Post
739
📢 The fast application of named entity recognition (NER) model towards vast amout of texts usually serves two major pitfalls:
🔴 Limitation of the input window size
🔴 Drastically slows down the downstream pipeline of the whole application
⭐ https://github.com/nicolay-r/bulk-ner
To address these problems, bulk-ner represent a no-string framework with the handy wrapping over any dynamically linked NER-ml model by providing:
☑️ Native long-input contexts handling.
☑️ Native support of batching (assuming that ML-model engine has the related support too)
To quick start, sharing the wrapper over DeepPavlov NER models.
With the application of such models you can play and bulk your data here:
📙 https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
(You have to have your data in CSV / JSONL format)
Lastly, it is powered by AREkit pipelines, and therefore could be a part of the relation extraction and complex information retrieval systems:
💻 https://github.com/nicolay-r/AREkit
📄 https://openreview.net/forum?id=nRybAsJMUt
🔴 Limitation of the input window size
🔴 Drastically slows down the downstream pipeline of the whole application
⭐ https://github.com/nicolay-r/bulk-ner
To address these problems, bulk-ner represent a no-string framework with the handy wrapping over any dynamically linked NER-ml model by providing:
☑️ Native long-input contexts handling.
☑️ Native support of batching (assuming that ML-model engine has the related support too)
To quick start, sharing the wrapper over DeepPavlov NER models.
With the application of such models you can play and bulk your data here:
📙 https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
(You have to have your data in CSV / JSONL format)
Lastly, it is powered by AREkit pipelines, and therefore could be a part of the relation extraction and complex information retrieval systems:
💻 https://github.com/nicolay-r/AREkit
📄 https://openreview.net/forum?id=nRybAsJMUt