Text Classification
Transformers
PyTorch
xlm-roberta
language classification
text-embeddings-inference
Instructions to use nikitast/lang-classifier-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikitast/lang-classifier-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikitast/lang-classifier-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikitast/lang-classifier-roberta") model = AutoModelForSequenceClassification.from_pretrained("nikitast/lang-classifier-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
bc8bf9d
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Add multilingual to the language tag (#1)
- Add multilingual to the language tag (94eced26d8760a471168f8991d6298dd1398779f) Co-authored-by: Loïck BOURDOIS <lbourdois@users.noreply.huggingface.co>7c97c98
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nice readme
33ed588
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Update README.md
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Update README.md
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Create README.md
83ce11f
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add tokenizer
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add model
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initial commit
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