Text Classification
Transformers
LiteRT
ONNX
Safetensors
bert
language
detection
classification
text-embeddings-inference
Instructions to use dewdev/language_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dewdev/language_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dewdev/language_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dewdev/language_detection") model = AutoModelForSequenceClassification.from_pretrained("dewdev/language_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 452b5c0b5692a62128fc74354d9d598ea434f6593c19e6034960d85650f5af07
- Size of remote file:
- 97.8 MB
- SHA256:
- ec3137634f58a55ae6127d61d12d4aa05c92380852909c1160e03f82f51a8a68
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