Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
deberta-v2
Generated from Trainer
nlu
intent-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use cartesinus/mdeberta-v3-base_amazon-massive_intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cartesinus/mdeberta-v3-base_amazon-massive_intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cartesinus/mdeberta-v3-base_amazon-massive_intent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/mdeberta-v3-base_amazon-massive_intent") model = AutoModelForSequenceClassification.from_pretrained("cartesinus/mdeberta-v3-base_amazon-massive_intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Add base_model information to model (#2)
- Librarian Bot: Add base_model information to model (3f843ef224829d9a1951abcec2b3190baf4c092b) Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>f3fbe01
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (5c2d966972826321f2da3c02c15516c314ec15cf) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>03fb33d
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End of training
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Training in progress, step 500
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initial commit
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