---
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