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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikitast/lang-classifier-roberta") model = AutoModelForSequenceClassification.from_pretrained("nikitast/lang-classifier-roberta") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26eef44929977aa753341cdaf63ce1da4ac04921bb0d407ee0202d3f5588065e
- Size of remote file:
- 1.11 GB
- SHA256:
- fb82a294e1851f1483fa192265dd1bccb9535e072d78f906280a1a3114144394
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.