Instructions to use vashuag/HindiEmotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vashuag/HindiEmotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vashuag/HindiEmotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vashuag/HindiEmotion") model = AutoModelForSequenceClassification.from_pretrained("vashuag/HindiEmotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1853704fb62c948bae1c72187d1832d8f360cb21d302f73369fa26c2ce5b2f14
- Size of remote file:
- 134 MB
- SHA256:
- e88d3ef45479b0d106b4527476380a321e922ee7cd6944f3617386f2a8d5a8bc
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