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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fgaim/tibert-base") model = AutoModelForMaskedLM.from_pretrained("fgaim/tibert-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1b5b657fa5f4f453ec1ecc48ff284c02695ad2d295590ce9ff61ba938171586
- Size of remote file:
- 438 MB
- SHA256:
- 3711b1e43d645c1d2bb421e869f0c3a9394a9c9a0a360da494cda744ef51a54d
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