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:
- 6c2fefc633fb17cda530c607305cfefee5451254f1ff37dba13346480cae49c7
- Size of remote file:
- 3.45 kB
- SHA256:
- 977f6a705e4081304247b67b66dd00c1d5360103144d7e5df39ec104b9ff83fd
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