[
  {
    "F1": 78.58,
    "code_links": [
      {
        "title": "ncbi-nlp/BLUE_Benchmark",
        "url": "https://github.com/ncbi-nlp/BLUE_Benchmark"
      },
      {
        "title": "wonjininfo/CollaboNet",
        "url": "https://github.com/wonjininfo/CollaboNet"
      }
    ],
    "date": "2018-09-21",
    "date2": 20180921,
    "model": "CollaboNet",
    "paper": {
      "title": "CollaboNet: collaboration of deep neural networks for biomedical named entity recognition",
      "url": "https://cknow.io/lib/84b96b97e29a0b27"
    },
    "paper_data_uoa": "84b96b97e29a0b27"
  },
  {
    "F1": 77.59,
    "code_links": [
      {
        "title": "dmis-lab/biobert",
        "url": "https://github.com/dmis-lab/biobert"
      },
      {
        "title": "re-search/DocProduct",
        "url": "https://github.com/re-search/DocProduct"
      },
      {
        "title": "naver/biobert-pretrained",
        "url": "https://github.com/naver/biobert-pretrained"
      },
      {
        "title": "EmilyAlsentzer/clinicalBERT",
        "url": "https://github.com/EmilyAlsentzer/clinicalBERT"
      },
      {
        "title": "ncbi-nlp/NCBI_BERT",
        "url": "https://github.com/ncbi-nlp/NCBI_BERT"
      },
      {
        "title": "MeRajat/SolvingAlmostAnythingWithBert",
        "url": "https://github.com/MeRajat/SolvingAlmostAnythingWithBert"
      },
      {
        "title": "dmis-lab/bern",
        "url": "https://github.com/dmis-lab/bern"
      },
      {
        "title": "phucdev/TL_Bio_RE",
        "url": "https://github.com/phucdev/TL_Bio_RE"
      },
      {
        "title": "ardakdemir/my_bert_ner",
        "url": "https://github.com/ardakdemir/my_bert_ner"
      },
      {
        "title": "arnavbhandari/clinical-BioBERT",
        "url": "https://github.com/arnavbhandari/clinical-BioBERT"
      },
      {
        "title": "rahul-1996/KGraphs-QA",
        "url": "https://github.com/rahul-1996/KGraphs-QA"
      },
      {
        "title": "ManasRMohanty/DS5500-capstone",
        "url": "https://github.com/ManasRMohanty/DS5500-capstone"
      }
    ],
    "date": "2019-01-25",
    "date2": 20190125,
    "model": "BioBERT",
    "paper": {
      "title": "BioBERT: a pre-trained biomedical language representation model for biomedical text mining",
      "url": "https://cknow.io/lib/b747fc141d2a00b5"
    },
    "paper_data_uoa": "b747fc141d2a00b5"
  },
  {
    "F1": 77.03,
    "code_links": [
      {
        "title": "shreyashub/BioFLAIR",
        "url": "https://github.com/shreyashub/BioFLAIR"
      }
    ],
    "date": "2019-08-13",
    "date2": 20190813,
    "model": "BioFLAIR",
    "paper": {
      "title": "BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks",
      "url": "https://cknow.io/lib/3d2e5fa45ecbd7c6"
    },
    "paper_data_uoa": "3d2e5fa45ecbd7c6"
  },
  {
    "F1": 75.95,
    "code_links": [
      {
        "title": "allenai/scibert",
        "url": "https://github.com/allenai/scibert"
      },
      {
        "title": "georgetown-cset/ai-relevant-papers",
        "url": "https://github.com/georgetown-cset/ai-relevant-papers"
      }
    ],
    "date": "2019-03-26",
    "date2": 20190326,
    "model": "SciBERT (SciVocab)",
    "paper": {
      "title": "SciBERT: A Pretrained Language Model for Scientific Text",
      "url": "https://cknow.io/lib/14317a71d52bcaf6"
    },
    "paper_data_uoa": "14317a71d52bcaf6"
  },
  {
    "F1": 75.83,
    "code_links": [
      {
        "title": "allenai/scibert",
        "url": "https://github.com/allenai/scibert"
      },
      {
        "title": "georgetown-cset/ai-relevant-papers",
        "url": "https://github.com/georgetown-cset/ai-relevant-papers"
      }
    ],
    "date": "2019-03-26",
    "date2": 20190326,
    "model": "SciBERT (Base Vocab)",
    "paper": {
      "title": "SciBERT: A Pretrained Language Model for Scientific Text",
      "url": "https://cknow.io/lib/14317a71d52bcaf6"
    },
    "paper_data_uoa": "14317a71d52bcaf6"
  },
  {
    "F1": 74.29,
    "code_links": [
      {
        "title": "daixiangau/naacl2019-select-pretraining-data-for-ner",
        "url": "https://github.com/daixiangau/naacl2019-select-pretraining-data-for-ner"
      }
    ],
    "date": "2019-04-01",
    "date2": 20190401,
    "model": "BiLSTM-CRF with ELMo",
    "paper": {
      "title": "Using Similarity Measures to Select Pretraining Data for NER",
      "url": "https://cknow.io/lib/a2304436aaf6415b"
    },
    "paper_data_uoa": "a2304436aaf6415b"
  }
]