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--- |
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license: eupl-1.1 |
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datasets: |
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- EuropeanParliament/cellar_eurovoc |
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language: |
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- en |
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metrics: |
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- type: f1 |
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value: 0.XX |
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name: micro F1 |
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args: |
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threshold: 0.XX |
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- type: NDCG@3 |
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value: 0.X |
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name: NDCG@5 |
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- type: NDCG@5 |
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value: 0.XX |
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name: NDCG@5 |
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- type: NDCG@10 |
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value: 0.XX |
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name: NDCG@10 |
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tags: |
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- eurovoc |
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pipeline_tag: text-classification |
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widget: |
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- text: "The Union condemns the continuing grave human rights violations by the Myanmar armed forces, including torture, sexual and gender-based violence, the persecution of civil society actors, human rights defenders and journalists, and attacks on the civilian population, including ethnic and religious minorities." |
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--- |
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# Eurovoc Multilabel Classifer |
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[EuroVoc](https://op.europa.eu/fr/web/eu-vocabularies) is a large multidisciplinary multilingual hierarchical thesaurus of more than 7000 classes covering the activities of EU institutions. |
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Given the number of legal documents produced every day and the huge mass of pre-existing documents to be classified high quality automated or semi-automated classification methods are most welcome in this domain. |
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This model based on BERT Deep Neural Network was trained on more than 3, 200,000 documents to achieve that task and is used in a production environment via the huggingface inference endpoint. |
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This model support the 24 languages of the European Union. |
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## Architecture |
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This classification model is build on top of [EUBERT](https://huggingface.co/EuropeanParliament/EUBERT) with 7331 Eurovoc labels |
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## Usage |
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```python |
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from eurovoc import EurovocTagger |
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model = EurovocTagger.from_pretrained("EuropeanParliament/eurovoc_eu") |
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``` |
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## Metrics |
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### Eurlex57k Dataset |
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| Metric | Value | Threshold Value | |
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|------------|----------|-----------------| |
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| Micro F1 | 0.XX | 0.XX | |
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| NDCG@3 | 0.XX | - | |
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| NDCG@5 | 0.XX | - | |
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| NDCG@10 | 0.XX | - | |
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These values are in line with the state of the art in the field, see the publication [Large Scale Legal Text Classification Using Transformer Models](https://arxiv.org/pdf/2010.12871.pdf). |
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## Inference Endpoint |
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### Payload example |
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```json |
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{ |
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"inputs": "The Union condemns the continuing grave human rights violations by the Myanmar armed forces, including torture, sexual and gender-based violence, the persecution of civil society actors, human rights defenders and journalists, and attacks on the civilian population, including ethnic and religious minorities. ", |
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"topk": 10, |
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"threshold": 0.16 |
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} |
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``` |
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result: |
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```json |
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{'results': [{'label': 'international sanctions', 'score': 0.9994925260543823}, |
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{'label': 'economic sanctions', 'score': 0.9991770386695862}, |
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{'label': 'natural person', 'score': 0.9591936469078064}, |
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{'label': 'EU restrictive measure', 'score': 0.8388392329216003}, |
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{'label': 'legal person', 'score': 0.45630475878715515}, |
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{'label': 'Burma/Myanmar', 'score': 0.43375277519226074}]} |
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``` |
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Only six results, because the following one score is less that 0.16 |
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Default value, topk = 5 and threshold = 0.16 |
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## Author(s) |
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Sébastien Campion <sebastien.campion@europarl.europa.eu> |
