Instructions to use Sitata/lang-detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sitata/lang-detect with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sitata/lang-detect")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sitata/lang-detect") model = AutoModelForSequenceClassification.from_pretrained("Sitata/lang-detect", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Sitata/lang-detect: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
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https://huggingface.co/Sitata/lang-detect/resolve/main/README.md
- Command line
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hf download hf://Sitata/lang-detect/README.md
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curl -L -o README.md https://huggingface.co/Sitata/lang-detect/resolve/main/README.md
1.05 kB
| # bert-base-multilingual-cased-language-detection | |
| A model for language detection with support for 45 languages | |
| ## Model description | |
| This model was created by fine-tuning | |
| [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the [common language](https://huggingface.co/datasets/common_language) dataset. | |
| This dataset has support for 45 languages, which are listed below: | |
| ``` | |
| Arabic, Basque, Breton, Catalan, Chinese_China, Chinese_Hongkong, Chinese_Taiwan, Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, French, Frisian, Georgian, German, Greek, Hakha_Chin, Indonesian, Interlingua, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Maltese, Mongolian, Persian, Polish, Portuguese, Romanian, Romansh_Sursilvan, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Ukranian, Welsh | |
| ``` | |
| ## Evaluation | |
| This model was evaluated on the test split of the [common language](https://huggingface.co/datasets/common_language) dataset, and achieved the following metrics: | |
| * Accuracy: 97.8% | |