content.raw: 📢 The fast application of named entity recognition (NER) model towards vast amout of texts usually serves two major pitfalls:
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content.value: 📢 The fast application of named entity recognition (NER) model towards vast amout of texts usually serves two major pitfalls:
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content.raw: 🔴 Limitation of the input window size
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content.value: 🔴 Limitation of the input window size
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content.raw: 🔴 Drastically slows down the downstream pipeline of the whole application
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content.value: 🔴 Drastically slows down the downstream pipeline of the whole application
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content.raw: ⭐ 
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content.value: ⭐ 
content.href: https://github.com/nicolay-r/bulk-ner
content.raw: https://github.com/nicolay-r/bulk-ner
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content.raw: To address these problems, bulk-ner represent a no-string framework with the handy wrapping over any dynamically linked NER-ml model by providing:
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content.value: To address these problems, bulk-ner represent a no-string framework with the handy wrapping over any dynamically linked NER-ml model by providing:
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content.raw: ☑️ Native long-input contexts handling.
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content.value: ☑️ Native long-input contexts handling.
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content.raw: ☑️ Native support of batching (assuming that ML-model engine has the related support too)
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content.value: ☑️ Native support of batching (assuming that ML-model engine has the related support too)
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content.raw: To quick start, sharing the wrapper over DeepPavlov NER models.
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content.value: To quick start, sharing the wrapper over DeepPavlov NER models.
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content.raw: With the application of such models you can play and bulk your data here:
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content.value: With the application of such models you can play and bulk your data here:
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content.raw: 📙 
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content.value: 📙 
content.href: https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
content.raw: https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
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content.raw: (You have to have your data in CSV / JSONL format)
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content.value: (You have to have your data in CSV / JSONL format)
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content.raw: Lastly, it is powered by AREkit pipelines, and therefore could be a part of the relation extraction and complex information retrieval systems:
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content.value: Lastly, it is powered by AREkit pipelines, and therefore could be a part of the relation extraction and complex information retrieval systems:
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content.raw: 💻 
content.type: text
content.value: 💻 
content.href: https://github.com/nicolay-r/AREkit
content.raw: https://github.com/nicolay-r/AREkit
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content.raw: 📄 
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content.value: 📄 
content.href: https://openreview.net/forum?id=nRybAsJMUt
content.raw: https://openreview.net/forum?id=nRybAsJMUt
content.type: link
content.type: new_line
rawContent: 📢 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
  
reactions.reaction: 👀
reactions.users: John6666