metadata
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: beto-base-cased-finetuned-conll2002
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: 0.8597014925373134
- name: Recall
type: recall
value: 0.8602941176470589
- name: F1
type: f1
value: 0.8599977029975882
- name: Accuracy
type: accuracy
value: 0.978761597037205
beto-base-cased-finetuned-conll2002
This model is a fine-tuned version of NazaGara/NER-fine-tuned-BETO on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1259
- Precision: 0.8597
- Recall: 0.8603
- F1: 0.8600
- Accuracy: 0.9788
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 |
|---|---|---|---|---|---|---|---|
| 0.0154 | 1.0 | 1041 | 0.1064 | 0.8523 | 0.8603 | 0.8563 | 0.9782 |
| 0.0162 | 2.0 | 2082 | 0.1060 | 0.8556 | 0.8635 | 0.8596 | 0.9790 |
| 0.0161 | 3.0 | 3123 | 0.1259 | 0.8597 | 0.8603 | 0.8600 | 0.9788 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1