LiteRT
Keras
English
tensorflow
emotion-recognition
transformer
lstm
mediapipe
computer-vision
deep-learning
facial-expression
affective-computing
sequential-data
Eval Results (legacy)
Instructions to use PSewmuthu/EmotionFormer-BiLSTM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use PSewmuthu/EmotionFormer-BiLSTM with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://PSewmuthu/EmotionFormer-BiLSTM") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- f39681776a54683d2c3d28ae5a575397c93274e8aba0369c45813ff13b3829bb
- Size of remote file:
- 62.3 kB
- SHA256:
- bbcb56770f25b67c2b9923eb34ba3d4532a599fe83d94576c37047f1df0047b3
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