mistral-7b-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, fine-tuned on the Quaero French medical dataset 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.

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.

{
  "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 :

{
  "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.

Other formats

This model is also available in the following formats:

• 16bit
  → yqnis/mistral-7b-quaero

• GGUF Q5_K_M
  → yqnis/mistral-7b-quaero-gguf

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.