Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
nlu
intent-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use cartesinus/multilingual_minilm-amazon-massive-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cartesinus/multilingual_minilm-amazon-massive-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cartesinus/multilingual_minilm-amazon-massive-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/multilingual_minilm-amazon-massive-intent") model = AutoModelForSequenceClassification.from_pretrained("cartesinus/multilingual_minilm-amazon-massive-intent") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18ec51a888290973546055024ab6bad4e01537350045cfccd1868fb4e9c1b5cc
- Size of remote file:
- 471 MB
- SHA256:
- e191fdbbe9b1cad05a36d36a7554be95d496f692410619502f45b2c08e363431
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