content.raw: 📢 Deligted to share the most recent milestone on quick deployment of Named Entity Recognition (NER) in Gen-AI powered systems. 
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content.value: 📢 Deligted to share the most recent milestone on quick deployment of Named Entity Recognition (NER) in Gen-AI powered systems. 
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content.raw: Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.
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content.value: Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.
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content.raw: 💎 Why is this important? In the era of GenAI the handling out textual  output might be challenging.  Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.
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content.value: 💎 Why is this important? In the era of GenAI the handling out textual  output might be challenging.  Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.
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content.raw: 📦: 
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content.value: 📦: 
content.href: https://pypi.org/project/bulk-ner/0.25.0/
content.raw: https://pypi.org/project/bulk-ner/0.25.0/
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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: I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
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content.value: I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
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content.raw: In particular:
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content.value: In particular:
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content.raw: 1. Processing CONLL format with B-I-O tags from model outputs
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content.value: 1. Processing CONLL format with B-I-O tags from model outputs
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content.raw: 2. Input trimming: long input content might not be completely fitted
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content.value: 2. Input trimming: long input content might not be completely fitted
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content.raw: To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
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content.value: To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
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content.raw: ✅ 🐍 Python API support: see screenshot below for a quick deployment (see screenshot below 📸)
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content.value: ✅ 🐍 Python API support: see screenshot below for a quick deployment (see screenshot below 📸)
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content.raw: ✅ 🪶 No-string: dependencies are now clear, so it is purely Python implementation for API calls.
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content.value: ✅ 🪶 No-string: dependencies are now clear, so it is purely Python implementation for API calls.
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content.raw: ✅ 👌 Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below 📸)
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content.value: ✅ 👌 Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below 📸)
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content.raw: 📒 We have a colab for a quick start here (or screenshot for bash / Python API 📸)
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content.value: 📒 We have a colab for a quick start here (or screenshot for bash / Python API 📸)
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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: 👏 The code for pipeline deployment is taken from the AREkit project:
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content.value: 👏 The code for pipeline deployment is taken from the AREkit project:
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content.href: https://github.com/nicolay-r/AREkit
content.raw: https://github.com/nicolay-r/AREkit
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rawContent: 📢 Deligted to share the most recent milestone on quick deployment of Named Entity Recognition (NER) in Gen-AI powered systems. 
  
  Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.
  
  💎 Why is this important? In the era of GenAI the handling out textual  output might be challenging.  Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.
  
  📦: https://pypi.org/project/bulk-ner/0.25.0/
  🌟: https://github.com/nicolay-r/bulk-ner
  
  I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
  In particular:
  1. Processing CONLL format with B-I-O tags from model outputs
  2. Input trimming: long input content might not be completely fitted
  
  To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
  ✅ 🐍 Python API support: see screenshot below for a quick deployment (see screenshot below 📸)
  ✅ 🪶 No-string: dependencies are now clear, so it is purely Python implementation for API calls.
  ✅ 👌 Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below 📸)
  
  📒 We have a colab for a quick start here (or screenshot for bash / Python API 📸)
  https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
  
  👏 The code for pipeline deployment is taken from the AREkit project:
  https://github.com/nicolay-r/AREkit
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