Instructions to use wesleyaag/data2vec-emotion-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleyaag/data2vec-emotion-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleyaag/data2vec-emotion-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleyaag/data2vec-emotion-test") model = AutoModelForSequenceClassification.from_pretrained("wesleyaag/data2vec-emotion-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c60597db06d57d74cd3843bc9bf2db6b59fd6e02ed19f99245c98bb14831b73b
- Size of remote file:
- 499 MB
- SHA256:
- 8cad7fc8db2372d9585d37ebe597cefe59238a281db73fddbaa6d7680d7941be
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