{
  "meta": {
    "pretty_name": "Copious",
    "dataset_id": "extraordinarylab/copious",
    "paper_url": null,
    "tags": [
      "Knowledge",
      "NER"
    ],
    "metrics": [
      "precision",
      "recall",
      "f1_score",
      "accuracy"
    ],
    "few_shot_num": 5,
    "eval_split": "test",
    "train_split": "train",
    "subset_list": [
      "default"
    ],
    "description": "## Overview\n\nCopious corpus is a gold standard corpus for biodiversity entity recognition, consisting of 668 documents downloaded from the Biodiversity Heritage Library with over 26K sentences and more than 28K entities covering taxonomic and ecological information.\n\n## Task Description\n\n- **Task Type**: Biodiversity Named Entity Recognition (NER)\n- **Input**: Biodiversity literature text\n- **Output**: Identified biodiversity entity spans\n- **Domain**: Biodiversity, ecology, taxonomy\n\n## Key Features\n\n- 668 documents from Biodiversity Heritage Library\n- Over 26,000 sentences\n- More than 28,000 entity annotations\n- Five biodiversity-specific entity types\n- Gold standard for biodiversity NLP\n\n## Evaluation Notes\n\n- Default configuration uses **5-shot** evaluation\n- Metrics: Precision, Recall, F1-Score, Accuracy\n- Entity types: TAXON, GEOGRAPHICAL_LOCATION, HABITAT, PERSON, TEMPORAL_EXPRESSION",
    "prompt_template": "You are a named entity recognition system that identifies the following entity types:\n{entities}\n\nProcess the provided text and mark all named entities with XML-style tags.\n\nFor example:\n<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.\n\nAvailable entity tags: {entity_list}\n\nINSTRUCTIONS:\n1. Wrap your entire response in <response>...</response> tags.\n2. Inside these tags, include the original text with entity tags inserted.\n3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).\n4. Tag ALL entities you can identify using the exact tag names provided.\n5. Do not include explanations, just the tagged text.\n6. If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\n{text}\n",
    "system_prompt": "",
    "few_shot_prompt_template": "Here are some examples of named entity recognition:\n\n{fewshot}\n\nYou are a named entity recognition system that identifies the following entity types:\n{entities}\n\nProcess the provided text and mark all named entities with XML-style tags.\n\nFor example:\n<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.\n\nAvailable entity tags: {entity_list}\n\nINSTRUCTIONS:\n1. Wrap your entire response in <response>...</response> tags.\n2. Inside these tags, include the original text with entity tags inserted.\n3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).\n4. Tag ALL entities you can identify using the exact tag names provided.\n5. Do not include explanations, just the tagged text.\n6. If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\n{text}\n",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "llm"
  },
  "statistics": {
    "total_samples": 0,
    "subset_stats": [],
    "prompt_length": {
      "mean": 0,
      "min": 0,
      "max": 0,
      "std": null
    },
    "target_length_mean": null,
    "computed_at": "2026-01-28T14:13:21.451837"
  },
  "sample_example": {
    "data": {
      "input": [
        {
          "id": "a4f5bffd",
