metadata
metrics:
- precision: 0.936
- recall: 0.9458
- f1: 0.9409
- accuracy: 0.9902
datasets:
- conll2003
language:
- en
- de
- nl
- es
- multilingual
model-index:
- name: gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
results:
- task:
type: ner
name: Named Entity Recognition
dataset:
type: conll2003
name: ConLL 2003
metrics:
- type: f1-score
value: 0.9409
gunghio/distilbert-base-multilingual-cased-finetuned-conll2003-ner
This model was trained from scratch on an conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0388
- Precision: 0.9360
- Recall: 0.9458
- F1: 0.9409
- Accuracy: 0.9902
Model description
It is based on distilbert-base-multilingual-cased
Intended uses & limitations
More information needed
Training and evaluation data
Training dataset: conll2003
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.1653 | 1.0 | 878 | 0.0465 | 0.9267 | 0.9300 | 0.9283 | 0.9883 |
| 0.0322 | 2.0 | 1756 | 0.0404 | 0.9360 | 0.9431 | 0.9396 | 0.9897 |
| 0.0185 | 3.0 | 2634 | 0.0388 | 0.9360 | 0.9458 | 0.9409 | 0.9902 |
Framework versions
- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.2