Instructions to use HooshvareLab/bert-fa-zwnj-base-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-zwnj-base-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/bert-fa-zwnj-base-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-zwnj-base-ner") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/bert-fa-zwnj-base-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/bert-fa-zwnj-base-ner: direct link, hf CLI and curl.
- Browser
- Download file 471 MB
-
https://huggingface.co/HooshvareLab/bert-fa-zwnj-base-ner/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/bert-fa-zwnj-base-ner/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/bert-fa-zwnj-base-ner/resolve/main/flax_model.msgpack
471 MB
- Xet hash:
- 8df88696ff16f364366bd481a223bca6d20f2cae762ed13d1afe1567719c0162
- Size of remote file:
- 471 MB
- SHA256:
- 5d9e3bb2a80a63702f51e77146aba8c8df4c71258b695df29d6bd6b61ce8a723
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