---
license: cc-by-nc-4.0
language:
- fr
base_model:
- google-bert/bert-base-multilingual-cased
pipeline_tag: text-classification
widget:
- text: >-
    * 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.
datasets:
- GEODE/GeoEDdA-TopoRel
---



# bert-base-multilingual-cased-place-entry-classification


<!-- Provide a quick summary of what the model is/does. -->

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 [GeoEDdA-TopoRel](https://huggingface.co/datasets/GEODE/GeoEDdA-TopoRel), 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](https://artfl-project.uchicago.edu)).




## Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Bin Yang, [Ludovic Moncla](https://ludovicmoncla.github.io), [Fabien Duchateau](https://perso.liris.cnrs.fr/fabien.duchateau/) and [Frédérique Laforest](https://perso.liris.cnrs.fr/flaforest/)
- **Model type:** Text classification
- **Repository:** [https://gitlab.liris.cnrs.fr/ecoda/encyclopedia2geokg](https://gitlab.liris.cnrs.fr/ecoda/encyclopedia2geokg)
- **Language(s) (NLP):** French
- **License:** cc-by-nc-4.0


## Class labels


The tagset is as follows (with examples from the dataset):
- **City**: villes, bourgs, villages, etc.
- **Island**: îles, presqu'îles, etc.
- **Region**: régions, contrées, provinces, cercles, etc.
- **River**: rivières, fleuves,etc.
- **Mountain**: montagnes, vallées, etc.
- **Country**: pays, royaumes, etc.
- **Sea**: mer, golphe, baie, etc.
- **Other**: promontoires, caps, rivages, déserts, etc.
- **Human-made**: ports, châteaux, forteresses, abbayes, etc.
- **Lake**: lacs, étangs, marais, etc.


## Dataset

The model was trained using the [GeoEDdA-TopoRel](https://huggingface.co/datasets/GEODE/GeoEDdA-TopoRel) dataset.
The dataset is splitted into train, validation and test sets which have the following distribution of entries among classes: 

|   | Train | Validation | Test|
|---|:---:|:---:|:---:|
| City                  | 921 | 33 | 40 | 
| Island                | 216 | 20 | 27 |
| Region                | 138 | 40 |  28 |
| River                 | 133 | 20 |  28 |
| Mountain              | 63 | 29 |  22 |
| Human-made            | 38 | 10 |  9 |
| Other                 | 27 | 12 |  12 |
| Sea                   | 26 | 13 |  12 |
| Lake                  | 22 | 9 |  9 |
| Country               | 16 | 14 |  13 |



## Evaluation


* Overall macro-average model performances

 | Precision | Recall | F-score |
|:---:|:---:|:---:|
|0.95  |    0.92  |    0.93  | 


* Overall weighted-average model performances

| Precision | Recall | F-score |
|:---:|:---:|:---:|
|0.94  |    0.94  |    0.94  | 


* Model performances (Test set)

|   | Precision | Recall | F-score | Support |
|---|:---:|:---:|:---:|:---:|
|              City   |    0.91   |   1.00   |   0.95   |     40|
|            Island   |    0.96   |   0.96   |   0.96   |     27|
|             River   |    0.97   |   1.00   |   0.98   |     28|
|            Region   |    0.86   |   0.89   |   0.88   |     28|
|          Mountain   |    1.00   |   0.95   |   0.98   |     22|
|           Country   |    1.00   |   0.85   |   0.92   |     13|
|               Sea   |    1.00   |   0.92   |   0.96   |     12|
|             Other   |    0.90   |   0.75   |   0.82   |     12|
|        Human-made   |    0.90   |   1.00   |   0.95   |      9|
|              Lake   |    1.00   |   0.89   |   0.94   |      9|






## How to Get Started with the Model

Use the code below to get started with the model.


```python
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
device = torch.device("mps" if torch.backends.mps.is_available() else ("cuda" if torch.cuda.is_available() else "cpu"))

tokenizer = AutoTokenizer.from_pretrained("GEODE/bert-base-multilingual-cased-place-entry-classification")
model = AutoModelForSequenceClassification.from_pretrained("GEODE/bert-base-multilingual-cased-place-entry-classification")

pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, 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.",
    "* ARCALU (Principauté d') petit état des Tartares-Monguls, sur la riviere d'Hoamko, où commence  la grande muraille de la Chine, sous le 122e degré de longitude & le 42e de latitude septentrionale."
]


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


# Output

[{'label': 'City', 'score': 0.9969543218612671}]
[{'label': 'Region', 'score': 0.9811353087425232}]
```


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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. 



## Acknowledgement

The authors are grateful to the [ASLAN project](https://aslan.universite-lyon.fr) (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR).
Data courtesy the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu), University of Chicago.