Instructions to use savasy/bert-base-turkish-sentiment-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use savasy/bert-base-turkish-sentiment-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="savasy/bert-base-turkish-sentiment-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased") model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from savasy/bert-base-turkish-sentiment-cased: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/savasy/bert-base-turkish-sentiment-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://savasy/bert-base-turkish-sentiment-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/savasy/bert-base-turkish-sentiment-cased/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 211bff5df4bbff2d5da536baa4abbf4b3c0191e036d9160f7e909b74a4a8e74c
- Size of remote file:
- 443 MB
- SHA256:
- d70cb523285fcdb88057254781270fedc247b6fadbc9923f3d0e8bf5ac52352c
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