          "content": "Here are some examples of named entity recognition:\n\nInput:\nFAMILY SERRANIDAE — SCHULTZ 363 Genus PLECTROPOMUS Oken Plectropomus Oken, Isis, p. 1182] 1782 (on \"Les Plectropomes\" Cuvier, Rgne animal, vol. 2, p. 277, 1817). (Genotype, Bodianus  ... [TRUNCATED] ... 9276, Sp. Blancoanae 787, Phil. PL 126, Elmer 18216, 16818, 164-17, 9570, B. S. 12036, 13334, 32736, 14746 Ramos. India to southern China and Formosa through Malaya to Samoa. Local names: Banana (Sub); bacilli (C. Bis); balisibis (Iv) ; baud\n"
        }
      ],
      "target": "<response>Vo] l <taxon>GRAMINEAE</taxon> 53 23. <taxon>THYSANOLAENA Nees</taxon> <taxon>THYSANOLAENA MAXIMA (Roxb) O. Kuntze</taxon> Rev. Gen. PI. (1891) 794; Pilger in Perk. Frag. Fl. Philip. (1904) 141; Merr. in Philip. Journ. Si. 1 (1906)  ... [TRUNCATED] ... aphical_location>Malaya</geographical_location> to <geographical_location>Samoa.</geographical_location> Local names: <taxon>Banana</taxon> (Sub); <taxon>bacilli</taxon> (C. Bis); <taxon>balisibis</taxon> (Iv) ; <taxon>baud</taxon></response>",
      "id": 0,
      "group_id": 0,
      "metadata": {
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          "Vo]",
          "l",
          "GRAMINEAE",
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          "Local",
          "names:",
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          "(Sub);",
          "bacilli",
          "(C.",
          "Bis);",
          "balisibis",
          "(Iv)",
          ";",
          "baud"
        ],
        "ner_tags": [
          "O",
          "O",
          "B-TAXON",
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          "O",
          "B-TAXON",
          "I-TAXON",
          "B-TAXON",
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          "O",
          "O",
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          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
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          "O",
          "O",
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          "B-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
          "I-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
          "I-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
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          "B-GEOGRAPHICAL_LOCATION",
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          "B-GEOGRAPHICAL_LOCATION",
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          "B-GEOGRAPHICAL_LOCATION",
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          "B-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
          "B-GEOGRAPHICAL_LOCATION",
          "O",
          "B-HABITAT",
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          "I-HABITAT",
          "B-HABITAT",
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          "O",
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          "O",
          "O",
          "O",
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          "O",
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          "I-GEOGRAPHICAL_LOCATION",
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          "B-GEOGRAPHICAL_LOCATION",
          "I-GEOGRAPHICAL_LOCATION",
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          "B-GEOGRAPHICAL_LOCATION",
