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new_camembert_jb
This model is a fine-tuned version of Jean-Baptiste/camembert-ner on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0752
- Overall Precision: 0.8202
- Overall Recall: 0.8595
- Overall F1: 0.8394
- Overall Accuracy: 0.9814
- Er F1: 0.8430
- Oc F1: 0.8418
- Umanprod F1: 0.6933
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Er F1 | Oc F1 | Umanprod F1 |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.2682 | 1.0 | 613 | 0.0813 | 0.7550 | 0.8071 | 0.7802 | 0.9749 | 0.7920 | 0.7709 | 0.6667 |
| 0.0717 | 2.0 | 1226 | 0.0706 | 0.8139 | 0.8411 | 0.8273 | 0.9808 | 0.8446 | 0.8126 | 0.6857 |
| 0.0524 | 3.0 | 1839 | 0.0723 | 0.8215 | 0.8567 | 0.8387 | 0.9812 | 0.8462 | 0.8346 | 0.7368 |
| 0.0372 | 4.0 | 2452 | 0.0752 | 0.8202 | 0.8595 | 0.8394 | 0.9814 | 0.8430 | 0.8418 | 0.6933 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cpu
- Datasets 2.7.1
- Tokenizers 0.13.2
