metadata
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
bert-finetuned-ner
This model is a fine-tuned version of 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