          "O",
          "B-GEOGRAPHICAL_LOCATION",
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          "O",
          "O",
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          "O",
          "O",
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          "O",
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          "B-HABITAT",
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          "O",
          "O",
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          "O",
          "O",
          "O",
          "O",
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          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
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          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "O",
          "B-GEOGRAPHICAL_LOCATION",
          "O",
          "B-GEOGRAPHICAL_LOCATION",
          "I-GEOGRAPHICAL_LOCATION",
          "O",
          "B-GEOGRAPHICAL_LOCATION",
          "O",
          "B-GEOGRAPHICAL_LOCATION",
          "O",
          "B-GEOGRAPHICAL_LOCATION",
          "O",
          "O",
          "B-TAXON",
          "O",
          "B-TAXON",
          "O",
          "O",
          "B-TAXON",
          "O",
          "O",
          "B-TAXON"
        ]
      }
    },
    "subset": "default",
    "truncated": true
  },
  "readme": {
    "en": "# Copious\n\n## Overview\n\nCopious corpus is a gold standard corpus for biodiversity entity recognition, consisting of 668 documents downloaded from the Biodiversity Heritage Library with over 26K sentences and more than 28K entities covering taxonomic and ecological information.\n\n## Task Description\n\n- **Task Type**: Biodiversity Named Entity Recognition (NER)\n- **Input**: Biodiversity literature text\n- **Output**: Identified biodiversity entity spans\n- **Domain**: Biodiversity, ecology, taxonomy\n\n## Key Features\n\n- 668 documents from Biodiversity Heritage Library\n- Over 26,000 sentences\n- More than 28,000 entity annotations\n- Five biodiversity-specific entity types\n- Gold standard for biodiversity NLP\n\n## Evaluation Notes\n\n- Default configuration uses **5-shot** evaluation\n- Metrics: Precision, Recall, F1-Score, Accuracy\n- Entity types: TAXON, GEOGRAPHICAL_LOCATION, HABITAT, PERSON, TEMPORAL_EXPRESSION\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `copious` |\n| **Dataset ID** | [extraordinarylab/copious](https://modelscope.cn/datasets/extraordinarylab/copious/summary) |\n| **Paper** | N/A |\n| **Tags** | `Knowledge`, `NER` |\n| **Metrics** | `precision`, `recall`, `f1_score`, `accuracy` |\n| **Default Shots** | 5-shot |\n| **Evaluation Split** | `test` |\n| **Train Split** | `train` |\n\n\n## Data Statistics\n\n*Statistics not available.*\n\n## Sample Example\n\n**Subset**: `default`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"a4f5bffd\",\n      \"content\": \"Here are some examples of named entity recognition:\\n\\nInput:\\nFAMILY SERRANIDAE — SCHULTZ 363 Genus PLECTROPOMUS Oken Plectropomus Oken, Isis, p. 1182] 1782 (on \\\"Les Plectropomes\\\" Cuvier, Rgne animal, vol. 2, p. 277, 1817). (Genotype, Bodianus  ... [TRUNCATED] ... 