bert-base-multilingual-cased-geography-entry-classification
This model is designed to classify geographic encyclopedia articles into Place, Person, or Other. It is a fine-tuned version of the bert-base-multilingual-cased model. It has been trained on 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
- 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:
- Place: encyclopedia entry describing the name of a place (such as a city, a river, a country, etc.)
- Person: encyclopedia entry describing the name of a people or community
- Other: encyclopedia entry describing any other type of entity (such as abstract geographic concepts, cross-references to other entries, etc.)
Dataset
The model was trained using the no-name-dataset dataset. The dataset is splitted into train, validation and test sets which have the following distribution of entries among classes:
| Train | Validation | Test | |
|---|---|---|---|
| Place | 1,800 | 225 | 225 |
| Person | 200 | 25 | 25 |
| Misc | 200 | 25 | 25 |
Evaluation
- Overall weighted-average model performances
| Precision | Recall | F-score | |
|---|---|---|---|
| 0.980 | 0.978 | 0.979 |
- Model performances (Test set)
| Precision | Recall | F-score | Support | |
|---|---|---|---|---|
| Place | 0.99 | 0.98 | 0.99 | 225 |
| Person | 1.00 | 0.96 | 0.98 | 25 |
| Other | 0.83 | 0.96 | 0.89 | 25 |
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-entry-type-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.",
"MAEATAE, (Géogr. anc.) anciens peuples de l'île de la grande Bretagne ; ils étoient auprès du mur qui coupoit l'île en deux parties. Cambden ne doute point que ce soit le Nortumberland.",
"APPONDURE, s. f. terme de riviere ; mot dont on se sert dans la composition d'un train ; c'est une portion de perche employée pour fortifier le chantier lorsqu'il est trop menu."
]
for sample in samples:
print(pipe(sample))
# Output
[{'label': 'Place', 'score': 0.9984742999076843}]
[{'label': 'Person', 'score': 0.9927592277526855}]
[{'label': 'Other', 'score': 0.9885557293891907}]
Bias, Risks, and Limitations
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.
- Downloads last month
- 4
Model tree for no-name-research/multilingual-bert-entry-type-classifier
Base model
google-bert/bert-base-multilingual-cased