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")# pip install -U transformers accelerate # 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
Download onnx/model_static_quantized.onnx from dewdev/language_detection: direct link, hf CLI and curl.
- Browser
- Download file 24.8 MB
-
https://huggingface.co/dewdev/language_detection/resolve/main/onnx/model_static_quantized.onnx
- Command line
-
hf download hf://dewdev/language_detection/onnx/model_static_quantized.onnx
-
curl -L -o model_static_quantized.onnx https://huggingface.co/dewdev/language_detection/resolve/main/onnx/model_static_quantized.onnx
24.8 MB
- Xet hash:
- 3f78f39dda2cc913a3ec5f773c6ad15e5e0da616e6b6e13160ebaca53c1c52b9
- Size of remote file:
- 24.8 MB
- SHA256:
- 210bb86f405787fc0ceb548c3ec0a81d4735680add5b79572e80e01b7acc7125
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