bert-base-multilingual-cased-finetuned-ner-4
#This model is part of a test for creating multilingual BioMedical NER systems. Not intended for proffesional use now.
This model is a fine-tuned version of bert-base-multilingual-cased on the CRAFT+BC4CHEMD+BioNLP09 datasets concatenated. It achieves the following results on the evaluation set:
- Loss: 0.1027
- Precision: 0.9830
- Recall: 0.9832
- F1: 0.9831
- Accuracy: 0.9799
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: 3e-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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0658 | 1.0 | 6128 | 0.0751 | 0.9795 | 0.9795 | 0.9795 | 0.9758 |
| 0.0406 | 2.0 | 12256 | 0.0753 | 0.9827 | 0.9815 | 0.9821 | 0.9786 |
| 0.0182 | 3.0 | 18384 | 0.0934 | 0.9834 | 0.9825 | 0.9829 | 0.9796 |
| 0.011 | 4.0 | 24512 | 0.1027 | 0.9830 | 0.9832 | 0.9831 | 0.9799 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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