---
library_name: transformers
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: v2-camembert-ner-job-ads
  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. -->

# v2-camembert-ner-job-ads

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: 0.5342
- Precision: 0.3948
- Recall: 0.7311
- F1: 0.5127
- F2: 0.6247

## 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 741
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | F2     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|
| 3.0518        | 1.0   | 121  | 2.8715          | 0.0       | 0.0    | 0.0    | 0      |
| 2.5717        | 2.0   | 242  | 2.2493          | 0.3210    | 0.1778 | 0.2288 | 0.1952 |
| 2.0235        | 3.0   | 363  | 1.8607          | 0.2572    | 0.4860 | 0.3364 | 0.4126 |
| 1.7860        | 4.0   | 484  | 1.6046          | 0.2161    | 0.5011 | 0.3019 | 0.3965 |
| 1.4654        | 5.0   | 605  | 1.3541          | 0.2824    | 0.6345 | 0.3908 | 0.5078 |
| 1.2812        | 6.0   | 726  | 1.1570          | 0.2624    | 0.6150 | 0.3678 | 0.4847 |
| 1.0853        | 7.0   | 847  | 0.9783          | 0.2889    | 0.6503 | 0.4001 | 0.5202 |
| 0.8719        | 8.0   | 968  | 0.8576          | 0.2611    | 0.6552 | 0.3734 | 0.5032 |
| 0.7604        | 9.0   | 1089 | 0.7621          | 0.3454    | 0.6951 | 0.4615 | 0.5781 |
| 0.6377        | 10.0  | 1210 | 0.6978          | 0.3358    | 0.6871 | 0.4511 | 0.5682 |
| 0.5671        | 11.0  | 1331 | 0.6360          | 0.3287    | 0.6903 | 0.4454 | 0.5658 |
| 0.4867        | 12.0  | 1452 | 0.6012          | 0.3483    | 0.7055 | 0.4663 | 0.5854 |
| 0.4340        | 13.0  | 1573 | 0.5768          | 0.3330    | 0.7059 | 0.4525 | 0.5767 |
| 0.4057        | 14.0  | 1694 | 0.5495          | 0.3528    | 0.6907 | 0.4670 | 0.5797 |
| 0.3688        | 15.0  | 1815 | 0.5370          | 0.3739    | 0.7218 | 0.4926 | 0.6085 |
| 0.3279        | 16.0  | 1936 | 0.5372          | 0.3589    | 0.7142 | 0.4777 | 0.5961 |
| 0.3076        | 17.0  | 2057 | 0.5402          | 0.3702    | 0.7285 | 0.4910 | 0.6104 |
| 0.2874        | 18.0  | 2178 | 0.5484          | 0.3994    | 0.7205 | 0.5139 | 0.6207 |
| 0.2774        | 19.0  | 2299 | 0.5410          | 0.4021    | 0.7315 | 0.5190 | 0.6285 |
| 0.2600        | 20.0  | 2420 | 0.5429          | 0.4011    | 0.7340 | 0.5188 | 0.6295 |
| 0.2505        | 21.0  | 2541 | 0.5525          | 0.4175    | 0.7321 | 0.5317 | 0.6362 |
| 0.2399        | 22.0  | 2662 | 0.5436          | 0.4165    | 0.7323 | 0.5310 | 0.6359 |
| 0.2298        | 23.0  | 2783 | 0.5315          | 0.4045    | 0.7326 | 0.5212 | 0.6303 |
| 0.2266        | 24.0  | 2904 | 0.5342          | 0.3948    | 0.7311 | 0.5127 | 0.6247 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2