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:
- d2cb140fbb1d664b02a5c375eac84b3673a3397803a1acc955c5bda7f4177f1c
- Size of remote file:
- 57.1 kB
- SHA256:
- a4ec2fdf14d79cf3ae6e3b295f1f9551fe73a71fb07ebb5ae1c7d4d663e44bdf
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