Update README.md

README.md

@@ -1,9 +1,99 @@
-👷🏻 Work in progress 
+---
+license: eupl-1.1
+datasets:
+- EuropeanParliament/cellar_eurovoc
+language:
+- en
+metrics:
+  - type: f1         
+    value: 0.XX 
+    name: micro F1
+    args:
+      threshold: 0.XX
+  - type: NDCG@3         
+    value: 0.X  
+    name: NDCG@5
+  - type: NDCG@5         
+    value: 0.XX 
+    name: NDCG@5
+  - type: NDCG@10         
+    value: 0.XX  
+    name: NDCG@10
+tags:
+- eurovoc
+pipeline_tag: text-classification
 
-NDCG@3: 0.7127
+widget:
+- 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."
+ 
+---
 
-NDCG@5: 0.6549
+# Eurovoc Multilabel Classifer
 
-NDCG@10: 0.6382
+[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.
+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.
 
-Micro F1 Score: 0.31 (0.5295)
+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.
+This model support the 24 languages of the European Union.
+
+
+## Architecture
+
+![architecture](img/architecture.png)
+
+7331 Eurovoc labels
+
+## Usage 
+
+```python
+from eurovoc import EurovocTagger
+model = EurovocTagger.from_pretrained("EuropeanParliament/eurovoc_eu")
+```
+
+## Metrics
+
+
+### Eurlex57k Dataset
+
+| Metric     | Value    | Threshold Value |
+|------------|----------|-----------------|
+| Micro F1   | 0.XX     | 0.XX            |
+| NDCG@3     | 0.XX     | -               |
+| NDCG@5     | 0.XX     | -               |
+| NDCG@10    | 0.XX     | -               |
+
+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).
+
+
+## Inference Endpoint
+
+### Payload example 
+
+```json 
+{
+  "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. ",
+  "topk": 10,
+  "threshold": 0.16
+}
+
+```
+
+result: 
+
+```json 
+{'results': [{'label': 'international sanctions', 'score': 0.9994925260543823},
+             {'label': 'economic sanctions', 'score': 0.9991770386695862},
+             {'label': 'natural person', 'score': 0.9591936469078064},
+             {'label': 'EU restrictive measure', 'score': 0.8388392329216003},
+             {'label': 'legal person', 'score': 0.45630475878715515},
+             {'label': 'Burma/Myanmar', 'score': 0.43375277519226074}]}
+```
+
+Only six results, because the following one score is less that 0.16
+
+Default value, topk = 5 and threshold = 0.16
+
+
+## Author(s)
+
+Sébastien Campion <sebastien.campion@europarl.europa.eu>