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 pytorch_model.bin from HooshvareLab/bert-fa-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 654 MB
-
https://huggingface.co/HooshvareLab/bert-fa-base-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HooshvareLab/bert-fa-base-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HooshvareLab/bert-fa-base-uncased/resolve/main/pytorch_model.bin
654 MB
- Xet hash:
- bdabc18cdb55002ebb93cb893ff11794144a5d22ccae3cab1ab3cfecf5e6a09c
- Size of remote file:
- 654 MB
- SHA256:
- 4e0276452d76633de56e2eedc35d39f3dfe8d3038a2a192877787ae83105726e
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