.9498 | 0.9990   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1