|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- conll2002 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: conll2002 |
|
type: conll2002 |
|
config: es |
|
split: validation |
|
args: es |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.8614552827213336 |
|
- name: Recall |
|
type: recall |
|
value: 0.8786764705882353 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8699806620407236 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9785031604018444 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# bert-finetuned-ner |
|
|
|
This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0944 |
|
- Precision: 0.8615 |
|
- Recall: 0.8787 |
|
- F1: 0.8700 |
|
- Accuracy: 0.9785 |
|
|
|
## 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: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.104 | 1.0 | 521 | 0.0823 | 0.8616 | 0.8814 | 0.8714 | 0.9789 | |
|
| 0.034 | 2.0 | 1042 | 0.0855 | 0.8601 | 0.8773 | 0.8686 | 0.9777 | |
|
| 0.0197 | 3.0 | 1563 | 0.0944 | 0.8615 | 0.8787 | 0.8700 | 0.9785 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.30.2 |
|
- Pytorch 2.0.0 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.13.3 |
|
|
