Instructions to use HooshvareLab/bert-fa-zwnj-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-zwnj-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HooshvareLab/bert-fa-zwnj-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-zwnj-base") model = AutoModelForMaskedLM.from_pretrained("HooshvareLab/bert-fa-zwnj-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/bert-fa-zwnj-base: direct link, hf CLI and curl.
- Browser
- Download file 473 MB
-
https://huggingface.co/HooshvareLab/bert-fa-zwnj-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/bert-fa-zwnj-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/bert-fa-zwnj-base/resolve/main/flax_model.msgpack
473 MB
- Xet hash:
- 0b3759ff1fe24200365176b530441a755a8195d22c29eff80d63bd20b7185c84
- Size of remote file:
- 473 MB
- SHA256:
- cd68fdde13c9b3d3b737ab8e162247c1e60a6796a05743bf186cd406e7ea08ad
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