readme: ---
  license: apache-2.0
  language:
  - en
  - ar
  - zh
  - nl
  - fr
  - de
  - hi
  - in
  - it
  - ja
  - pt
  - ru
  - es
  - vi
  - multilingual
  datasets:
  - unicamp-dl/mmarco
  ---
  # Cross-Encoder for multilingual MS Marco
  
  This model was trained on the [MMARCO](https://hf.co/unicamp-dl/mmarco) dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages.
  
  As a base model, we used the [multilingual MiniLMv2](https://huggingface.co/nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large) model.
  
  The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
  
  ## Usage with SentenceTransformers
  
  The usage becomes easy when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
  ```python
  from sentence_transformers import CrossEncoder
  model = CrossEncoder('model_name')
  scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
  ```
  
  
  
  
  ## Usage with Transformers
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  model = AutoModelForSequenceClassification.from_pretrained('model_name')
  tokenizer = AutoTokenizer.from_pretrained('model_name')
  
  features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'],  padding=True, truncation=True, return_tensors="pt")
  
  model.eval()
  with torch.no_grad():
      scores = model(**features).logits
      print(scores)
  ```
  
  
  
  