|
--- |
|
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) |
|
``` |
|
|
|
|
|
|
|
|
