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{ "model_name": "test/model", "seed": 42, "device": "cpu", "architecture": "bitag", "threshold": 0.4, "training_steps": 4000 }
{ "emea_ner": { "f1": 0.6, "precision": 0.62, "recall": 0.58 }, "medline_ner": { "f1": 0.65, "precision": 0.67, "recall": 0.63 }, "cas1_ner": { "f1": 0.7, "precision": 0.68, "recall": 0.72 }, "cas2_ner": { "f1": 0.67, "precision": 0.65, "recall": 0.69 } }
{ "final_step": 3200, "best_validation_loss": 0.0016, "early_stopped": true }
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French Medical NLP Leaderboard - Results Dataset

This dataset contains evaluation results for the French Medical NLP Leaderboard.

Results Format

Each results file should be a JSON file with the following structure:

{
  "config": {
    "model_dtype": "float16",
    "model_name": "model_name",
    "model_sha": "revision"
  },
  "results": {
    "emea_ner": {
      "f1": 0.85,
      "precision": 0.83,
      "recall": 0.87
    },
    "medline_ner": {
      "f1": 0.82,
      "precision": 0.80,
      "recall": 0.84
    }
  }
}

Tasks

  • emea_ner: French medical NER on EMEA texts
  • medline_ner: French medical NER on MEDLINE abstracts

Metrics

All metrics use seqeval with IOB2 scheme:

  • f1: Micro F1 score
  • precision: Micro precision
  • recall: Micro recall
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