Instructions to use HooshvareLab/bert-base-parsbert-armanner-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-base-parsbert-armanner-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/bert-base-parsbert-armanner-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-armanner-uncased") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/bert-base-parsbert-armanner-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HooshvareLab/bert-base-parsbert-armanner-uncased: direct link, hf CLI and curl.
- Browser
- Download file 649 MB
-
https://huggingface.co/HooshvareLab/bert-base-parsbert-armanner-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HooshvareLab/bert-base-parsbert-armanner-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HooshvareLab/bert-base-parsbert-armanner-uncased/resolve/main/flax_model.msgpack
649 MB
- Xet hash:
- 734aed1ef0ab302e718a522154cb78b3005bfb2efc0d4a14761dc47f1deda696
- Size of remote file:
- 649 MB
- SHA256:
- 685102cc1dc9fa215745d291cbd16b8d6ec4259665d1efc5e771709ba20587ae
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