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 tracking file types
Browse files- .gitattributes +3 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
*.png filter=lfs diff=lfs merge=lfs -text
|
| 37 |
+
*.json filter=lfs diff=lfs merge=lfs -text
|
| 38 |
+
*.ipynb filter=lfs diff=lfs merge=lfs -text
|