Text Classification
Transformers
PyTorch
TensorFlow
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use eleldar/language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eleldar/language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eleldar/language-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eleldar/language-detection") model = AutoModelForSequenceClassification.from_pretrained("eleldar/language-detection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1836d6607e2ff86a952ad74d6f4860102a424ee5a2bfa6c904b7e2d152f4f8b5
- Size of remote file:
- 1.11 GB
- SHA256:
- eb6bded160fdd712245e1bd19c4de417e1508094a9f69d92ae287f32a8732888
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