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gustavecortal/
distilcamembert-cae-component

Name: distilcamembert-cae-component
Availability: InStock

generated_from_trainer

mit

autotrain_compatible

endpoints_compatible

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Gustave Cortal, Alain Finkel, Patrick Paroubek, Lina Ye. May 2023. Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation. In Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 72–81, Dubrovnik, Croatia. Association for Computational Linguistics.

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distilcamembert-cae-component

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

Loss: 0.3683
Precision: 0.9317
Recall: 0.9303
F1: 0.9306

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.6221	1.0	309	0.3860	0.9007	0.8720	0.8761
0.1723	2.0	618	0.3505	0.9233	0.9157	0.9168
0.0604	3.0	927	0.3683	0.9317	0.9303	0.9306
0.0117	4.0	1236	0.4214	0.9311	0.9303	0.9304
0.0061	5.0	1545	0.4232	0.9317	0.9303	0.9305

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2

gustavecortal/distilcamembert-cae-component

distilcamembert-cae-component

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gustavecortal/
distilcamembert-cae-component