adding examples to readmes

README.md

@@ -1,12 +1,15 @@
 ---
-license: mit
+licepredictor
 
 widget:
- - text: 'C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C'
+ prtext-classification T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C'
+   example_title: "V3 Macrophage"
  - text: 'C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C' 
- - text: 'C T R P N N N T R R S I R I G P G Q A F Y A T G D I I G D I R Q A H C'
- - text: 'C G R P N N H R I K G L R I G P G R A F F A M G A I G G G E I R Q A H C'
- 
+   example_title: "V3 T-cell"
+
+datasets:
+  - damlab/HIV_V3_bodysitepredictor:
+  - accuractext-classificationorN N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C
 ---
 
 # Model Card for [HIV_V3_bodysite]
@@ -40,7 +43,57 @@ This tool was trained using the Los Alamos HIV sequence dataset (https://www.hiv
 
 ## How to use
 
-*Need to add*
+This model is able to predict the likely bodysite from a V3 sequence.
+This may be use for surveillance of cells that are emerging from latent reservoirs.
+Remember, a sequence can come from multiple sites, they are not mutually exclusive.
+```python
+from transformers import pipeline
+
+predictor = pipeline("text-classification", model="damlab/HIV_V3_bodysite")
+predictor(f"C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C")
+
+[
+  [
+    {
+      "label": "periphery-tcell",
+      "score": 0.29097115993499756
+    },
+    {
+      "label": "periphery-monocyte",
+      "score": 0.014322502538561821
+    },
+    {
+      "label": "CNS",
+      "score": 0.06870711594820023
+    },
+    {
+      "label": "breast-milk",
+      "score": 0.002785981632769108
+    },
+    {
+      "label": "female-genitals",
+      "score": 0.024997007101774216
+    },
+    {
+      "label": "male-genitals",
+      "score": 0.01040483545511961
+    },
+    {
+      "label": "gastric",
+      "score": 0.06872137635946274
+    },
+    {
+      "label": "lung",
+      "score": 0.04432062804698944
+    },
+    {
+      "label": "organ",
+      "score": 0.47476938366889954
+    }
+  ]
+]
+
+```
 
 ## Training Data