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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/bert-fa-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 652 MB
-
https://huggingface.co/HooshvareLab/bert-fa-base-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/bert-fa-base-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/bert-fa-base-uncased/resolve/main/flax_model.msgpack
652 MB
- Xet hash:
- b48637557b6f9935659be6a1e43956c92822c242744af4b3509ee41ba557ed77
- Size of remote file:
- 652 MB
- SHA256:
- 3bff76cfe00483d8881fef769fbfac1a2fcd22341af870bf464c9c1fe9b8ae28
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