Instructions to use HooshvareLab/bert-base-parsbert-ner-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-base-parsbert-ner-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/bert-base-parsbert-ner-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-ner-uncased") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/bert-base-parsbert-ner-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/bert-base-parsbert-ner-uncased: direct link, hf CLI and curl.
- Browser
- Download file 649 MB
-
https://huggingface.co/HooshvareLab/bert-base-parsbert-ner-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/bert-base-parsbert-ner-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/bert-base-parsbert-ner-uncased/resolve/main/flax_model.msgpack
649 MB
- Xet hash:
- 3775a9307af42943eff11b968bef337638b46e33727adae599419eb097c171ff
- Size of remote file:
- 649 MB
- SHA256:
- bf0f5a2aedac66c2890619003e6f83c7e967641080d071bfe2c92ee048d1d26b
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