camembert-allocine – AI Model by baptiste-pasquier | AlphaNeural AI

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baptiste-pasquier/
camembert-allocine

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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camembert-allocine

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

Loss: 0.0928
Accuracy: 0.9754
F1: 0.9749
Precision: 0.9723
Recall: 0.9775

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
0.1276	0.2	500	0.1187	0.9623	0.9622	0.9462	0.9787
0.1013	0.4	1000	0.0917	0.9683	0.9675	0.9725	0.9625
0.1254	0.6	1500	0.0889	0.9701	0.9698	0.9597	0.9801
0.1004	0.8	2000	0.0792	0.9716	0.9709	0.9727	0.9691
0.1149	1.0	2500	0.0762	0.9727	0.9723	0.9673	0.9773
0.0574	1.2	3000	0.0849	0.9733	0.9729	0.9679	0.9780
0.0394	1.4	3500	0.1026	0.9718	0.9715	0.9595	0.9839
0.0401	1.6	4000	0.1065	0.9698	0.9697	0.9528	0.9872
0.0458	1.8	4500	0.0834	0.9744	0.9739	0.9715	0.9764
0.0554	2.0	5000	0.0873	0.9719	0.9717	0.9594	0.9844
0.0516	2.2	5500	0.0928	0.9754	0.9749	0.9723	0.9775
0.0355	2.4	6000	0.1017	0.9744	0.9741	0.9642	0.9842
0.0227	2.6	6500	0.0983	0.9748	0.9743	0.9729	0.9757
0.0359	2.8	7000	0.0990	0.9747	0.9743	0.9665	0.9823
0.0384	3.0	7500	0.1001	0.9746	0.9742	0.9662	0.9824

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.10.1
Tokenizers 0.13.2