
# Model for English and Russian

This is a truncated version of [jeffwan/mmarco-mMiniLMv2-L12-H384-v1](https://huggingface.co/jeffwan/mmarco-mMiniLMv2-L12-H384-v1).

This model has only English and Russian tokens left in the vocabulary. Thus making it twice as small as the original model while producing the same embeddings.


# 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)
```

The model has been truncated in [this notebook](https://colab.research.google.com/drive/19IFjWpJpxQie1gtHSvDeoKk7CQtpy6bT?usp=sharing). 
{"language": ["en", "ru"], "license": "apache-2.0", "datasets": ["unicamp-dl/mmarco"]}
transformers safetensors xlm-roberta text-classification en ru dataset:unicamp-dl/mmarco license:apache-2.0 autotrain_compatible endpoints_compatible region:us