Update README.md

README.md

@@ -1,22 +1,82 @@
 ---
-base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
+library_name: transformers
 tags:
-- text-generation-inference
-- transformers
 - unsloth
-- mistral
 - trl
-license: apache-2.0
-language:
-- en
+- sft
+- med
+- mistral
+- quaero
+- lora
 ---
 
-# Uploaded  model
+# LLaMA 3 8B 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)
 
-- **Developed by:** yqnis
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
+- **GGUF Q8_0**  
+  → [yqnis/mistral-7b-quaero-gguf](https://huggingface.co/yqnis/llama3-8b-quaero-yqnis/mistral-7b-quaero-gguf)
 
-This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.