Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
xlm-roberta
Generated from Trainer
nlu
slot-tagging
Eval Results (legacy)
Instructions to use cartesinus/xlm-r-base-amazon-massive-slot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cartesinus/xlm-r-base-amazon-massive-slot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cartesinus/xlm-r-base-amazon-massive-slot")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/xlm-r-base-amazon-massive-slot") model = AutoModelForTokenClassification.from_pretrained("cartesinus/xlm-r-base-amazon-massive-slot") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d654487e084b0c217a5c522989109a3f988eb7d17099cb1c38b02e785fcd8bd8
- Size of remote file:
- 3.38 kB
- SHA256:
- 1575b6c900cc79a3dd4675863a86778d56255ec0b9f46db264d9b1a0e14a8bdf
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