Instructions to use Azma-AI/bert-base-named-entity-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azma-AI/bert-base-named-entity-extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Azma-AI/bert-base-named-entity-extractor")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Azma-AI/bert-base-named-entity-extractor") model = AutoModelForTokenClassification.from_pretrained("Azma-AI/bert-base-named-entity-extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Azma-AI/bert-base-named-entity-extractor: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/Azma-AI/bert-base-named-entity-extractor/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Azma-AI/bert-base-named-entity-extractor/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Azma-AI/bert-base-named-entity-extractor/resolve/main/flax_model.msgpack
431 MB
- Xet hash:
- 2964ccb88b1124ba97deea72180c413c68144139ca99ef739f27a75c73214321
- Size of remote file:
- 431 MB
- SHA256:
- a124466eab9adb43377d35d32afe77313fceeb16b74b106f3742884c666a2c1e
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