Instructions to use HalogenFlo/microsoft-deberta-v3-base-emotion-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HalogenFlo/microsoft-deberta-v3-base-emotion-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HalogenFlo/microsoft-deberta-v3-base-emotion-recognition")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HalogenFlo/microsoft-deberta-v3-base-emotion-recognition") model = AutoModelForSequenceClassification.from_pretrained("HalogenFlo/microsoft-deberta-v3-base-emotion-recognition") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f899474b894a92cc5bec9aee3ceb2e4c1bf1b178c91c74f3d2acdc2b982dafd3
- Size of remote file:
- 5.84 kB
- SHA256:
- d87bf867be511818dbf6fe30eacfc4201a74c48b4c3c31429f130c21614c0483
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.