9276, Sp. Blancoanae 787, Phil. PL 126, Elmer 18216, 16818, 164-17, 9570, B. S. 12036, 13334, 32736, 14746 Ramos. India to southern China and Formosa through Malaya to Samoa. Local names: Banana (Sub); bacilli (C. Bis); balisibis (Iv) ; baud\\n\"\n    }\n  ],\n  \"target\": \"<response>Vo] l <taxon>GRAMINEAE</taxon> 53 23. <taxon>THYSANOLAENA Nees</taxon> <taxon>THYSANOLAENA MAXIMA (Roxb) O. Kuntze</taxon> Rev. Gen. PI. (1891) 794; Pilger in Perk. Frag. Fl. Philip. (1904) 141; Merr. in Philip. Journ. Si. 1 (1906)  ... [TRUNCATED] ... aphical_location>Malaya</geographical_location> to <geographical_location>Samoa.</geographical_location> Local names: <taxon>Banana</taxon> (Sub); <taxon>bacilli</taxon> (C. Bis); <taxon>balisibis</taxon> (Iv) ; <taxon>baud</taxon></response>\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"metadata\": {\n    \"tokens\": [\n      \"Vo]\",\n      \"l\",\n      \"GRAMINEAE\",\n      \"53\",\n      \"23.\",\n      \"THYSANOLAENA\",\n      \"Nees\",\n      \"THYSANOLAENA\",\n      \"MAXIMA\",\n      \"(Roxb)\",\n      \"O.\",\n      \"Kuntze\",\n      \"Rev.\",\n      \"Gen.\",\n      \"PI.\",\n      \"(1891)\",\n      \"794;\",\n      \"Pilger\",\n      \"in\",\n      \"Perk.\",\n      \"Frag.\",\n      \"Fl.\",\n      \"Philip.\",\n      \"(1904)\",\n      \"141;\",\n      \"Merr.\",\n      \"in\",\n      \"Philip.\",\n      \"Journ.\",\n      \"Si.\",\n      \"1\",\n      \"(1906)\",\n      \"Suppl.\",\n      \"343.\",\n      \"Agrostis\",\n      \"maxima\",\n      \"Roxb.\",\n      \"Fl.\",\n      \"Ind.\",\n      \"1\",\n      \"(1820)\",\n      \"319.\",\n      \"Thysanolaena\",\n      \"agrostis\",\n      \"Nees\",\n      \"in\",\n      \"Edinb.\",\n      \"New\",\n      \"Philos.\",\n      \"Journ.\",\n      \"18\",\n      \"(1835)\",\n      \"180.\",\n      \"Thysanolaena\",\n      \"acarifera\",\n      \"Arn.\",\n      \"&\",\n      \"Nees\",\n      \"in\",\n      \"Nov.\",\n      \"Act.\",\n      \"Acad.\",\n      \"Nat.\",\n      \"Cur.\",\n      \"1\",\n      \"9\",\n      \"(1843)\",\n      \"Suppl.\",\n      \"1:\",\n      \"181,\",\n      \"Vidal\",\n      \"Rev.\",\n      \"PI.\",\n      \"Vase.\",\n      \"Filip.\",\n      \"(1886)\",\n      \"289.\",\n      \"Luzon\",\n      \"(Ilocos\",\n      \"Norte,\",\n      \"Apayao,\",\n      \"Bontoc,\",\n      \"Benguet,\",\n      \"Nueva\",\n      \"Vizcaya,\",\n      \"Nueva\",\n      \"Ecija,\",\n      \"Bulacan,\",\n      \"Zambales,\",\n      \"Bataan,\",\n      \"Laguna,\",\n      \"Tayabas,\",\n      \"Sorsogon),\",\n      \"Mindoro,\",\n      \"Palawan,\",\n      \"Mindanao.\",\n      \"On\",\n      \"banks\",\n      \"of\",\n      \"ravines,\",\n      \"slopes,\",\n      \"about\",\n      \"cliffs,\",\n      \"etc,\",\n      \"at\",\n      \"low\",\n      \"and\",\n      \"\\\"medium,\",\n      \"altitudes,\",\n      \"ascending\",\n      \"to\",\n      \"1,600\",\n      \"m.\",\n      \"India\",\n      \"through\",\n      \"Malaya\",\n      \"to\",\n      \"New\",\n      \"Guinea.\",\n      \"Local\",\n      \"names:\",\n      \"Bugubui\",\n      \"(Pamp)\",\n      \";\",\n      \"buybui\",\n      \"(Ilk);\",\n      \"eagadu\",\n      \"(Bon);\",\n      \"gatbo\",\n      \"(Bik)\",\n      \";\",\n      \"lasa\",\n      \"(Tag);\",\n      \"tagadeu\",\n      \"(Bon)\",\n      \",\",\n      \"tagisa\",\n      \"(Sub);\",\n      \"tambu\",\n      \"(Tag);\",\n      \"talankaran\",\n      \"(Ig).\",\n      \"24.