mBERT-base-cased-NER-CONLL (EN-ES)
This model is a fine-tuned version of bert-base-multilingual-cased on the conll2003 and conll2002 datasets. Training was performed separately. It achieves the following results on the evaluation set:
Connll2003:
- Loss: 0.0585
- Precision: 0.9489
- Recall: 0.9541
- F1: 0.9515
- Accuracy: 0.9880
Conll2002:
- Loss: 0.1435
- Precision: 0.8621
- Recall: 0.8663
- F1: 0.8642
- Accuracy: 0.9791
Model description
IOB tagging Scheme. PER/LOC/MISC/ORG tags
Intended uses & limitations
More information needed
Training and evaluation data
Conll2002/2003 (ES-EN)
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Conll2003:
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.1739 | 1.0 | 878 | 0.0741 | 0.9246 | 0.9181 | 0.9213 | 0.9823 |
0.045 | 2.0 | 1756 | 0.0586 | 0.9469 | 0.9476 | 0.9472 | 0.9870 |
0.0213 | 3.0 | 2634 | 0.0583 | 0.9503 | 0.9510 | 0.9506 | 0.9877 |
0.0113 | 4.0 | 3512 | 0.0585 | 0.9489 | 0.9541 | 0.9515 | 0.9880 |
Conll2002:
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0739 | 1.0 | 4162 | 0.1322 | 0.8430 | 0.8267 | 0.8348 | 0.9741 |
0.0454 | 2.0 | 8324 | 0.1158 | 0.8664 | 0.8614 | 0.8639 | 0.9782 |
0.031 | 3.0 | 12486 | 0.1243 | 0.8521 | 0.8660 | 0.8590 | 0.9783 |
0.0136 | 4.0 | 16648 | 0.1435 | 0.8621 | 0.8663 | 0.8642 | 0.9791 |
Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
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Datasets used to train StivenLancheros/mBERT-base-cased-NER-CONLL
Evaluation results
- Precision on conll2002self-reported0.862
- Recall on conll2002self-reported0.866
- F1 on conll2002self-reported0.864
- Accuracy on conll2002self-reported0.979