Text Classification
Transformers
PyTorch
xlm-roberta
language classification
text-embeddings-inference
Instructions to use nikitast/multilang-classifier-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikitast/multilang-classifier-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikitast/multilang-classifier-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikitast/multilang-classifier-roberta") model = AutoModelForSequenceClassification.from_pretrained("nikitast/multilang-classifier-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nikitast/multilang-classifier-roberta: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/nikitast/multilang-classifier-roberta/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://nikitast/multilang-classifier-roberta@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nikitast/multilang-classifier-roberta/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 31cfad7e457e392bdebe2bd63796205ff3f6ab825e13da0a03d83dfbf932c919
- Size of remote file:
- 17.1 MB
- SHA256:
- 62c24cdc13d4c9952d63718d6c9fa4c287974249e16b7ade6d5a85e7bbb75626
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