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---
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: 0.8712310133756518
- name: Recall
type: recall
value: 0.8830422794117647
- name: F1
type: f1
value: 0.8770968846285518
- name: Accuracy
type: accuracy
value: 0.978961189654646
---
<!-- 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. -->
# roberta-base-bne-capitel-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: 0.1255
- Precision: 0.8712
- Recall: 0.8830
- F1: 0.8771
- Accuracy: 0.9790
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0135 | 1.0 | 1041 | 0.1233 | 0.8615 | 0.8803 | 0.8708 | 0.9783 |
| 0.0111 | 2.0 | 2082 | 0.1099 | 0.8709 | 0.8853 | 0.8781 | 0.9799 |
| 0.0061 | 3.0 | 3123 | 0.1203 | 0.8569 | 0.8739 | 0.8653 | 0.9781 |
| 0.0035 | 4.0 | 4164 | 0.1255 | 0.8712 | 0.8830 | 0.8771 | 0.9790 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.14.1
|