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 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 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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cartesinus/xlm-r-base-amazon-massive-intent") model = AutoModelForSequenceClassification.from_pretrained("cartesinus/xlm-r-base-amazon-massive-intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de0ef30a1f178bec6a324effb987112ed62d89ca4b084fc530086f6c6de8a4fb
- Size of remote file:
- 1.11 GB
- SHA256:
- 7de5627d7667a2cebd4b398c961deb7d01929734561fa31027552d391ba1c86e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.