Instructions to use fgaim/tibert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fgaim/tibert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fgaim/tibert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fgaim/tibert-base") model = AutoModelForMaskedLM.from_pretrained("fgaim/tibert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fgaim/tibert-base: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/fgaim/tibert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fgaim/tibert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fgaim/tibert-base/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 23eb33cdd0f94ad19635a7418ed8e0081ad510b261418dfa2ccfffac08fddb73
- Size of remote file:
- 438 MB
- SHA256:
- 9f4366f4005a2c9744f1896b18c7a02f4030d321bb964756c094e267fecddfde
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