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Named Entity Recognition with Bert – Depends on the definition

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Original source (www.depends-on-the-definition.com)
Tags: nlp artificial-intelligence bert ner named-entity-recognition www.depends-on-the-definition.com
Clipped on: 2020-02-05

Depends on the definition

it's about machine learning, data science and more

Tobias Sterbak

Data Scientist | Machine Learning Engineer | Natural Language Processing


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  1. Image (Asset 2/16) alt= you are right, maybe this is not the best possible result on this data set. I update it in the article. But please note, that this is not the Conll03 NER data set, where the state of the art is around 92%. You can try to tune the model to achieve better performance. But for me it was better than everything else on this data set. Let me know your experiences.

  • Image (Asset 3/16) alt= Thanks for sharing such an state of the art implementation with us.
    I really like the way you have explained all the stuffs in all of the posts.
    Actually i have collected lot of legal data ,so i was thinking to finetune
    bert encoder part with free text and then do the ner part for legal data, but i only have 8gb gpu…so i want to know how much memory does your model took…..
    and please share more bert posts (‘-‘)
    once agains thanks for sharing.

    • Image (Asset 4/16) alt= I’m happy you like it!
      The model provided by Huggingface is already pretrained on free text and you can fine-tune it for your dataset with the code in the post. 8gb GPU memory should be enough. If you also want to pretrain it on your texts, you can check the documentation by huggingface. Maybe I’ll do a post about it soon.
      Let me know how it works for you!

  • Image (Asset 5/16) alt= Let me know your suggestions.

    • Image (Asset 6/16) alt= I don’t know exactly what you want to do with tweets. But to some extend it will work. You could also try to fine-tune the language model to your tweets first and then fine-tune the actual task.

  • Image (Asset 7/16) alt= Is it possible to save this model as .h5 standalone model?