README.md

---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
widget:
- source_sentence: "This is a Norwegian boy"
  sentences:
    - "Dette er en norsk gutt"
    - "This is an English boy"
    - "This is a dog"
  example_title: "Cross Language"
- source_sentence: "Det er noen dyr utenfor vinduet"
  sentences:
    - "På utsiden kan jeg høre noen hunder"
    - "Noen mennesker prater utenfor vinduet"
    - "Alle burde ha kjæledyr"
  example_title: "Paraphrases"
- source_sentence: "En kvinne sitter i en stol"
  sentences:
    - "A woman is sitting in a chair"
    - "Hun slapper av og leser i en bok"
    - "Hun løper maraton"
  example_title: "Paraphrases across language"
  
---

# NB-SBERT
[Sentence-transformers](https://www.SBERT.net) model trained on the [machine translated mnli-dataset](https://huggingface.co/datasets/NbAiLab/mnli-norwegian) starting from [nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base). 

The model maps sentences & paragraphs to a 768 dimensional dense vector space. This vector can be used for tasks like clustering and semantic search. Below we give some examples on how to use the model in different framework. The easiest way is to simply measure the cosine distance between two sentences. Sentences that are close to each other in meaning, will have a small cosine distance and a similarity close to 1. The model is trained in a way where we try to keep this distance also between languages. Ideally an English-Norwegian sentence pair should have high similarity.

## Keyword Extraction
The model can be used for extracting keywords from the text. The basic technique is to find the words that are most similar to the document. There are various frameworks for doing this. An easy way is to use [keyBERT](https://github.com/MaartenGr/KeyBERT). This example shows how this can be used.

```bash
pip install keybert
```

```bash
from keybert import KeyBERT
from sentence_transformers import SentenceTransformer
sentence_model = SentenceTransformer("NbAiLab/nb-sbert")
kw_model = KeyBERT(model=sentence_model)

doc = """
Nasjonalbiblioteket er et norsk bibliotek som «bevarer fortiden for fremtiden» gjennom en nasjonal kultur- og kunnskapsbase. Rollen som nasjonalbibliotek innebærer blant annet administrering av pliktavleveringsloven som sier at alle dokument som produseres i Norge skal pliktavleveres til Nasjonalbiblioteket. Dette innebærer bøker, aviser, tidsskrifter, digitale dokumenter, film, video, fotografi, kart, kringkasting, lydbøker, musikk, notetrykk, postkort, plakater, småtrykk og teatermateriale. Alt stilles til rådighet for publikum i Nasjonalbibliotekets publikumslokaler i Oslo.
"""
kw_model.extract_keywords(doc, stop_words=None)


```


## Embeddings and Sentence Similarity (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```bash
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer, util
sentences = ["This is a Norwegian boy", "Dette er en norsk gutt"]

model = SentenceTransformer('NbAiLab/nb-sbert')
embeddings = model.encode(sentences)
print(embeddings)

# Compute cosine-similarities with sentence transformers
cosine_scores = util.cos_sim(embeddings[0],embeddings[1])
print(cosine_scores)

# Compute cosine-similarities with SciPy
from scipy import spatial
scipy_cosine_scores = 1 - spatial.distance.cosine(embeddings[0],embeddings[1])
print(scipy_cosine_scores)

# Both should give 0.8250 in the example above.

```



## Embeddings and Sentence Similarity (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 a Norwegian boy", "Dette er en norsk gutt"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('NbAiLab/nb-sbert')
model = AutoModel.from_pretrained('NbAiLab/nb-sbert')

# 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.
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print(embeddings)

# Compute cosine-similarities with SciPy
from scipy import spatial
scipy_cosine_scores = 1 - spatial.distance.cosine(embeddings[0],embeddings[1])
print(scipy_cosine_scores)

# This should give 0.8250 in the example above.

```


## Training
The model was trained with the parameters:

**DataLoader**:

`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 16471 with parameters:
```
{'batch_size': 32}
```

**Loss**:

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

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 1647,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1648,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel 
  (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

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