Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use papluca/xlm-roberta-base-language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use papluca/xlm-roberta-base-language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("papluca/xlm-roberta-base-language-detection") model = AutoModelForSequenceClassification.from_pretrained("papluca/xlm-roberta-base-language-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ab1184433fcf871dd1fba61a4ef9e6e7559954563a092a944a620d752873368
- Size of remote file:
- 1.11 GB
- SHA256:
- d6417044a1451c9a5fd302579ee5d39bae3831b0cd57bd008b61e79d33156f6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.