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

👀
John6666