metadata

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 déception pour ce thriller qui peine à convaincre.
base_model: cmarkea/distilcamembert-base
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

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

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