Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 language: en
-license: cc-by-nc-4.0
+license: apache-2.0
 library_name: transformers
 pipeline_tag: token-classification
 tags:
@@ -13,10 +13,17 @@ tags:
 
 # Feline-NER
 
+Named Entity Recognition model for feline veterinary medicine, trained on 1,300 annotated sentences from PubMed literature.
+
 ## Model Description
-**Feline-NER** is a transformer-based named entity recognition (NER) model for **feline veterinary scientific literature**.
 
-The model follows the BERT architecture and was initialized from **BioBERT v1.2**, further domain-adapted on feline-related PubMed Central articles (**Feline-BERT**), and fine-tuned for NER (**Feline-NER**).
+**Feline-NER** is a token classification model fine-tuned for extracting clinical entities from feline veterinary scientific literature.
+
+**Model lineage:**
+- Base: **BERT** (Devlin et al., 2019)
+- Domain-adapted: **BioBERT v1.2** (Lee et al., 2019) - biomedical literature
+- Further adapted: **Feline-BERT** - 11,830 feline PubMed articles (MLM fine-tuning)
+- Task-specific: **Feline-NER** - 1,300 manually annotated sentences (token classification)
 
 This model is intended **solely for research and educational use**.
 
@@ -44,6 +51,17 @@ Performance varies by entity type; PROCEDURE and ANATOMY remain challenging due
 - Information extraction from feline scientific literature
 - Educational demonstrations
 
+## Usage
+
+This model can be used with the Hugging Face `pipeline` API:
+
+```python
+from transformers import pipeline
+ner = pipeline("ner", model="Statistical-Impossibility/Feline-NER", aggregation_strategy="simple")
+print(ner("The cat was diagnosed with FIV and treated with prednisolone."))
+```
+
+
 ## ⚠️ Limitations & Warnings
 - **NOT FOR CLINICAL USE**
 - Not validated for diagnosis or treatment
@@ -51,4 +69,4 @@ Performance varies by entity type; PROCEDURE and ANATOMY remain challenging due
 - Single-annotator dataset
 
 ## Author
-Statistical-Impossibility
+Statistical-Impossibility