---
license: mit
tags:
- generated_from_trainer
model-index:
- name: camembert-ner-finetuned-jul
  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. -->

# camembert-ner-finetuned-jul

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: 0.1522
- Loc: {'precision': 0.7952488687782805, 'recall': 0.703, 'f1': 0.7462845010615711, 'number': 1000}
- Misc: {'precision': 0.6310931641188348, 'recall': 0.6640364188163884, 'f1': 0.6471458148476782, 'number': 3295}
- Org: {'precision': 0.6708074534161491, 'recall': 0.6792452830188679, 'f1': 0.6749999999999999, 'number': 477}
- Per: {'precision': 0.7778738115816768, 'recall': 0.7772020725388601, 'f1': 0.7775377969762419, 'number': 1158}
- Overall Precision: 0.6869
- Overall Recall: 0.6939
- Overall F1: 0.6904
- Overall Accuracy: 0.9567

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Loc                                                                                          | Misc                                                                                                      | Org                                                                                                      | Per                                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.1195        | 1.0   | 2044 | 0.1425          | {'precision': 0.750620347394541, 'recall': 0.605, 'f1': 0.6699889258028793, 'number': 1000}  | {'precision': 0.6498784300104203, 'recall': 0.5678300455235205, 'f1': 0.606090055069647, 'number': 3295}  | {'precision': 0.6763392857142857, 'recall': 0.6352201257861635, 'f1': 0.6551351351351351, 'number': 477} | {'precision': 0.6595744680851063, 'recall': 0.7763385146804835, 'f1': 0.7132090440301467, 'number': 1158} | 0.6692            | 0.6202         | 0.6438     | 0.9511           |
| 0.0736        | 2.0   | 4088 | 0.1387          | {'precision': 0.7714604236343366, 'recall': 0.692, 'f1': 0.7295730100158145, 'number': 1000} | {'precision': 0.6479814115596864, 'recall': 0.6770864946889226, 'f1': 0.6622143069159989, 'number': 3295} | {'precision': 0.7018348623853211, 'recall': 0.6415094339622641, 'f1': 0.6703176341730558, 'number': 477} | {'precision': 0.7717484926787253, 'recall': 0.7737478411053541, 'f1': 0.7727468736524364, 'number': 1158} | 0.6948            | 0.6956         | 0.6952     | 0.9575           |
| 0.0499        | 3.0   | 6132 | 0.1522          | {'precision': 0.7952488687782805, 'recall': 0.703, 'f1': 0.7462845010615711, 'number': 1000} | {'precision': 0.6310931641188348, 'recall': 0.6640364188163884, 'f1': 0.6471458148476782, 'number': 3295} | {'precision': 0.6708074534161491, 'recall': 0.6792452830188679, 'f1': 0.6749999999999999, 'number': 477} | {'precision': 0.7778738115816768, 'recall': 0.7772020725388601, 'f1': 0.7775377969762419, 'number': 1158} | 0.6869            | 0.6939         | 0.6904     | 0.9567           |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3