Text Classification
Transformers
PyTorch
xlm-roberta
language-detection
Frisian
Dhivehi
Hakha_Chin
Kabyle
Sakha
text-embeddings-inference
Instructions to use Mike0307/multilingual-e5-language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mike0307/multilingual-e5-language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mike0307/multilingual-e5-language-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mike0307/multilingual-e5-language-detection") model = AutoModelForSequenceClassification.from_pretrained("Mike0307/multilingual-e5-language-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from Mike0307/multilingual-e5-language-detection: direct link, hf CLI and curl.
- Browser
- Download file 5.07 MB
-
https://huggingface.co/Mike0307/multilingual-e5-language-detection/resolve/refs%2Fpr%2F1/sentencepiece.bpe.model
- Command line
-
hf download hf://Mike0307/multilingual-e5-language-detection@refs/pr/1/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/Mike0307/multilingual-e5-language-detection/resolve/refs%2Fpr%2F1/sentencepiece.bpe.model
5.07 MB
- Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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