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token-classification mask_token: [MASK]
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mys/electra-base-turkish-cased-ner mys/electra-base-turkish-cased-ner
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Contributed by

mys M. Yusuf Sarıgöz
1 model

How to use this model directly from the 🤗/transformers library:

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from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mys/electra-base-turkish-cased-ner") model = AutoModelForTokenClassification.from_pretrained("mys/electra-base-turkish-cased-ner")

What is this

A NER model for Turkish with 48 categories trained on the dataset Shrinked TWNERTC Turkish NER Data by Behçet Şentürk, which is itself a filtered and cleaned version of the following automatically labeled dataset:

Sahin, H. Bahadir; Eren, Mustafa Tolga; Tirkaz, Caglar; Sonmez, Ozan; Yildiz, Eray (2017), “English/Turkish Wikipedia Named-Entity Recognition and Text Categorization Dataset”, Mendeley Data, v1

Backbone model

The backbone model is electra-base-turkish-cased-discriminator, and I finetuned it for token classification.

I'm continuing to figure out if it is possible to improve accuracy with this dataset, but it is already usable for non-critic applications. You can reach out to me on Twitter for discussions and issues. I will also release a notebook to finetune NER models with Shrinked TWNERTC as well as sample inference code to demonstrate what's possible with this model.