Instructions to use Manirathinam21/Multilingual_lang_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manirathinam21/Multilingual_lang_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Manirathinam21/Multilingual_lang_detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Manirathinam21/Multilingual_lang_detection") model = AutoModelForSequenceClassification.from_pretrained("Manirathinam21/Multilingual_lang_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Manirathinam21/Multilingual_lang_detection: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/Manirathinam21/Multilingual_lang_detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Manirathinam21/Multilingual_lang_detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Manirathinam21/Multilingual_lang_detection/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 2df20b755df0130c34380f3a92011802fcc3886d45f9b202734bf5716abc4073
- Size of remote file:
- 1.11 GB
- SHA256:
- 8fad897681425781c0f72be7effd6531261f0f266320e11777fcb625c24a84a1
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