metadata
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-es-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: es
split: validation
args: es
metrics:
- name: Precision
type: precision
value: 0.8694376140239549
- name: Recall
type: recall
value: 0.8933170334148329
- name: F1
type: f1
value: 0.8812155806568316
- name: Accuracy
type: accuracy
value: 0.9448179020644737
bert-finetuned-ner-es-en
This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set:
- Loss: 0.2564
- Precision: 0.8694
- Recall: 0.8933
- F1: 0.8812
- Accuracy: 0.9448
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 |
|---|---|---|---|---|---|---|---|
| 0.2459 | 1.0 | 2500 | 0.2378 | 0.8169 | 0.8606 | 0.8382 | 0.9304 |
| 0.1441 | 2.0 | 5000 | 0.2468 | 0.8618 | 0.8876 | 0.8745 | 0.9429 |
| 0.0972 | 3.0 | 7500 | 0.2564 | 0.8694 | 0.8933 | 0.8812 | 0.9448 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2