Instructions to use Ghunghru/gbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ghunghru/gbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ghunghru/gbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ghunghru/gbert-base") model = AutoModelForSequenceClassification.from_pretrained("Ghunghru/gbert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ghunghru/gbert-base: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/Ghunghru/gbert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ghunghru/gbert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ghunghru/gbert-base/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- db81f9591d786bf438b6f5cc718eb85899e686573dcbd057126e48000d75fd45
- Size of remote file:
- 440 MB
- SHA256:
- 84849bd02d4dd24ce5c780a2d2433650ca6877dd68a17c7dfa05dbfa4a03b177
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