Instructions to use HooshvareLab/bert-fa-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HooshvareLab/bert-fa-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased") model = AutoModelForMaskedLM.from_pretrained("HooshvareLab/bert-fa-base-uncased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c2d24d43f31a17300a4c62bb5b5b334941f236a0ffc712a70190383aa43f95e
- Size of remote file:
- 963 MB
- SHA256:
- 2fb4f401473532d81d88ffba541cf441ba55d01320d364c6858bcf76fcbd7328
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