Update README.md

README.md

@@ -1,3 +1,59 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- unicamp-dl/mmarco
+language:
+- vi
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- cross-encoder
+- rerank
+---
+
+## Installation
+ -  Install `sentence-transformers` (recommend):
+	
+	- `pip install sentence-transformers`
+
+ -  Install `transformers` (optional):
+
+	- `pip install transformers`
+    
+ - Install `pyvi` to word segment:
+
+	- `pip install pyvi`
+
+
+## Usage with transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model = AutoModelForSequenceClassification.from_pretrained('itdainb/vietnamese-cross-encoder')
+tokenizer = AutoTokenizer.from_pretrained('itdainb/vietnamese-cross-encoder')
+
+features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'],  padding=True, truncation=True, return_tensors="pt")
+
+model.eval()
+with torch.no_grad():
+    scores = model(**features).logits
+    print(scores)
+```
+
+## Usage with sentence-transformers
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder('itdainb/vietnamese-cross-encoder', max_length=256)
+scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
+```