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 *.zip filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+tags:
+- gliner
+- ner
+- medical
+- french
+language:
+- fr
+---
+# BRIGHT NER: GLiNER2 fine-tuned for dates_outcomes
+
+## Description
+This is a GLiNER2 architecture fine-tuned to extract clinical neuro-oncology entities related to the `dates_outcomes` semantic group. It was trained on a synthetic dataset generated for the properly de-identified BRIGHT project dataset (see the `generated_data` folder in the primary repository).
+
+This model repository was specifically designed to fit within the `bright_db` overarching namespace.
+
+## Fields
+It extracts the following fields (described in French):
+- **date_chir**: Date intervention neurochirurgicale ou résection
+- **date_rcp**: Date réunion concertation pluridisciplinaire
+- **dn_date**: Date dernières nouvelles ou dernier suivi
+- **date_deces**: Date décès patient (seulement si décédé)
+- **date_1er_symptome**: Date apparition premiers symptômes
+- **exam_radio_date_decouverte**: Date premier examen découvrant la tumeur
+- **date_progression**: Date récidive/progression
+- **survie_globale**: Durée survie en mois
+- **infos_deces**: Circonstances décès
+
+## Performance on Validation Set
+**Aggregates**:
+- Macro F1: 0.2458 (Precision: 0.2195, Recall: 0.6817)
+- Micro F1: 0.3032 (Precision: 0.1861, Recall: 0.8171)
+
+**Per-Label Breakdowns**:
+| Label | Precision | Recall | F1 |
+|---|---|---|---|
+| date_chir | 0.0615 | 1.0000 | 0.1159 |
+| date_rcp | 0.7347 | 0.9231 | 0.8182 |
+| dn_date | 0.0000 | 0.0000 | 0.0000 |
+| date_deces | 0.0000 | 0.0000 | 0.0000 |
+| date_1er_symptome | 0.0615 | 1.0000 | 0.1159 |
+| exam_radio_date_decouverte | 0.0462 | 1.0000 | 0.0882 |
+| date_progression | 0.0333 | 1.0000 | 0.0645 |
+| survie_globale | 0.3714 | 0.8125 | 0.5098 |
+| infos_deces | 0.6667 | 0.4000 | 0.5000 |
+
+## Usage
+
+```python
+# Inference Code
+from gliner2 import GLiNER2
+
+model = GLiNER2.from_pretrained("raphael-r/bright-gliner-dates_outcomes")
+text = "Patient presenting with epileptic seizures..."
+entities = model.extract_entities(text)
+
+for entity in entities:
+    print(entity["text"], "=>", entity["label"])
+```

config.json

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+{
+  "_attn_implementation_autoset": true,
+  "counting_layer": "count_lstm",
+  "max_width": 8,
+  "model_name": "microsoft/mdeberta-v3-base",
+  "model_type": "extractor",
+  "token_pooling": "first",
+  "transformers_version": "5.0.0"
+}

encoder_config/config.json

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+{
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+  "dtype": "float32",
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+  "hidden_act": "gelu",
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+  "hidden_size": 768,
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+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "legacy": true,
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+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
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+  "num_hidden_layers": 12,
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+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
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+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 0,
+  "vocab_size": 250112
+}

model.safetensors

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tokenizer.json

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tokenizer_config.json

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+    "[EXAMPLE]",
+    "[OUTPUT]",
+    "[DESCRIPTION]"
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+}