README.md

---
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')])
```