Davlan
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distilbert-base-multilingual-cased-ner-hrl

Token Classification

Inference Endpoints

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Davlan commited on Aug 24, 2021

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@@ -36,16 +36,18 @@ This model is limited by its training dataset of entity-annotated news articles
 ## Training data
 The training data for the 10 languages are from:
-Arabic: [ANERcorp](https://github.com/EmnamoR/Arabic-named-entity-recognition)
-German: [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
-English: [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
-Spanish: [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
-French: [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
-Italian: []()
-Latvian: [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
-Dutch: [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
-Portuguese: [Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
-Chinese: [MSRA](https://huggingface.co/datasets/msra_ner)
 The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
 Abbreviation|Description

 ## Training data
 The training data for the 10 languages are from:
+Language|Dataset
+-|-
+Arabic | [ANERcorp](https://github.com/EmnamoR/Arabic-named-entity-recognition)
+German | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
+English | [conll 2003](https://www.clips.uantwerpen.be/conll2003/ner/)
+Spanish | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
+French | [Europeana Newspapers](https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio)
+Italian | [Italian I-CAB](https://ontotext.fbk.eu/icab.html)
+Latvian | [Latvian NER](https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities)
+Dutch | [conll 2002](https://www.clips.uantwerpen.be/conll2002/ner/)
+Portuguese |[Paramopama + Second Harem](https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese)
+Chinese | [MSRA](https://huggingface.co/datasets/msra_ner)
 The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:
 Abbreviation|Description