Instructions to use savasy/offLangDetectionTurkish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use savasy/offLangDetectionTurkish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="savasy/offLangDetectionTurkish")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("savasy/offLangDetectionTurkish") model = AutoModelForTokenClassification.from_pretrained("savasy/offLangDetectionTurkish", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from savasy/offLangDetectionTurkish: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/savasy/offLangDetectionTurkish/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://savasy/offLangDetectionTurkish/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/savasy/offLangDetectionTurkish/resolve/main/flax_model.msgpack
440 MB
- Xet hash:
- dfe86227477f32061404d152b310c932e2b7e66580b90f1688e9193a7c74d49e
- Size of remote file:
- 440 MB
- SHA256:
- e9715ef8ca1f8dbb1651d9745291f96041c02b35a78055530eed1be3ac5c4108
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.