Instructions to use savasy/bert-turkish-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use savasy/bert-turkish-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="savasy/bert-turkish-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("savasy/bert-turkish-text-classification") model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-turkish-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from savasy/bert-turkish-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/savasy/bert-turkish-text-classification/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://savasy/bert-turkish-text-classification/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/savasy/bert-turkish-text-classification/resolve/main/flax_model.msgpack
442 MB
- Xet hash:
- 538849e7cbbc4da246065e80c145a9e2380651d741af703003dd179b571c4a0d
- Size of remote file:
- 442 MB
- SHA256:
- 7c9845c0fac6b789f6d8796a0c9f77875eecfdfc57e244709057f535cbb633c3
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