\",\n      \"DIGITARIA\",\n      \"Scopoli\",\n      \"DIGITARIA\",\n      \"CHINENSIS\",\n      \"Nees\",\n      \"in\",\n      \"Hook.\",\n      \"&\",\n      \"Arn.\",\n      \"Bot.\",\n      \"Beechey\",\n      \"Voy.\",\n      \"(1841)\",\n      \"231.\",\n      \"Paspalum\",\n      \"ftlicidme\",\n      \"Nees\",\n      \"ex\",\n      \"Thwaites\",\n      \"Enum.\",\n      \"PL\",\n      \"Zeyl.\",\n      \"(1859-64)\",\n      \"358.\",\n      \"Digitaria\",\n      \"violascens\",\n      \"Merr.\",\n      \"in\",\n      \"Philip.\",\n      \"Journ.\",\n      \"Sci.\",\n      \"1\",\n      \"(1906)\",\n      \"Suppl.\",\n      \"347,\",\n      \"3\",\n      \"(1908)\",\n      \"Bot.\",\n      \"396,\",\n      \"Fl.\",\n      \"Manila\",\n      \"(1912)\",\n      \"88,\",\n      \"non\",\n      \"Link.\",\n      \"Batan\",\n      \"Islands\",\n      \"and\",\n      \"northern\",\n      \"Luzon\",\n      \"to\",\n      \"Palawan\",\n      \"and\",\n      \"Mindanao,\",\n      \"in\",\n      \"most\",\n      \"islands\",\n      \"and\",\n      \"provinces,\",\n      \"Merrill\",\n      \"4148,\",\n      \"9388,\",\n      \"7107,\",\n      \"7677,\",\n      \"4788,\",\n      \"Phil.\",\n      \"PL\",\n      \"17\",\n      \"U,\",\n      \"B.\",\n      \"S.\",\n      \"21715,\",\n      \"14524,\",\n      \"13939,\",\n      \"8250\",\n      \"Ramos.\",\n      \"In\",\n      \"open\",\n      \"grasslands\",\n      \"at\",\n      \"low\",\n      \"and\",\n      \"medium\",\n      \"altitudes.\",\n      \"India\",\n      \"to\",\n      \"China\",\n      \"and\",\n      \"Malaya.\",\n      \"Local\",\n      \"names:\",\n      \"Timi\",\n      \"(Bon);\",\n      \"tumi\",\n      \"(Bon);\",\n      \"ualisibis\",\n      \"(Iv).\",\n      \"DIGITARIA\",\n      \"CORYMBOSA\",\n      \"(Roxb)\",\n      \"comb.\",\n      \"nov.\",\n      \"Panicum\",\n      \"co-rymbosum\",\n      \"Roxb.\",\n      \"Hort.\",\n      \"Beng.\",\n      \"(1814)\",\n      \"7,\",\n      \"nomen\",\n      \"nudum,\",\n      \"Fl.\",\n      \"Ind.\",\n      \"1\",\n      \"(1820)\",\n      \"292.\",\n      \"Panicum\",\n      \"microbachne\",\n      \"Presl\",\n      \"Rel.\",\n      \"Haenk.\",\n      \"1\",\n      \"(1828)\",\n      \"298;\",\n      \"Kunth\",\n      \"Enum.\",\n      \"1\",\n      \"(1833)\",\n      \"81.\",\n      \"Digitaria\",\n      \"consanguinea\",\n      \"Merr.\",\n      \"in\",\n      \"Philip.\",\n      \"Journ.\",\n      \"Sci.\",\n      \"1\",\n      \"(1906)\",\n      \"Suppl.\",\n      \"347,\",\n      \"3\",\n      \"(1908)\",\n      \"Bot.\",\n      \"396,\",\n      \"Fl.\",\n      \"Manila\",\n      \"(1912)\",\n      \"88,\",\n      \"Sp.\",\n      \"Blancoanae\",\n      \"(1918)\",\n      \"64,\",\n      \"non\",\n      \"Gaudich.\",\n      \"Panicum\",\n      \"sanguinale\",\n      \"Rolfe\",\n      \"in\",\n      \"Journ.\",\n      \"Bot.\",\n      \"23\",\n      \"(1885)\",\n      \"216;\",\n      \"Vidal\",\n      \"Phan.\",\n      \"Cuming.\",\n      \"Philip.\",\n      \"(1885)\",\n      \"157,\",\n      \"non\",\n      \"Linn.\",\n      \"Digitaria\",\n      \"lanosa\",\n      \"Llanos\",\n      \"Frag.\",\n      \"PI.\",\n      \"Filip.\",\n      \"(1851)\",\n      \"28;\",\n      \"F-vill.\",\n      \"&\",\n      \"Naves\",\n      \"in\",\n      \"Blanco\",\n      \"Fl.\",\n      \"Filip.