Instructions to use HooshvareLab/roberta-fa-zwnj-base-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/roberta-fa-zwnj-base-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/roberta-fa-zwnj-base-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/roberta-fa-zwnj-base-ner") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/roberta-fa-zwnj-base-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/roberta-fa-zwnj-base-ner: direct link, hf CLI and curl.
- Browser
- Download file 471 MB
-
https://huggingface.co/HooshvareLab/roberta-fa-zwnj-base-ner/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/roberta-fa-zwnj-base-ner/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/roberta-fa-zwnj-base-ner/resolve/main/flax_model.msgpack
471 MB
- Xet hash:
- 20855eb4a15e2092f7751321ca1859375237406afe0a79a3d1c057fc7d9ec775
- Size of remote file:
- 471 MB
- SHA256:
- cf8e07f96ea99aae602b78b38fb46dbbdebfddfb754b1fa3d2c0ce91a8bdb514
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