Instructions to use dswah/address-ner-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dswah/address-ner-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dswah/address-ner-de")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dswah/address-ner-de") model = AutoModelForTokenClassification.from_pretrained("dswah/address-ner-de") - Notebooks
- Google Colab
- Kaggle
Upload README.md
Browse files
README.md
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---
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license: apache-2.0
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datasets:
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- oscar-corpus/OSCAR-2301
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language:
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- de
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metrics:
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- f1
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library_name: transformers
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pipeline_tag: token-classification
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---
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# address-ner-de
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## Address NER for Legal German Text
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See synthetic dataset creation at [github.com/dswah/address-ner-de](github.com/dswah/address-ner-de)
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