Update README.md

README.md

@@ -16,6 +16,33 @@ pipeline_tag: text-classification
 
 The model was trained as cross-encoder classification model with the objective to re-rank the results in a QA pipline.
 
+## How to use
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+model = AutoModelForSequenceClassification.from_pretrained("apohllo/albert-xxl-squad-sentences", num_labels=2)
+tokenizer = AutoTokenizer.from_pretrained("apohllo/albert-xxl-squad-sentences")
+
+from transformers import pipeline
+
+# Add device=0 if you want to use GPU!
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, batch_size=16) #, device=0)
+
+sentences = [...]    # some sentences to be re-ranked, wrt to the question
+question = "..."     # a question to be asked against the sentences
+
+samples = [{"text": s, "text_pair": question} for s in sentences]
+results = classifier(samples)
+    
+results = [(idx, r["score"]) if r["label"] == 'LABEL_1' else (idx, 1 - r["score"]) 
+            for idx, r in enumerate(results)]
+
+top_k = 5
+keys_values = sorted(results, key=lambda e: -e[1])[:top_k]
+```
+
 ## Data
 
 The data from SQuAD 2.0 was sentence-split. The question + the sentence containing the answer was a positive example. 
@@ -23,6 +50,8 @@ The question + the remaining sentence from the same Wikipedia passege were treat
 
 The table balow reports the classification results on the validation set.
 
+# Results
+
 
 |                | accuracy | F1       |
 |----------------|----------|----------|