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bert-base-multilingual-cased-finetuned-multilingual-ner
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2352
- Precision: 0.8109
- Recall: 0.8332
- F1: 0.8219
- Accuracy: 0.9264
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: 32
- eval_batch_size: 32
- 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 |
---|---|---|---|---|---|---|---|
0.7301 | 0.16 | 100 | 0.3827 | 0.6189 | 0.7009 | 0.6573 | 0.8734 |
0.3841 | 0.32 | 200 | 0.3195 | 0.7057 | 0.7511 | 0.7277 | 0.8922 |
0.3451 | 0.48 | 300 | 0.2862 | 0.7094 | 0.7750 | 0.7407 | 0.8952 |
0.3187 | 0.65 | 400 | 0.2735 | 0.7372 | 0.7802 | 0.7581 | 0.9019 |
0.3058 | 0.81 | 500 | 0.2533 | 0.7536 | 0.8015 | 0.7768 | 0.9052 |
0.2918 | 0.97 | 600 | 0.2458 | 0.7587 | 0.8085 | 0.7828 | 0.9126 |
0.2425 | 1.13 | 700 | 0.2379 | 0.7742 | 0.7976 | 0.7857 | 0.9150 |
0.2387 | 1.29 | 800 | 0.2300 | 0.7772 | 0.8108 | 0.7936 | 0.9165 |
0.2125 | 1.45 | 900 | 0.2387 | 0.7900 | 0.8130 | 0.8014 | 0.9180 |
0.2026 | 1.62 | 1000 | 0.2317 | 0.7877 | 0.8152 | 0.8012 | 0.9186 |
0.1963 | 1.78 | 1100 | 0.2326 | 0.7842 | 0.8269 | 0.8049 | 0.9220 |
0.2052 | 1.94 | 1200 | 0.2247 | 0.7924 | 0.8234 | 0.8076 | 0.9212 |
0.1868 | 2.1 | 1300 | 0.2410 | 0.7903 | 0.8282 | 0.8088 | 0.9204 |
0.1556 | 2.26 | 1400 | 0.2428 | 0.8064 | 0.8317 | 0.8189 | 0.9256 |
0.153 | 2.42 | 1500 | 0.2316 | 0.8017 | 0.8282 | 0.8147 | 0.9238 |
0.1484 | 2.58 | 1600 | 0.2379 | 0.8054 | 0.8338 | 0.8194 | 0.9258 |
0.137 | 2.75 | 1700 | 0.2331 | 0.8101 | 0.8324 | 0.8211 | 0.9270 |
0.1638 | 2.91 | 1800 | 0.2352 | 0.8109 | 0.8332 | 0.8219 | 0.9264 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1