Text Classification
Transformers
PyTorch
Transformers
intent-classification
multi-class-classification
natural-language-understanding
Instructions to use qanastek/XLMRoberta-Alexa-Intents-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qanastek/XLMRoberta-Alexa-Intents-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="qanastek/XLMRoberta-Alexa-Intents-Classification")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("qanastek/XLMRoberta-Alexa-Intents-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from qanastek/XLMRoberta-Alexa-Intents-Classification: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/qanastek/XLMRoberta-Alexa-Intents-Classification/resolve/main/training_args.bin
- Command line
-
hf download hf://qanastek/XLMRoberta-Alexa-Intents-Classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/qanastek/XLMRoberta-Alexa-Intents-Classification/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- d562d2f0c215b610b208300abcce28dd3b29b9f5e4b3283671100772d4b37aec
- Size of remote file:
- 3.06 kB
- SHA256:
- 2137f21f16a962c5f49f81d2aa3f5bfa3f22cc56ac06bd4b0e7c88d705169139
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.