{
 "p1": {
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  "pct": 94.0,
  "missed_examples": [
   "In March 2016, two relevant informational Internet RFCs were published:\n\nRFC 7763 – \"The text/markdown Media Type,\" Informational.",
   "RFC 7764 – \"Guidance on Markdown: Design Philosophies, Stability Strategies, and Select Registrations,\" Informational.",
   "Comparison of document markup languages\nComparison of documentation generators\nComparison of wiki software\nLightweight markup language\nList of markup languages\nList of text editors\nWiki markup\n\n\n\n\n\n\nWilburn, Gene (12 January 2022)."
  ],
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  "boilerplate_markers": {
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  },
  "chars": 47491,
  "tokens": 12750
 },
 "p1_nocache": {
  "sentence_recall": "47/50",
  "pct": 94.0,
  "missed_examples": [
   "In March 2016, two relevant informational Internet RFCs were published:\n\nRFC 7763 – \"The text/markdown Media Type,\" Informational.",
   "RFC 7764 – \"Guidance on Markdown: Design Philosophies, Stability Strategies, and Select Registrations,\" Informational.",
   "Comparison of document markup languages\nComparison of documentation generators\nComparison of wiki software\nLightweight markup language\nList of markup languages\nList of text editors\nWiki markup\n\n\n\n\n\n\nWilburn, Gene (12 January 2022)."
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  "boilerplate_markers": {
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  },
  "chars": 46935,
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 },
 "p1_gt_images": {
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 },
 "p2": {
  "sentence_recall": "205/212",
  "pct": 96.7,
  "missed_examples": [
   "json — JSON encoder and decoder ¶ Source code: Lib/json/__init__.py JSON (JavaScript Object Notation) , specified by RFC 7159 (which obsoletes RFC 4627 ) and by ECMA-404 , is a lightweight data interchange format inspired by JavaScript object literal syntax (although it is not a strict subset of JavaScript [ 1 ] ).",
   "dumps ( \" \\\" foo \\b ar\" )) \"\\\"foo\\bar\" >>> print ( json .",
   "dumps ( 1 + 2 j , default = custom_json ) '{\"__complex__\": true, \"real\": 1.0, \"imag\": 2.0}' Decoding JSON: >>> import json >>> json .",
   "dumps ( 2 + 1 j , cls = ComplexEncoder ) '[2.0, 1.0]' >>> ComplexEncoder () .",
   "encode ( 2 + 1 j ) '[2.0, 1.0]' >>> list ( ComplexEncoder () ."
  ],
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 },
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 },
 "p3_gt_authors": [
  "Albert Einstein",
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  "Albert Einstein",
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  "Albert Einstein",
  "André Gide",
  "Thomas A. Edison",
  "Eleanor Roosevelt",
  "Steve Martin"
 ],
 "p4": {
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  "abstract_sentence_recall_after_removing_pdf_hyphens": "6/8",
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  "missed": [
   "Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information.",
   "While these models are essential for tasks like information retrieval, semantic clustering, and text re-ranking, most existing open-source models, especially those built on architectures like BERT, struggle to represent lengthy documents and often resort to truncation.",
   "One common approach to mitigate this challenge involves splitting documents into smaller paragraphs for embedding.",
   "However, this strategy results in a much larger set of vectors, consequently leading to increased memory consumption and computationally intensive vector searches with elevated latency.",
   "To address these challenges, we introduce Jina Embeddings 2, an open-source text embedding model capable of accommodating up to 8192 tokens.",
   "This model is designed to transcend the conventional 512-token limit and adeptly process long documents.",
   "Jina Embeddings 2 not only achieves state-of-the-art performance on a range of embedding-related tasks in the MTEB benchmark but also matches the performance of OpenAI's proprietary ada-002 model.",
   "Additionally, our experiments indicate that an extended context can enhance performance in tasks such as NarrativeQA."
  ],
  "numPages": 14,
  "title": "Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents",
  "chars": 47126,
  "tokens": 12214
 }
}