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distilbert-base-uncased-mapa-ner-coarse_grained-v2
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1627
- Precision: 0.7898
- Recall: 0.4843
- F1: 0.6004
- Accuracy: 0.9857
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: 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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0354 | 1.0 | 1739 | 0.0834 | 0.7023 | 0.4612 | 0.5568 | 0.9840 |
| 0.0255 | 2.0 | 3478 | 0.1034 | 0.8172 | 0.4355 | 0.5682 | 0.9852 |
| 0.0168 | 3.0 | 5217 | 0.0969 | 0.7714 | 0.4588 | 0.5754 | 0.9848 |
| 0.0132 | 4.0 | 6956 | 0.1042 | 0.7477 | 0.4838 | 0.5875 | 0.9852 |
| 0.0112 | 5.0 | 8695 | 0.1109 | 0.7421 | 0.4863 | 0.5876 | 0.9849 |
| 0.0085 | 6.0 | 10434 | 0.1076 | 0.7194 | 0.4951 | 0.5865 | 0.9850 |
| 0.0067 | 7.0 | 12173 | 0.1343 | 0.7828 | 0.4587 | 0.5784 | 0.9849 |
| 0.0047 | 8.0 | 13912 | 0.1252 | 0.7425 | 0.4840 | 0.5860 | 0.9853 |
| 0.0045 | 9.0 | 15651 | 0.1410 | 0.7943 | 0.4615 | 0.5838 | 0.9852 |
| 0.0035 | 10.0 | 17390 | 0.1311 | 0.7624 | 0.4929 | 0.5987 | 0.9857 |
| 0.003 | 11.0 | 19129 | 0.1494 | 0.8059 | 0.4691 | 0.5930 | 0.9855 |
| 0.0025 | 12.0 | 20868 | 0.1436 | 0.7674 | 0.4852 | 0.5945 | 0.9856 |
| 0.002 | 13.0 | 22607 | 0.1513 | 0.7778 | 0.4741 | 0.5891 | 0.9852 |
| 0.0014 | 14.0 | 24346 | 0.1577 | 0.7986 | 0.4726 | 0.5938 | 0.9855 |
| 0.0016 | 15.0 | 26085 | 0.1573 | 0.7802 | 0.4766 | 0.5917 | 0.9855 |
| 0.0011 | 16.0 | 27824 | 0.1599 | 0.7917 | 0.4723 | 0.5916 | 0.9856 |
| 0.0012 | 17.0 | 29563 | 0.1601 | 0.7848 | 0.4867 | 0.6008 | 0.9857 |
| 0.001 | 18.0 | 31302 | 0.1572 | 0.7614 | 0.4939 | 0.5991 | 0.9856 |
| 0.0011 | 19.0 | 33041 | 0.1602 | 0.7858 | 0.4870 | 0.6013 | 0.9857 |
| 0.0009 | 20.0 | 34780 | 0.1627 | 0.7898 | 0.4843 | 0.6004 | 0.9857 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
