Instructions to use wesleyaag/data2vec-emo-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleyaag/data2vec-emo-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleyaag/data2vec-emo-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleyaag/data2vec-emo-test") model = AutoModelForSequenceClassification.from_pretrained("wesleyaag/data2vec-emo-test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 692a99878f20ee4daa9db41f90377a8809d2281c04389e110f7e242334ebdc5f
- Size of remote file:
- 499 MB
- SHA256:
- 93111295194f4dff98f491a200dd3f8ae37555b18f5a1c3419b9c549a3939d4d
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