Instructions to use Osiris/emotion_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Osiris/emotion_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Osiris/emotion_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Osiris/emotion_classifier") model = AutoModelForSequenceClassification.from_pretrained("Osiris/emotion_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f09e8869d049e6c391ce14a60b04df82ee56fe74125ccc64eb46b111aa327eb7
- Size of remote file:
- 499 MB
- SHA256:
- 53635fb00832cb39872b55f854618077020fc291418df981768e35c37a555a12
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.