multilingual-bert-place-ner-classifier

bert-base-multilingual-cased-classification-ner

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This model is designed to classify place named entities recognized from geographic encyclopedia articles. It is a fine-tuned version of the bert-base-multilingual-cased model. It has been trained on no-name-research/no-name-dataset, 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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• Authors: xxxxxxxxxx
• Model type: Text classification
• Repository: xxxxxxxxxx
• Language(s) (NLP): French
• License: cc-by-nc-4.0

Class labels

The tagset is as follows:

• City:
• Country:
• Human-made:
• Island:
• Lake:
• Mountain:
• Other:
• Region:
• River:
• Sea:

Dataset

The model was trained using the no-name-research/no-name-dataset dataset. The dataset is splitted into train, validation and test sets which have the following distribution of entries among classes:

Evaluation

• 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"))

ner = pipeline("token-classification", model="no-name-research/camembert-token-classification", aggregation_strategy="simple", device=device)
placename_classifier = pipeline("text-classification", model="no-name-research/multilingual-bert-place-ner-classifier", truncation=True, device=device)

def get_context(text, span, ngram_context_size=5):
    word = span["word"]
    start = span["start"]
    end = span["end"]
    label = span["entity_group"]

    # Extract context
    previous_text = text[:start].strip()
    next_text = text[end:].strip()
    previous_words = previous_text.split()[-ngram_context_size:]
    next_words = next_text.split()[:ngram_context_size]

    # Build context string
    context = f"[{word}]: {' '.join(previous_words)} {word} {' '.join(next_words)}"
    return word, context, label

content = "WINCHESTER, (Géog. mod.) ou plutôt Wintchester, ville d'Angleterre, capitale du Hampshire, sur le bord de l'Itching, à dix-huit milles au sud-est de Salisbury, & à soixante sud-ouest de Londres. Long. 16. 20. latit. 51. 3."

spans = ner(content)
for span in spans:
    if span['entity_group'] == 'NP_Spatial':
        word, context, label = get_context(content, span, ngram_context_size=5)
        print(f"Place name: {word}")

        label = placename_classifier(context)
        print(f"Predicted label: {label}")


# Output
Place name: Wintchester
Predicted label: [{'label': 'City', 'score': 0.9968810081481934}]
Place name: Angleterre
Predicted label: [{'label': 'Country', 'score': 0.9953059554100037}]
Place name: Hampshire
Predicted label: [{'label': 'Region', 'score': 0.9967537522315979}]
Place name: Itching
Predicted label: [{'label': 'River', 'score': 0.9929990768432617}]
Place name: Salisbury
Predicted label: [{'label': 'City', 'score': 0.9969013929367065}]
Place name: Londres
Predicted label: [{'label': 'City', 'score': 0.9969471096992493}]

Bias, Risks, and Limitations

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