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:
- d16933fff418ef28b95e52824d815df22819ea6115c60e4d5bfb196169ae6fc9
- Size of remote file:
- 5.37 kB
- SHA256:
- ff5385b395f04e9901f916d3402f1e5ff5d43696fff3fe0a118a028e38af018d
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