Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
xlm-roberta
Generated from Trainer
nlu
intent-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use cartesinus/xlm-r-base-amazon-massive-intent-label_smoothing 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-intent-label_smoothing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cartesinus/xlm-r-base-amazon-massive-intent-label_smoothing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/xlm-r-base-amazon-massive-intent-label_smoothing") model = AutoModelForSequenceClassification.from_pretrained("cartesinus/xlm-r-base-amazon-massive-intent-label_smoothing") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): c8be175
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README.md
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- name: xlm-r-base-amazon-massive-intent-label_smoothing
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results:
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dataset:
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name: MASSIVE
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type: AmazonScience/massive
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- name: xlm-r-base-amazon-massive-intent-label_smoothing
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results:
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- task:
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name: intent-classification
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type: intent-classification
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dataset:
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name: MASSIVE
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type: AmazonScience/massive
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