
# MiniLM-L6-danish-reranker

This is a lightweight (~22 M parameters) [sentence-transformers](https://www.SBERT.net) model for Danish NLP: It takes two sentences as input and outputs a relevance score. Therefore, the model can be used for information retrieval, e.g. given a query and candidate matches, rank the candidates by their relevance. 

The maximum sequence length is 512 tokens (for both passages).

The model was not pre-trained from scratch but adapted from the English version of [cross-encoder/ms-marco-MiniLM-L-6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) with a [Danish tokenizer](https://huggingface.co/KennethTM/bert-base-uncased-danish).

Trained on ELI5 and SQUAD data machine translated from English to Danish.

## Usage with Transformers

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('KennethTM/MiniLM-L6-danish-reranker')
tokenizer = AutoTokenizer.from_pretrained('KennethTM/MiniLM-L6-danish-reranker')
features = tokenizer(['Kører der cykler på vejen?', 'Kører der cykler på vejen?'], ['En panda løber på vejen.', 'En mand kører hurtigt forbi på cykel.'],  padding=True, truncation=True, return_tensors="pt")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    print(scores)
```

## Usage with SentenceTransformers

The usage becomes easier 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('KennethTM/MiniLM-L6-danish-reranker', max_length=512)
scores = model.predict([('Kører der cykler på vejen?', 'En panda løber på vejen.'), ('Kører der cykler på vejen?', 'En mand kører hurtigt forbi på cykel.')])
```

{"language": ["da"], "license": "mit", "datasets": ["squad", "eli5", "sentence-transformers/embedding-training-data"]}
transformers pytorch safetensors bert text-classification da dataset:squad dataset:eli5 dataset:sentence-transformers/embedding-training-data license:mit autotrain_compatible endpoints_compatible has_space region:us