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# camembert-keyword-extractor

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2199
- Precision: 0.6743
- Recall: 0.6979
- Accuracy: 0.9346
- F1: 0.6859

## 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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.1747        | 1.0   | 1875  | 0.1780          | 0.5935    | 0.7116 | 0.9258   | 0.6472 |
| 0.1375        | 2.0   | 3750  | 0.1588          | 0.6505    | 0.7032 | 0.9334   | 0.6759 |
| 0.1147        | 3.0   | 5625  | 0.1727          | 0.6825    | 0.6689 | 0.9355   | 0.6756 |
| 0.0969        | 4.0   | 7500  | 0.1759          | 0.6886    | 0.6621 | 0.9350   | 0.6751 |
| 0.0837        | 5.0   | 9375  | 0.1967          | 0.6688    | 0.7112 | 0.9348   | 0.6893 |
| 0.0746        | 6.0   | 11250 | 0.2088          | 0.6646    | 0.7114 | 0.9334   | 0.6872 |
| 0.0666        | 7.0   | 13125 | 0.2169          | 0.6713    | 0.7054 | 0.9347   | 0.6879 |
| 0.0634        | 8.0   | 15000 | 0.2199          | 0.6743    | 0.6979 | 0.9346   | 0.6859 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"language": ["fr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "Le pr\u00e9sident de la R\u00e9publique appelle en outre les Fran\u00e7ais \u00e0 faire le choix d'une \"majorit\u00e9 stable et s\u00e9rieuse pour les prot\u00e9ger face aux crises et pour agir pour l'avenir\". \"Je vois dans le projet de Jean-Luc M\u00e9lenchon ou de Madame Le Pen un projet de d\u00e9sordre et de soumission. Ils expliquent qu'il faut sortir de nos alliances, de l'Europe, et b\u00e2tir des alliances strat\u00e9giques avec la Russie. C'est la soumission \u00e0 la Russie\", assure-t-il."}, {"text": "Top d\u00e9part \u00e0 l\u2019ouverture des bureaux de vote. La Polyn\u00e9sie et les Fran\u00e7ais r\u00e9sidant \u00e0 l'\u00e9tranger, dont certains ont d\u00e9j\u00e0 pu voter en ligne, sont invit\u00e9s aux urnes ce week-end pour le premier tour des l\u00e9gislatives, samedi 4 juin pour le continent am\u00e9ricain et les Cara\u00efbes, et dimanche 5 juin pour le reste du monde. En France m\u00e9tropolitaine, les premier et second tours auront lieu les 12 et 19 juin."}, {"text": "Le minist\u00e8re a aussi indiqu\u00e9 que des missiles russes ont frapp\u00e9 un centre d'entra\u00eenement d'artillerie dans la r\u00e9gion de Soumy o\u00f9 travaillaient des instructeurs \u00e9trangers. Il a jout\u00e9 qu'une autre frappe avait d\u00e9truit une position de \"mercenaires \u00e9trangers\" dans la r\u00e9gion d'Odessa."}, {"text": "Le malaise est profond et ressemble \u00e0 une crise existentielle. Fait rarissime au Quai d\u2019Orsay, six syndicats et un collectif de 500 jeunes diplomates du minist\u00e8re des Affaires \u00e9trang\u00e8res ont appel\u00e9 \u00e0 la gr\u00e8ve, jeudi 2 juin, pour protester contre la r\u00e9forme de la haute fonction publique qui, \u00e0 terme, entra\u00eenera la disparition des deux corps historiques de la diplomatie fran\u00e7aise : celui de ministre pl\u00e9nipotentiaire (ambassadeur) et celui de conseiller des affaires \u00e9trang\u00e8res."}, {"text": "Ils se font passer pour des recruteurs de Lockheed Martin ou du g\u00e9ant britannique de la d\u00e9fense et de l\u2019a\u00e9rospatial BAE Systems. Ces soi-disant chasseurs de t\u00eate font miroiter des perspectives lucratives de carri\u00e8re et des postes \u00e0 responsabilit\u00e9. Mais ce n\u2019est que du vent. En r\u00e9alit\u00e9, il s\u2019agit de cyberespions nord-cor\u00e9ens cherchant \u00e0 voler des secrets industriels de groupes de d\u00e9fense ou du secteur de l\u2019a\u00e9rospatial, r\u00e9v\u00e8le Eset, une soci\u00e9t\u00e9 slovaque de s\u00e9curit\u00e9 informatique, dans un rapport publi\u00e9 mardi 31 mai."}], "model-index": [{"name": "camembert-keyword-extractor", "results": []}]}
transformers pytorch camembert token-classification generated_from_trainer fr license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us 

camembert-keyword-extractor
===========================


This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2199
* Precision: 0.6743
* Recall: 0.6979
* Accuracy: 0.9346
* F1: 0.6859


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: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### 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: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1