metadata
license: apache-2.0
base_model: BSC-LT/roberta-base-bne-capitel-ner
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-bne-capitel-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: 0.8601446000903751
- name: Recall
type: recall
value: 0.8747702205882353
- name: F1
type: f1
value: 0.8673957621326043
- name: Accuracy
type: accuracy
value: 0.9779993282237626
roberta-base-bne-capitel-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: 0.1229
- Precision: 0.8601
- Recall: 0.8748
- F1: 0.8674
- Accuracy: 0.9780
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: 8
- eval_batch_size: 8
- 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 |
---|---|---|---|---|---|---|---|
0.0172 | 1.0 | 1041 | 0.1157 | 0.8468 | 0.8640 | 0.8553 | 0.9770 |
0.0109 | 2.0 | 2082 | 0.1177 | 0.8705 | 0.8853 | 0.8779 | 0.9786 |
0.0066 | 3.0 | 3123 | 0.1229 | 0.8601 | 0.8748 | 0.8674 | 0.9780 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.14.1