Instructions to use Davlan/bert-base-multilingual-cased-ner-hrl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Davlan/bert-base-multilingual-cased-ner-hrl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Davlan/bert-base-multilingual-cased-ner-hrl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl") model = AutoModelForTokenClassification.from_pretrained("Davlan/bert-base-multilingual-cased-ner-hrl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Davlan/bert-base-multilingual-cased-ner-hrl: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Davlan/bert-base-multilingual-cased-ner-hrl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- 719cd6f848ba3ac101e6c4e4385426bbc6f8d116c8d347c46033d84727549108
- Size of remote file:
- 709 MB
- SHA256:
- 8c707863b713df859962ba50dcd834ab1b5bd459e7cc184e3aab62f2d34fc764
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