Instructions to use Andrija/M-bert-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andrija/M-bert-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Andrija/M-bert-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Andrija/M-bert-NER") model = AutoModelForTokenClassification.from_pretrained("Andrija/M-bert-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Andrija/M-bert-NER: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/Andrija/M-bert-NER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Andrija/M-bert-NER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Andrija/M-bert-NER/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- dc6c51d3b862af7ce0e88d34c88d2e6c78c35460e2c6a26ee9a03bea0a15b6ee
- Size of remote file:
- 709 MB
- SHA256:
- 191316ae56206b20a5eceb5451844fab6f467b62ed6b40ba0cb72667aaa0bdec
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