Text Classification
Transformers
PyTorch
English
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use futuredatascience/multiclass_message_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use futuredatascience/multiclass_message_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="futuredatascience/multiclass_message_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("futuredatascience/multiclass_message_classifier") model = AutoModelForSequenceClassification.from_pretrained("futuredatascience/multiclass_message_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d362fe40fb4b75c7d2d0e38af642ebdef2c0d2d681059a349e0ae4a10866fbfb
- Size of remote file:
- 8.66 MB
- SHA256:
- 8e3c28173d512c595ae9c4056e9e01b80670c0bb412995522689ebd8e3f00aeb
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