Edit model card

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.

paper, slides, video

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
Downloads last month
11
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.