Add example usage

README.md

@@ -42,3 +42,27 @@ Here is a detailed view on how the remaining heads are distributed in the networ
 | ------ | --------- | --------- | --------- |
 | **accuracy** | **91.17** | **92.7** | **-1.53**|
 
+
+## Example Usage
+Install nn_pruning: it contains the optimization script, which just pack the linear layers into smaller ones by removing empty rows/columns.
+
+`pip install nn_pruning`
+
+Then you can use the `transformers library` almost as usual: you just have to call `optimize_model` when the pipeline has loaded.
+
+```python
+from transformers import pipeline
+from nn_pruning.inference_model_patcher import optimize_model
+
+cls_pipeline = pipeline(
+    "text-classification",
+    model="echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid",
+    tokenizer="echarlaix/bert-base-uncased-sst2-acc91.1-d37-hybrid",
+)
+
+print(f"Parameters count (includes only head pruning, no feed forward pruning)={int(cls_pipeline.model.num_parameters() / 1E6)}M")
+cls_pipeline.model = optimize_model(cls_pipeline.model, "dense")
+print(f"Parameters count after optimization={int(cls_pipeline.model.num_parameters() / 1E6)}M")
+predictions = cls_pipeline("This restaurant is awesome")
+print(predictions)
+```