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--- |
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library_name: transformers |
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tags: |
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- unsloth |
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- trl |
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- sft |
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- med |
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- mistral |
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- quaero |
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--- |
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# Mistral 7B fine-tuned on Quaero for Named Entity Recognition (Generative) |
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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). |
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## Task |
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The model was trained to extract entities from French biomedical sentences (medlines) using a structured, prompt-based format. |
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| Tag | Description | |
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| ------ | ----------------------------------------------------------- | |
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| `DISO` | **Diseases** or health-related conditions | |
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| `ANAT` | **Anatomical parts** (organs, tissues, body regions, etc.) | |
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| `PROC` | **Medical or surgical procedures** | |
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| `DEVI` | **Medical devices or instruments** | |
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| `CHEM` | **Chemical substances or medications** | |
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| `LIVB` | **Living beings** (e.g. humans, animals, bacteria, viruses) | |
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| `GEOG` | **Geographical locations** (e.g. countries, regions) | |
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| `OBJC` | **Physical objects** not covered by other categories | |
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| `PHEN` | **Biological processes** (e.g. inflammation, mutation) | |
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| `PHYS` | **Physiological functions** (e.g. respiration, vision) | |
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I use `<>` as a separator and the output format is : |
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``` |
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TAG_1 entity_1 <> TAG_2 entity_2 <> ... <> TAG_n entity_n |
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``` |
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## Dataset |
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The original dataset is Quaero French Medical Corpus and I converted it to a JSON format for generative instruction-style training. |
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```json |
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{ |
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"input": "Etude de l'efficacité et de la tolérance de la prazosine à libération prolongée chez des patients hypertendus et diabétiques non insulinodépendants.", |
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"output": "DISO tolérance <> CHEM prazosine <> LIVB patients <> DISO hypertendus <> DISO diabétiques non insulinodépendants" |
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} |
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``` |
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The QUAERO French Medical corpus features **overlapping entity spans**, including nested structures, for instance : |
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```json |
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{ |
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"input": "Cancer du pancréas", |
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"output": "DISO Cancer <> DISO Cancer du pancréas <> ANAT pancréas" |
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} |
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``` |
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## Evaluation |
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Evaluation was performed on the test split by comparing the predicted entity set against the ground truth annotations using exact (type, entity) matching. |
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| Metric | Score | |
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| --------- | ------ | |
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| Precision | 0.6883 | |
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| Recall | 0.7143 | |
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| F1 Score | 0.7011 | |
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## Other formats |
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This model is also available in the following formats: |
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- **LoRA Adapter** |
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→ [yqnis/mistral-7b-quaero-lora](https://huggingface.co/yqnis/mistral-7b-quaero-lora) |
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- **GGUF Q5_K_M** |
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→ [yqnis/mistral-7b-quaero-gguf](https://huggingface.co/yqnis/mistral-7b-quaero-gguf) |
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This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