\",\n      \"ed.\",\n      \"3,\",\n      \"4\",\n      \"1\",\n      \"(1880)\",\n      \"19.\",\n      \"Panicum\",\n      \"gaudichaudii\",\n      \"F-vill.\",\n      \"Novis.\",\n      \"App.\",\n      \"(1882)\",\n      \"311,\",\n      \"non\",\n      \"Kunth.\",\n      \"Panicum\",\n      \"pruriens\",\n      \"Trin.\",\n      \"var.\",\n      \"glabrum\",\n      \"Nees\",\n      \"in\",\n      \"Hook.\",\n      \"Journ.\",\n      \"Bot.\",\n      \"Kew\",\n      \"Miscel.\",\n      \"2\",\n      \"(1850)\",\n      \"97.\",\n      \"Throughout\",\n      \"the\",\n      \"Philippines\",\n      \"at\",\n      \"low\",\n      \"and\",\n      \"medium\",\n      \"altitudes\",\n      \"in\",\n      \"open\",\n      \"waste\",\n      \"places,\",\n      \"old\",\n      \"clearings,\",\n      \"etc,\",\n      \"usually\",\n      \"common,\",\n      \"Merrill\",\n      \"4-230,\",\n      \"5347,\",\n      \"7368,\",\n      \"9276,\",\n      \"Sp.\",\n      \"Blancoanae\",\n      \"787,\",\n      \"Phil.\",\n      \"PL\",\n      \"126,\",\n      \"Elmer\",\n      \"18216,\",\n      \"16818,\",\n      \"164-17,\",\n      \"9570,\",\n      \"B.\",\n      \"S.\",\n      \"12036,\",\n      \"13334,\",\n      \"32736,\",\n      \"14746\",\n      \"Ramos.\",\n      \"India\",\n      \"to\",\n      \"southern\",\n      \"China\",\n      \"and\",\n      \"Formosa\",\n      \"through\",\n      \"Malaya\",\n      \"to\",\n      \"Samoa.\",\n      \"Local\",\n      \"names:\",\n      \"Banana\",\n      \"(Sub);\",\n      \"bacilli\",\n      \"(C.\",\n      \"Bis);\",\n      \"balisibis\",\n      \"(Iv)\",\n      \";\",\n      \"baud\"\n    ],\n    \"ner_tags\": [\n      \"O\",\n      \"O\",\n      \"B-TAXON\",\n      \"O\",\n      \"O\",\n      \"B-TAXON\",\n      \"I-TAXON\",\n      \"B-TAXON\",\n      \"I-TAXON\",\n      \"I-TAXON\",\n      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\"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"B-GEOGRAPHICAL_LOCATION\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"B-HABITAT\",\n      \"I-HABITAT\",\n      \"I-HABITAT\",\n      \"B-HABITAT\",\n      \"I-HABITAT\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"B-TAXON\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"O\",\n      \"B-GEOGRAPHICAL_LOCATION\",\n      \"O\",\n      \"B-GEOGRAPHICAL_LOCATION\",\n      \"I-GEOGRAPHICAL_LOCATION\",\n      \"O\",\n      \"B-GEOGRAPHICAL_LOCATION\",\n      \"O\",\n      \"B-GEOGRAPHICAL_LOCATION\",\n      \"O\",\n      \"B-GEOGRAPHICAL_LOCATION\",\n      \"O\",\n      \"O\",\n      \"B-TAXON\",\n      \"O\",\n      \"B-TAXON\",\n      \"O\",\n      \"O\",\n      \"B-TAXON\",\n      \"O\",\n      \"O\",\n      \"B-TAXON\"\n    ]\n  }\n}\n```\n\n*Note: Some content was truncated for display.*\n\n## Prompt Template\n\n**Prompt Template:**\n```text\nYou are a named entity recognition system that identifies the following entity types:\n{entities}\n\nProcess the provided text and mark all named entities with XML-style tags.\n\nFor example:\n<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.\n\nAvailable entity tags: {entity_list}\n\nINSTRUCTIONS:\n1. Wrap your entire response in <response>...</response> tags.\n2. Inside these tags, include the original text with entity tags inserted.\n3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).