Instructions to use HooshvareLab/bert-fa-base-uncased-ner-arman with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-fa-base-uncased-ner-arman with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/bert-fa-base-uncased-ner-arman")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-fa-base-uncased-ner-arman") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/bert-fa-base-uncased-ner-arman", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/bert-fa-base-uncased-ner-arman: direct link, hf CLI and curl.
- Browser
- Download file 649 MB
-
https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-arman/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/bert-fa-base-uncased-ner-arman/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/bert-fa-base-uncased-ner-arman/resolve/main/flax_model.msgpack
649 MB
- Xet hash:
- 1017f611895fc50cf665a6c74006e3cf9e818c8319c79c09d724bd61224ac292
- Size of remote file:
- 649 MB
- SHA256:
- c54865ec2f58b3d8fefdaca07173ff3477f9b46e99ea2b8fcc9d858ffb901641
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