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emotion-recognition
tahiyacy/emotion-recognition
emotion-recognition is a feature extraction model from tahiyacy. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as creativeml-openrail-m.
This model is a Perceiver-based (https://huggingface.co/docs/transformers/modeldoc/perceiver) emotion recognition model trained on RAVDESS dataset (https://zenodo.org/record/1188976.Y5iqPy2B1QI). The model is trained…
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At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- creativeml-openrail-m
- Model type
- perceiver
- Access
- Public
- Created
- Dec 13, 2022
- Updated
- Apr 8, 2023
- SHA
- 532c3598
Datasets
Model card
From the Hugging Face model README
Perceiver-based Emotion Recognition
This model is a Perceiver-based (https://huggingface.co/docs/transformers/model_doc/perceiver) emotion recognition model trained on RAVDESS dataset (https://zenodo.org/record/1188976#.Y5iqPy2B1QI). The model is trained using 3 modalities: video, audio, and text.
For details on the data collection, check here: https://zenodo.org/record/1188976
The feature extraction for each modality and training procedure follows the steps mentioned here: https://dl.acm.org/doi/10.1145/3551876.3554806
Intended uses
You can use the raw model for directly recognize emotion (classes: 01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised) or fine-tune on a downstream task.
Limitations
The model is trained on only one dataset and uses 8 specific classes of emotions. The limitation lies in the lack of diversity in the demographics and emotions.
- Task
- Feature Extraction
- Library
- transformers
- Type
- perceiver
- License
- creativeml-openrail-m
- Languages
- en
- Created
- Dec 13, 2022
- Updated
- Apr 8, 2023