---
license: mit
tags:
- generated_from_trainer
model-index:
- name: 10_epochs_camembert_jb
  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. -->

# 10_epochs_camembert_jb

This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1070
- Overall Precision: 0.8279
- Overall Recall: 0.8660
- Overall F1: 0.8465
- Overall Accuracy: 0.9803
- Er F1: 0.8617
- Oc F1: 0.8347
- Umanprod F1: 0.7297

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Er F1  | Oc F1  | Umanprod F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:-----------:|
| 0.2805        | 1.0   | 613  | 0.0797          | 0.7802            | 0.7990         | 0.7895     | 0.9749           | 0.8187 | 0.7677 | 0.4231      |
| 0.072         | 2.0   | 1226 | 0.0790          | 0.8060            | 0.8392         | 0.8223     | 0.9773           | 0.8458 | 0.8050 | 0.5574      |
| 0.0511        | 3.0   | 1839 | 0.0807          | 0.8139            | 0.8623         | 0.8374     | 0.9789           | 0.8583 | 0.8200 | 0.6933      |
| 0.0354        | 4.0   | 2452 | 0.0808          | 0.8097            | 0.8574         | 0.8329     | 0.9793           | 0.8589 | 0.8115 | 0.6667      |
| 0.0198        | 5.0   | 3065 | 0.0940          | 0.7936            | 0.8591         | 0.8250     | 0.9781           | 0.8426 | 0.8124 | 0.6835      |
| 0.0165        | 6.0   | 3678 | 0.0988          | 0.8350            | 0.8542         | 0.8445     | 0.9802           | 0.8656 | 0.8297 | 0.6486      |
| 0.0126        | 7.0   | 4291 | 0.0990          | 0.8292            | 0.8692         | 0.8488     | 0.9805           | 0.8682 | 0.8340 | 0.6849      |
| 0.0103        | 8.0   | 4904 | 0.1042          | 0.8246            | 0.8666         | 0.8450     | 0.9803           | 0.8630 | 0.8331 | 0.6575      |
| 0.0076        | 9.0   | 5517 | 0.1066          | 0.8195            | 0.8687         | 0.8434     | 0.9801           | 0.8593 | 0.8305 | 0.7297      |
| 0.0066        | 10.0  | 6130 | 0.1070          | 0.8279            | 0.8660         | 0.8465     | 0.9803           | 0.8617 | 0.8347 | 0.7297      |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cpu
- Datasets 2.7.1
- Tokenizers 0.13.2