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---
library_name: transformers
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-model-camembert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ner-model-camembert
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1642
- Precision: 0.8721
- Recall: 0.7732
- F1: 0.8197
- Accuracy: 0.9571
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 24 | 0.3640 | 0.0 | 0.0 | 0.0 | 0.8739 |
| No log | 2.0 | 48 | 0.2640 | 0.6884 | 0.4312 | 0.5303 | 0.9037 |
| No log | 3.0 | 72 | 0.2248 | 0.6976 | 0.6431 | 0.6692 | 0.9198 |
| No log | 4.0 | 96 | 0.2163 | 0.8182 | 0.6022 | 0.6938 | 0.9330 |
| No log | 5.0 | 120 | 0.1690 | 0.7336 | 0.8086 | 0.7692 | 0.9388 |
| No log | 6.0 | 144 | 0.1768 | 0.8558 | 0.6840 | 0.7603 | 0.9456 |
| No log | 7.0 | 168 | 0.1838 | 0.8578 | 0.6952 | 0.7680 | 0.9470 |
| No log | 8.0 | 192 | 0.1591 | 0.8158 | 0.8067 | 0.8112 | 0.9526 |
| No log | 9.0 | 216 | 0.1688 | 0.8571 | 0.7584 | 0.8047 | 0.9536 |
| No log | 10.0 | 240 | 0.1596 | 0.8431 | 0.7993 | 0.8206 | 0.9559 |
| No log | 11.0 | 264 | 0.1599 | 0.8563 | 0.7751 | 0.8137 | 0.9552 |
| No log | 12.0 | 288 | 0.1713 | 0.8515 | 0.7565 | 0.8012 | 0.9526 |
| No log | 13.0 | 312 | 0.1646 | 0.8394 | 0.7770 | 0.8069 | 0.9531 |
| No log | 14.0 | 336 | 0.1705 | 0.8367 | 0.7807 | 0.8077 | 0.9531 |
| No log | 15.0 | 360 | 0.1717 | 0.8236 | 0.7900 | 0.8065 | 0.9522 |
| No log | 16.0 | 384 | 0.1689 | 0.8631 | 0.7732 | 0.8157 | 0.9559 |
| No log | 17.0 | 408 | 0.1608 | 0.8835 | 0.7751 | 0.8257 | 0.9587 |
| No log | 18.0 | 432 | 0.1499 | 0.8849 | 0.7862 | 0.8327 | 0.9602 |
| No log | 19.0 | 456 | 0.1614 | 0.8846 | 0.7695 | 0.8231 | 0.9583 |
| No log | 20.0 | 480 | 0.1688 | 0.8448 | 0.7788 | 0.8104 | 0.9541 |
| 0.0983 | 21.0 | 504 | 0.1672 | 0.8482 | 0.7788 | 0.8120 | 0.9545 |
| 0.0983 | 22.0 | 528 | 0.1668 | 0.8563 | 0.7751 | 0.8137 | 0.9552 |
| 0.0983 | 23.0 | 552 | 0.1678 | 0.8545 | 0.7751 | 0.8129 | 0.9550 |
| 0.0983 | 24.0 | 576 | 0.1645 | 0.8703 | 0.7732 | 0.8189 | 0.9569 |
| 0.0983 | 25.0 | 600 | 0.1642 | 0.8721 | 0.7732 | 0.8197 | 0.9571 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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