\n4. Tag ALL entities you can identify using the exact tag names provided.\n5. Do not include explanations, just the tagged text.\n6. If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\n{text}\n\n```\n\n<details>\n<summary>Few-shot Template</summary>\n\n```text\nHere are some examples of named entity recognition:\n\n{fewshot}\n\nYou are a named entity recognition system that identifies the following entity types:\n{entities}\n\nProcess the provided text and mark all named entities with XML-style tags.\n\nFor example:\n<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.\n\nAvailable entity tags: {entity_list}\n\nINSTRUCTIONS:\n1. Wrap your entire response in <response>...</response> tags.\n2. Inside these tags, include the original text with entity tags inserted.\n3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).\n4. Tag ALL entities you can identify using the exact tag names provided.\n5. Do not include explanations, just the tagged text.\n6. If entity spans overlap, choose the most specific entity type.\n7. Ensure every opening tag has a matching closing tag.\n\nText to process:\n{text}\n\n```\n\n</details>\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets copious \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['copious'],\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# Copious\n\n## 概述\n\nCopious 语料库是一个用于生物多样性实体识别的黄金标准语料库，包含从生物多样性遗产图书馆（Biodiversity Heritage Library）下载的 668 篇文档，涵盖超过 26,000 个句子和 28,000 多个实体，内容涉及分类学和生态学信息。\n\n## 任务描述\n\n- **任务类型**：生物多样性命名实体识别（NER）\n- **输入**：生物多样性文献文本\n- **输出**：识别出的生物多样性实体片段\n- **领域**：生物多样性、生态学、分类学\n\n## 主要特点\n\n- 来自生物多样性遗产图书馆的 668 篇文档\n- 超过 26,000 个句子\n- 超过 28,000 个实体标注\n- 五种生物多样性专用实体类型\n- 生物多样性 NLP 的黄金标准\n\n## 评估说明\n\n- 默认配置使用 **5-shot** 评估\n- 评估指标：精确率（Precision）、召回率（Recall）、F1 分数（F1-Score）、准确率（Accuracy）\n- 实体类型：TAXON（分类单元）、GEOGRAPHICAL_LOCATION（地理位置）、HABITAT（栖息地）、PERSON（人物）、TEMPORAL_EXPRESSION（时间表达）\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `copious` |\n| **数据集 ID** | [extraordinarylab/copious](https://modelscope.cn/datasets/extraordinarylab/copious/summary) |\n| **论文** | N/A |\n| **标签** | `Knowledge`, `NER` |\n| **指标** | `precision`, `recall`, `f1_score`, `accuracy` |\n| **默认示例数** | 5-shot |\n| **评估集** | `test` |\n| **训练集** | `train` |\n\n\n## 数据统计\n\n*统计数据不可用。*\n\n## 样例示例\n\n**子集**: `default`\n\n```json\n{\n  \"input\": [\n    {\n      \"id\": \"a4f5bffd\",\n      \"content\": \"Here are some examples of named entity recognition:\\n\\nInput:\\nFAMILY SERRANIDAE — SCHULTZ 363 Genus PLECTROPOMUS Oken Plectropomus Oken, Isis, p. 1182] 1782 (on \\\"Les Plectropomes\\\" Cuvier, Rgne animal, vol. 2, p. 277, 1817). (Genotype, Bodianus  ... [TRUNCATED] ... 9276, Sp. Blancoanae 787, Phil. PL 126, Elmer 18216, 16818, 164-17, 9570, B. S. 12036, 13334, 32736, 14746 Ramos. India to southern China and Formosa through Malaya to Samoa. Local names: Banana (Sub); bacilli (C. Bis); balisibis (Iv) ; baud\\n\"\n    }\n  ],\n  \"target\": \"<response>Vo] l <taxon>GRAMINEAE</taxon> 53 23. <taxon>THYSANOLAENA Nees</taxon> <taxon>THYSANOLAENA MAXIMA (Roxb) O. Kuntze</taxon> Rev. Gen. PI. (1891) 794; Pilger in Perk. Frag. Fl. Philip. (1904) 141; Merr. in Philip. Journ. Si. 1 (1906)  ... [TRUNCATED] ... aphical_location>Malaya</geographical_location> to <geographical_location>Samoa.