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---
library_name: transformers
tags:
- unsloth
- trl
- sft
- med
- mistral
- quaero
- lora
---
# Mistral 7B fine-tuned on Quaero for Named Entity Recognition (Generative)
This is a **LoRA adapter** 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:
- **16bit**
→ [yqnis/mistral-7b-quaero](https://huggingface.co/yqnis/mistral-7b-quaero)
- **GGUF Q5_K_M**
→ [yqnis/mistral-7b-quaero-gguf](https://huggingface.co/yqnis/mistral-7b-quaero-gguf)
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |