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
Add trained and optimized model files
Browse files
tf_emotion_sequence_transformer_mp478_seq256.h5
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oid sha256:6e3c783a4c1e89b467a5e0b98cac83b56237ea4428b0126e26f23858cf6146b7
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tf_emotion_sequence_transformer_mp478_seq256_optimized.tflite
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oid sha256:4f3106cfe80841c3e4ae0f9a22778ffff6f29b442876adb4477ea334a2e9f534
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size 45787832
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