---
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