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# distilcamembert-allocine

This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the allocine dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1066
- Accuracy: 0.9714
- F1: 0.9710
- Precision: 0.9648
- Recall: 0.9772

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |   F1   | Precision | Recall |
| :-----------: | :---: | :---: | :-------------: | :------: | :----: | :-------: | :----: |
|    0.1504     |  0.2  |  500  |     0.1290      |  0.9555  | 0.9542 |  0.9614   | 0.9470 |
|    0.1334     |  0.4  | 1000  |     0.1049      |  0.9624  | 0.9619 |  0.9536   | 0.9703 |
|    0.1158     |  0.6  | 1500  |     0.1052      |  0.963   | 0.9627 |  0.9498   | 0.9760 |
|    0.1153     |  0.8  | 2000  |     0.0949      |  0.9661  | 0.9653 |  0.9686   | 0.9620 |
|    0.1053     |  1.0  | 2500  |     0.0936      |  0.9666  | 0.9663 |  0.9542   | 0.9788 |
|    0.0755     |  1.2  | 3000  |     0.0987      |   0.97   | 0.9695 |  0.9644   | 0.9748 |
|    0.0716     |  1.4  | 3500  |     0.1078      |  0.9688  | 0.9684 |  0.9598   | 0.9772 |
|    0.0688     |  1.6  | 4000  |     0.1051      |  0.9673  | 0.9670 |  0.9552   | 0.9792 |
|    0.0691     |  1.8  | 4500  |     0.0940      |  0.9709  | 0.9704 |  0.9688   | 0.9720 |
|    0.0733     |  2.0  | 5000  |     0.1038      |  0.9686  | 0.9683 |  0.9558   | 0.9812 |
|    0.0476     |  2.2  | 5500  |     0.1066      |  0.9714  | 0.9710 |  0.9648   | 0.9772 |
|     0.047     |  2.4  | 6000  |     0.1098      |  0.9689  | 0.9686 |  0.9587   | 0.9788 |
|    0.0431     |  2.6  | 6500  |     0.1110      |  0.9711  | 0.9706 |  0.9666   | 0.9747 |
|    0.0464     |  2.8  | 7000  |     0.1149      |  0.9697  | 0.9694 |  0.9592   | 0.9798 |
|    0.0342     |  3.0  | 7500  |     0.1122      |  0.9703  | 0.9699 |  0.9621   | 0.9778 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"language": ["fr"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["allocine"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Un film magnifique avec un duo d'acteurs excellent."}, {"text": "Grosse d\u00e9ception pour ce thriller qui peine \u00e0 convaincre."}], "model-index": [{"name": "distilcamembert-allocine", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "allocine", "type": "allocine", "config": "allocine", "split": "validation", "args": "allocine"}, "metrics": [{"type": "accuracy", "value": 0.9714, "name": "Accuracy"}, {"type": "f1", "value": 0.9709909727152854, "name": "F1"}, {"type": "precision", "value": 0.9648256399919372, "name": "Precision"}, {"type": "recall", "value": 0.9772356063699469, "name": "Recall"}]}]}]}
transformers pytorch tf tensorboard camembert text-classification generated_from_trainer fr dataset:allocine license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #tensorboard #camembert #text-classification #generated_from_trainer #fr #dataset-allocine #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilcamembert-allocine
========================


This model is a fine-tuned version of cmarkea/distilcamembert-base on the allocine dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1066
* Accuracy: 0.9714
* F1: 0.9710
* Precision: 0.9648
* Recall: 0.9772


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tf #tensorboard #camembert #text-classification #generated_from_trainer #fr #dataset-allocine #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tf #tensorboard #camembert #text-classification #generated_from_trainer #fr #dataset-allocine #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2