---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
datasets:
- language-and-voice-lab/ruquad1
language:
- is
---

# sbert-ruquad

sbert-ruquald is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

The model is based on the [distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2), fine-tuned on [RUQuAD](https://repository.clarin.is/repository/xmlui/handle/20.500.12537/310) - a question-answer dataset for Icelandic.

The data used for this model contains approximately question-span and question-paragraph pairs, with 14920 pairs used for training under the [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss).


## Usage (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 SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('language-and-voice-lab/sbert-ruquad')
embeddings = model.encode(sentences)
print(embeddings)
```

## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

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


#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('language-and-voice-lab/sbert-ruquad')
model = AutoModel.from_pretrained('language-and-voice-lab/sbert-ruquad')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)
```



## Evaluation Results

The model was evaluated with a hold-out set from the original data using the [BinaryClassificationEvaluator](https://www.sbert.net/docs/package_reference/evaluation.html?highlight=binaryclassificationevaluator#sentence_transformers.evaluation.BinaryClassificationEvaluator) approach. 

| cossim_accuracy | cossim_f1   | cossim_precision | cossim_recall | cossim_ap   | manhattan_accuracy | manhattan_f1 | manhattan_precision | manhattan_recall | manhattan_ap | euclidean_accuracy | euclidean_f1 | euclidean_precision | euclidean_recall | euclidean_ap | dot_accuracy | dot_f1      | dot_precision | dot_recall  | dot_ap      |
|-----------------|-------------|------------------|---------------|-------------|--------------------|--------------|---------------------|------------------|--------------|--------------------|--------------|---------------------|------------------|--------------|--------------|-------------|---------------|-------------|-------------|
| 0.913616792     | 0.910709318 | 0.942429476      | 0.881054898   | 0.968807199 | 0.869483315        | 0.856401384  | 0.922360248         | 0.799246502      | 0.932638132  | 0.869214209        | 0.857062937  | 0.892253931         | 0.824542519      | 0.932737722  | 0.914962325  | 0.911732456 | 0.929050279   | 0.895048439 | 0.968732732 |


For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name="language-and-voice-lab/sbert-ruquad")


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 933 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 20,
    "evaluation_steps": 500,
    "evaluator": "sentence_transformers.evaluation.BinaryClassificationEvaluator.BinaryClassificationEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1000,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Citing & Authors

Stefán Ólafsson (stefanola@ru.is) trained the model.
Njáll Skarphéðinsson et al. created the [RUQuAD dataset](https://repository.clarin.is/repository/xmlui/handle/20.500.12537/310).