multilingual-bert-cardinality-classifier

bert-base-multilingual-cased-single-multiple-place-classification

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This model is designed to classify geographic encyclopedia articles describing places. It is a fine-tuned version of the bert-base-multilingual-cased model. It has been trained on a manually annotated subset of the French Encyclopédie ou dictionnaire raisonné des sciences des arts et des métiers par une société de gens de lettres (1751-1772) edited by Diderot and d'Alembert (provided by the ARTFL Encyclopédie Project).

Model Description

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• Developed by: xxxxxxxxx
• Model type: Text classification
• Repository: xxxxxxxxx
• Language(s) (NLP): French
• License: cc-by-nc-4.0

Class labels

The tagset is as follows:

• Single: only one place is described
• Multiple: several places are described (a single name with multiple locations)

Dataset

The model was trained using a set of 8658 entries classified as 'Place' (using this model: https://huggingface.co/no-name-research/multilingual-bert-entry-type-classifier) among entries classified as 'Geography'. The datasets have the following distribution of entries among datasets and classes:

Evaluation

• Overall macro-average model performances

• Overall weighted-average model performances

• Model performances (Test set)

How to Get Started with the Model

Use the code below to get started with the model.

import torch
from transformers import pipeline
device = torch.device("mps" if torch.backends.mps.is_available() else ("cuda" if torch.cuda.is_available() else "cpu"))

pipe = pipeline("text-classification", model="no-name-research/multilingual-bert-cardinality-classifier", truncation=True, device=device)

samples = [
    "* ALBI, (Géog.) ville de France, capitale de l'Albigeois, dans le haut Languedoc : elle est sur le Tarn. Long. 19. 49. lat. 43. 55. 44.",
    "PEGOE, (Géog. anc.) 1°. ville de l'Achaie, dans la Mégaride ; 2°. ville de l'Hellespont, selon Ortelius ; 3°. ville de l'île de Cypre ou de la Cyrénie, selon Etienne le géographe. "
]


for sample in samples:
    print(pipe(sample))

Bias, Risks, and Limitations

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This model was trained entirely on French encyclopaedic entries classified as Geography (and place) and will likely not perform well on text in other languages or other corpora.