no-name-research/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.
