yqnis
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mistral-7b-quaero

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 ---
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 ---
 library_name: transformers
+tags:
+- unsloth
+- trl
+- sft
 ---
+# LLaMA 3 8B fine-tuned on Quaero for Named Entity Recognition (Generative)
+This model is a 16-bit merged version of [unsloth/mistral-7b-instruct-v0.3](https://huggingface.co/unsloth/mistral-7b-instruct-v0.3), fine-tuned on the [Quaero French medical dataset](https://quaerofrenchmed.limsi.fr/) using a generative approach to Named Entity Recognition (NER).
+## Task
+The model was trained to extract entities from French biomedical sentences (medlines) using a structured, prompt-based format.
+| Tag    | Description                                                 |
+| ------ | ----------------------------------------------------------- |
+| `DISO` | **Diseases** or health-related conditions                   |
+| `ANAT` | **Anatomical parts** (organs, tissues, body regions, etc.)  |
+| `PROC` | **Medical or surgical procedures**                          |
+| `DEVI` | **Medical devices or instruments**                          |
+| `CHEM` | **Chemical substances or medications**                      |
+| `LIVB` | **Living beings** (e.g. humans, animals, bacteria, viruses) |
+| `GEOG` | **Geographical locations** (e.g. countries, regions)        |
+| `OBJC` | **Physical objects** not covered by other categories        |
+| `PHEN` | **Biological processes** (e.g. inflammation, mutation)      |
+| `PHYS` | **Physiological functions** (e.g. respiration, vision)      |
+I use `<>` as a separator and the output format is :
+```
+TAG_1 entity_1 <> TAG_2 entity_2 <> ... <> TAG_n entity_n'
+```
+## Dataset
+The original dataset is Quaero French Medical Corpus and I converted it to a JSON format for generative instruction-style training.
+```json
+{
+  "input": "Etude de l'efficacité et de la tolérance de la prazosine à libération prolongée chez des patients hypertendus et diabétiques non insulinodépendants.",
+  "output": "DISO tolérance <> CHEM prazosine <> LIVB patients <> DISO hypertendus <> DISO diabétiques non insulinodépendants"
+}
+```
+The QUAERO French Medical corpus features **overlapping entity spans**, including nested structures, for instance :
+```json
+{
+  "input": "Cancer du pancréas",
+  "output": "DISO Cancer <> DISO Cancer du pancréas <> ANAT pancréas"
+}
+```
 ## Evaluation
+Evaluation was performed on the test split by comparing the predicted entity set against the ground truth annotations using exact (type, entity) matching.
+| Metric    | Score  |
+| --------- | ------ |
+| Precision | 0.6883 |
+| Recall    | 0.7143 |
+| F1 Score  | 0.7011 |
+## Other formats
+This model is also available in the following formats:
+- **LoRA Adapter**
+  → [yqnis/mistral-7b-quaero-lora](https://huggingface.co/yqnis/mistral-7b-quaero-lora)
+- **GGUF Q8_0**
+  → [yqnis/mistral-7b-quaero-gguf](https://huggingface.co/yqnis/llama3-8b-quaero-yqnis/mistral-7b-quaero-gguf)
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.