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
args: es
metrics:
- name: Precision
type: precision
value: 0.7394396551724138
- name: Recall
type: recall
value: 0.7883731617647058
- name: F1
type: f1
value: 0.7631227758007118
- name: Accuracy
type: accuracy
value: 0.9655744705631151
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1458
- Precision: 0.7394
- Recall: 0.7884
- F1: 0.7631
- Accuracy: 0.9656
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 |
|---|---|---|---|---|---|---|---|
| 0.1047 | 1.0 | 1041 | 0.1516 | 0.7173 | 0.7505 | 0.7335 | 0.9602 |
| 0.068 | 2.0 | 2082 | 0.1280 | 0.7470 | 0.7888 | 0.7673 | 0.9664 |
| 0.0406 | 3.0 | 3123 | 0.1458 | 0.7394 | 0.7884 | 0.7631 | 0.9656 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3