Update README.md

README.md

@@ -14,12 +14,29 @@ This model is a 16-bit merged version of [unsloth/Meta-Llama-3.1-8B-Instruct](ht
 
 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 used `<>` 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.
 
-I used `<>` as a separator and the format is : `'TAG_1 entity_1 <> TAG_2 entity_2 <> ... <> TAG_n entity_n'`.
-
 ```json
 {
   "input": "Etude de l'efficacité et de la tolérance de la prazosine à libération prolongée chez des patients hypertendus et diabétiques non insulinodépendants.",