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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: distilcamembert-cae-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
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](https://aclanthology.org/2023.latechclfl-1.8/), [slides](https://gustavecortal.com/data/Emotion_Recognition_based_on_Psychological_Components_slides.pdf), [video](https://underline.io/lecture/71953-emotion-recognition-based-on-psychological-components-in-guided-narratives-for-emotion-regulation)
# distilcamembert-cae-all
This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6016
- Precision: 0.8510
- Recall: 0.8481
- F1: 0.8471
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 1.18 | 1.0 | 40 | 0.9901 | 0.6418 | 0.4557 | 0.2991 |
| 0.8718 | 2.0 | 80 | 0.6938 | 0.7667 | 0.7468 | 0.7196 |
| 0.4656 | 3.0 | 120 | 0.6928 | 0.8364 | 0.8354 | 0.8353 |
| 0.2418 | 4.0 | 160 | 0.6008 | 0.8276 | 0.8228 | 0.8228 |
| 0.1285 | 5.0 | 200 | 0.6016 | 0.8510 | 0.8481 | 0.8471 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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