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 training_args.bin from Davlan/bert-base-multilingual-cased-ner-hrl: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl/resolve/main/training_args.bin
- Command line
-
hf download hf://Davlan/bert-base-multilingual-cased-ner-hrl/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl/resolve/main/training_args.bin
1.52 kB
- Xet hash:
- 80a0af2353563b954044bed8229b7e9d654702136effe67781044f6ad09791a5
- Size of remote file:
- 1.52 kB
- SHA256:
- 22d7b45befcaae3b668f7a2bc0a9e2d77c4e5a9f7d09e99db695b6cb6edcca81
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