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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c5f8c2e8897104d638e17f08d40ca1b4c58ebe129e191c7425419c0230f8603
- Size of remote file:
- 738 MB
- SHA256:
- a236d6bd9df3ef8952a2879788c49f894a902a86455a86f94f0b5f9f73974395
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