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---
library_name: transformers
base_model: dccuchile/bert-base-spanish-wwm-uncased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-BETO-UNCASED-BIOBERT
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: biobert_json
type: biobert_json
config: Biobert_json
split: validation
args: Biobert_json
metrics:
- name: Precision
type: precision
value: 0.9467966573816156
- name: Recall
type: recall
value: 0.9626168224299065
- name: F1
type: f1
value: 0.9546412020783598
- name: Accuracy
type: accuracy
value: 0.9762832612799123
---
<!-- 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. -->
# NER-finetuning-BETO-UNCASED-BIOBERT
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1165
- Precision: 0.9468
- Recall: 0.9626
- F1: 0.9546
- Accuracy: 0.9763
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3684 | 1.0 | 612 | 0.1093 | 0.9356 | 0.9523 | 0.9438 | 0.9708 |
| 0.1191 | 2.0 | 1224 | 0.1026 | 0.9388 | 0.9674 | 0.9529 | 0.9746 |
| 0.0842 | 3.0 | 1836 | 0.1011 | 0.9394 | 0.9690 | 0.9539 | 0.9754 |
| 0.0657 | 4.0 | 2448 | 0.0986 | 0.9468 | 0.9682 | 0.9574 | 0.9779 |
| 0.0437 | 5.0 | 3060 | 0.0988 | 0.9499 | 0.9649 | 0.9573 | 0.9772 |
| 0.0395 | 6.0 | 3672 | 0.1070 | 0.9446 | 0.9645 | 0.9545 | 0.9757 |
| 0.0311 | 7.0 | 4284 | 0.1110 | 0.9459 | 0.9673 | 0.9565 | 0.9766 |
| 0.0302 | 8.0 | 4896 | 0.1141 | 0.9449 | 0.9635 | 0.9541 | 0.9763 |
| 0.023 | 9.0 | 5508 | 0.1133 | 0.9485 | 0.9641 | 0.9563 | 0.9770 |
| 0.0198 | 10.0 | 6120 | 0.1165 | 0.9468 | 0.9626 | 0.9546 | 0.9763 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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