</geographical_location> Local names: <taxon>Banana</taxon> (Sub); <taxon>bacilli</taxon> (C. Bis); <taxon>balisibis</taxon> (Iv) ; <taxon>baud</taxon></response>\",\n  \"id\": 0,\n  \"group_id\": 0,\n  \"metadata\": {\n    \"tokens\": [\n      \"Vo]\",\n      \"l\",\n      \"GRAMINEAE\",\n      \"53\",\n      \"23.\",\n      \"THYSANOLAENA\",\n      \"Nees\",\n      \"THYSANOLAENA\",\n      \"MAXIMA\",\n      \"(Roxb)\",\n      \"O.\",\n      \"Kuntze\",\n      \"Rev.\",\n      \"Gen.\",\n      \"PI.\",\n      \"(1891)\",\n      \"794;\",\n      \"Pilger\",\n      \"in\",\n      \"Perk.\",\n      \"Frag.\",\n      \"Fl.\",\n      \"Philip.\",\n      \"(1904)\",\n      \"141;\",\n      \"Merr.\",\n      \"in\",\n      \"Philip.\",\n      \"Journ.\",\n      \"Si.\",\n      \"1\",\n      \"(1906)\",\n      \"Suppl.\",\n      \"343.\",\n      \"Agrostis\",\n      \"maxima\",\n      \"Roxb.\",\n      \"Fl.\",\n      \"Ind.\",\n      \"1\",\n      \"(1820)\",\n      \"319.\",\n      \"Thysanolaena\",\n      \"agrostis\",\n      \"Nees\",\n      \"in\",\n      \"Edinb.\",\n      \"New\",\n      \"Philos.\",\n      \"Journ.\",\n      \"18\",\n      \"(1835)\",\n      \"180.\",\n      \"Thysanolaena\",\n      \"acarifera\",\n      \"Arn.\",\n      \"&\",\n      \"Nees\",\n      \"in\",\n      \"Nov.\",\n      \"Act.\",\n      \"Acad.\",\n      \"Nat.\",\n      \"Cur.\",\n      \"1\",\n      \"9\",\n      \"(1843)\",\n      \"Suppl.\",\n      \"1:\",\n      \"181,\",\n      \"Vidal\",\n      \"Rev.\",\n      \"PI.\",\n      \"Vase.\",\n      \"Filip.\",\n      \"(1886)\",\n      \"289.\",\n      \"Luzon\",\n      \"(Ilocos\",\n      \"Norte,\",\n      \"Apayao,\",\n      \"Bontoc,\",\n      \"Benguet,\",\n      \"Nueva\",\n      \"Vizcaya,\",\n      \"Nueva\",\n      \"Ecija,\",\n      \"Bulacan,\",\n      \"Zambales,\",\n      \"Bataan,\",\n      \"Laguna,\",\n      \"Tayabas,\",\n      \"Sorsogon),\",\n      \"Mindoro,\",\n      \"Palawan,\",\n      \"Mindanao.\",\n      \"On\",\n      \"banks\",\n      \"of\",\n      \"ravines,\",\n      \"slopes,\",\n      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\"B-TAXON\"\n    ]\n  }\n}\n```\n\n*注：部分内容因展示需要已被截断。*\n\n## 提示模板\n\n**提示模板：**\n```text\n你是一个命名实体识别系统，能够识别以下类型的实体：\n{entities}\n\n请处理提供的文本，并使用 XML 风格的标签标记所有命名实体。\n\n例如：\n<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.\n\n可用的实体标签：{entity_list}\n\n说明：\n1. 将你的完整回复用 <response>...</response> 标签包裹。\n2. 在这些标签内，插入带有实体标签的原始文本。\n3. 不得以任何方式修改原始文本（保留空格、标点、大小写等）。\n4. 使用提供的确切标签名称标记所有你能识别的实体。\n5. 不要包含解释，仅返回带标签的文本。\n6. 如果实体范围重叠，请选择最具体的实体类型。\n7. 确保每个开始标签都有对应的结束标签。\n\n待处理文本：\n{text}\n\n```\n\n<details>\n<summary>少样本（Few-shot）模板</summary>\n\n```text\n以下是命名实体识别的一些示例：\n\n{fewshot}\n\n你是一个命名实体识别系统，能够识别以下类型的实体：\n{entities}\n\n请处理提供的文本，并使用 XML 风格的标签标记所有命名实体。\n\n例如：\n<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.\n\n可用的实体标签：{entity_list}\n\n说明：\n1. 将你的完整回复用 <response>...</response> 标签包裹。\n2. 在这些标签内，插入带有实体标签的原始文本。\n3. 不得以任何方式修改原始文本（保留空格、标点、大小写等）。\n4. 使用提供的确切标签名称标记所有你能识别的实体。\n5. 不要包含解释，仅返回带标签的文本。\n6. 如果实体范围重叠，请选择最具体的实体类型。\n7. 确保每个开始标签都有对应的结束标签。\n\n待处理文本：\n{text}\n\n```\n\n</details>\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets copious \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['copious'],\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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