Instructions to use suku9/emotion_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suku9/emotion_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="suku9/emotion_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("suku9/emotion_classifier") model = AutoModelForSequenceClassification.from_pretrained("suku9/emotion_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from suku9/emotion_classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/suku9/emotion_classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://suku9/emotion_classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/suku9/emotion_classifier/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 95f3c2dbdb4db88c84a0735e45f181967464c6acefa4e7188592fe76e43ead86
- Size of remote file:
- 5.37 kB
- SHA256:
- 5b64e2e3ee8f097a410b8123c3100887a4c94da1f8abf657b78cacea0188b9c1
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