Update README.md

README.md

@@ -52,6 +52,21 @@ pip install git+https://github.com/urchade/GLiNER.git
 ---
 
 ## Usage
+If you need an open ontology entity extraction use tag `label` in the list of labels, please check example below:
+
+```python
+from gliner import GLiNER
+
+model = GLiNER.from_pretrained("knowledgator/gliner-decoder-small-v1.0")
+
+text = "Hugging Face is a company that advances and democratizes artificial intelligence through open source and science."
+
+labels = ["label"]
+
+model.predict_entities(text, labels, threshold=0.3, num_gen_sequences=1)
+```
+
+If you need to run a model on many text and/or set some labels constraints, please check example below:
 
 ```python
 from gliner import GLiNER
@@ -64,7 +79,7 @@ text = (
     "develop and sell Wozniak's Apple I personal computer."
 )
 
-labels = ["person", "other"]
+labels = ["person", "company", "date"]
 
 model.run([text], labels, threshold=0.3, num_gen_sequences=1)
 ```
@@ -80,7 +95,58 @@ model.run([text], labels, threshold=0.3, num_gen_sequences=1)
       "start": 21,
       "end": 26,
       "text": "Apple",
-      "label": "other",
+      "label": "company",
+      "score": 0.6795641779899597,
+      "generated labels": ["Organization"]
+    },
+    {
+      "start": 47,
+      "end": 60,
+      "text": "April 1, 1976",
+      "label": "date",
+      "score": 0.44296327233314514,
+      "generated labels": ["Date"]
+    },
+    {
+      "start": 65,
+      "end": 78,
+      "text": "Steve Wozniak",
+      "label": "person",
+      "score": 0.9934439659118652,
+      "generated labels": ["Person"]
+    },
+    {
+      "start": 80,
+      "end": 90,
+      "text": "Steve Jobs",
+      "label": "person",
+      "score": 0.9725918769836426,
+      "generated labels": ["Person"]
+    },
+    {
+      "start": 107,
+      "end": 119,
+      "text": "Ronald Wayne",
+      "label": "person",
+      "score": 0.9964536428451538,
+      "generated labels": ["Person"]
+    }
+  ]
+]
+```
+
+---
+
+### Example Output
+
+```json
+[
+  [
+    {
+      "start": 21,
+      "end": 26,
+      "text": "Apple",
+      "label": "company",
       "score": 0.6795641779899597,
       "generated labels": ["Organization"]
     },
@@ -88,7 +154,7 @@ model.run([text], labels, threshold=0.3, num_gen_sequences=1)
       "start": 47,
       "end": 60,
       "text": "April 1, 1976",
-      "label": "other",
+      "label": "time",
       "score": 0.44296327233314514,
       "generated labels": ["Date"]
     },