Instructions to use kanad13/emotion_detection_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanad13/emotion_detection_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kanad13/emotion_detection_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kanad13/emotion_detection_model") model = AutoModelForSequenceClassification.from_pretrained("kanad13/emotion_detection_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from kanad13/emotion_detection_model: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/kanad13/emotion_detection_model/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://kanad13/emotion_detection_model/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/kanad13/emotion_detection_model/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- c46c01bee8fe5e0022634c38768292fc2508b79434d51a659b1dbd8e087878f3
- Size of remote file:
- 438 MB
- SHA256:
- a0ba97a0dc911aae90a970f4ff78819cec81f92668f91a2c1b7b3cf3902d73c1
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