Token Classification
Transformers
PyTorch
xlm-roberta
text
named entity recognition
roberta
historical languages
precision
recall
Eval Results (legacy)
Instructions to use magistermilitum/roberta-multilingual-medieval-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magistermilitum/roberta-multilingual-medieval-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="magistermilitum/roberta-multilingual-medieval-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("magistermilitum/roberta-multilingual-medieval-ner") model = AutoModelForTokenClassification.from_pretrained("magistermilitum/roberta-multilingual-medieval-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Model Details
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This is a Fine-tuned version of the multilingual Roberta model on medieval charters. The model is intended to recognize Locations and persons in medieval texts
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in a Flat and nested manner. The train dataset entails 8k annotated texts on medieval latin, french and Spanish from a period ranging from 11th to 15th centuries.
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### How to Get Started with the Model
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## Model Details
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This is a Fine-tuned version of the multilingual Roberta model on medieval charters. The model is intended to recognize Locations and persons in medieval texts
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in a Flat and nested manner. The train dataset entails 8k annotated texts on medieval latin, Old french and Old Spanish from a period ranging from 11th to 15th centuries.
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### How to Get Started with the Model
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