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Model: KBLab/sentence-bert-swedish-cased Source: Original Platform
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README.md
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README.md
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---
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pipeline_tag: sentence-similarity
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lang:
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- sv
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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widget:
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- source_sentence: Mannen åt mat.
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sentences:
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- Han förtärde en närande och nyttig måltid.
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- Det var ett sunkigt hak med ganska gott käk.
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- Han inmundigade middagen tillsammans med ett glas rödvin.
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- Potatischips är jättegoda.
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- Tryck på knappen för att få tala med kundsupporten.
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example_title: Mat
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- source_sentence: Kan jag deklarera digitalt från utlandet?
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sentences:
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- Du som befinner dig i utlandet kan deklarera digitalt på flera olika sätt.
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- >-
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Du som har kvarskatt att betala ska göra en inbetalning till ditt
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skattekonto.
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- >-
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Efter att du har deklarerat går vi igenom uppgifterna i din deklaration och
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räknar ut din skatt.
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- >-
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I din deklaration som du får från oss har vi räknat ut vad du ska betala
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eller få tillbaka.
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- Tryck på knappen för att få tala med kundsupporten.
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example_title: Skatteverket FAQ
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- source_sentence: Hon kunde göra bakåtvolter.
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sentences:
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- Hon var atletisk.
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- Hon var bra på gymnastik.
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- Hon var inte atletisk.
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- Hon var oförmögen att flippa baklänges.
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example_title: Gymnastik
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license: apache-2.0
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language:
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- sv
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---
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# KBLab/sentence-bert-swedish-cased
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps Swedish sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a bilingual Swedish-English model trained according to instructions in the paper [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/pdf/2004.09813.pdf) and the [documentation](https://www.sbert.net/examples/training/multilingual/README.html) accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder ([all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)) as a teacher model, and the pretrained Swedish [KB-BERT](https://huggingface.co/KB/bert-base-swedish-cased) as the student model.
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A more detailed description of the model can be found in an article we published on the KBLab blog [here](https://kb-labb.github.io/posts/2021-08-23-a-swedish-sentence-transformer/) and for the updated model [here](https://kb-labb.github.io/posts/2023-01-16-sentence-transformer-20/).
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**Update**: We have released updated versions of the model since the initial release. The original model described in the blog post is **v1.0**. The current version is **v2.0**. The newer versions are trained on longer paragraphs, and have a longer max sequence length. **v2.0** is trained with a stronger teacher model and is the current default.
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| Model version | Teacher Model | Max Sequence Length |
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|---------------|---------|----------|
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| v1.0 | [paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) | 256 |
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| v1.1 | [paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) | 384 |
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| v2.0 | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 384 |
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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["Det här är en exempelmening", "Varje exempel blir konverterad"]
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model = SentenceTransformer('KBLab/sentence-bert-swedish-cased')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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### Loading an older model version (Sentence-Transformers)
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Currently, the easiest way to load an older model version is to clone the model repository and load it from disk. For example, to clone the **v1.0** model:
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```bash
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git clone --depth 1 --branch v1.0 https://huggingface.co/KBLab/sentence-bert-swedish-cased
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```
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Then you can load the model by pointing to the local folder where you cloned the model:
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("path_to_model_folder/sentence-bert-swedish-cased")
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```
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## Usage (HuggingFace Transformers)
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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.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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sentences = ['Det här är en exempelmening', 'Varje exempel blir konverterad']
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# Load model from HuggingFace Hub
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# To load an older version, e.g. v1.0, add the argument revision="v1.0"
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tokenizer = AutoTokenizer.from_pretrained('KBLab/sentence-bert-swedish-cased')
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model = AutoModel.from_pretrained('KBLab/sentence-bert-swedish-cased')
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, max pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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### Loading an older model (Hugginfface Transformers)
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To load an older model specify the version tag with the `revision` arg. For example, to load the **v1.0** model, use the following code:
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```python
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AutoTokenizer.from_pretrained('KBLab/sentence-bert-swedish-cased', revision="v1.0")
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AutoModel.from_pretrained('KBLab/sentence-bert-swedish-cased', revision="v1.0")
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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The model was evaluated on [SweParaphrase v1.0](https://spraakbanken.gu.se/en/resources/sweparaphrase) and **SweParaphrase v2.0**. This test set is part of [SuperLim](https://spraakbanken.gu.se/en/resources/superlim) -- a Swedish evaluation suite for natural langage understanding tasks. We calculated Pearson and Spearman correlation between predicted model similarity scores and the human similarity score labels. Results from **SweParaphrase v1.0** are displayed below.
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| Model version | Pearson | Spearman |
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|---------------|---------|----------|
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| v1.0 | 0.9183 | 0.9114 |
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| v1.1 | 0.9183 | 0.9114 |
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| v2.0 | **0.9283** | **0.9130** |
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The following code snippet can be used to reproduce the above results:
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```python
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from sentence_transformers import SentenceTransformer
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import pandas as pd
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df = pd.read_csv(
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"sweparaphrase-dev-165.csv",
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sep="\t",
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header=None,
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names=[
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"original_id",
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"source",
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"type",
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"sentence_swe1",
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"sentence_swe2",
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"score",
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"sentence1",
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"sentence2",
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],
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)
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model = SentenceTransformer("KBLab/sentence-bert-swedish-cased")
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sentences1 = df["sentence_swe1"].tolist()
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sentences2 = df["sentence_swe2"].tolist()
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# Compute embedding for both lists
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embeddings1 = model.encode(sentences1, convert_to_tensor=True)
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embeddings2 = model.encode(sentences2, convert_to_tensor=True)
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# Compute cosine similarity after normalizing
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embeddings1 /= embeddings1.norm(dim=-1, keepdim=True)
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embeddings2 /= embeddings2.norm(dim=-1, keepdim=True)
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cosine_scores = embeddings1 @ embeddings2.t()
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sentence_pair_scores = cosine_scores.diag()
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df["model_score"] = sentence_pair_scores.cpu().tolist()
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print(df[["score", "model_score"]].corr(method="spearman"))
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print(df[["score", "model_score"]].corr(method="pearson"))
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```
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### Sweparaphrase v2.0
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In general, **v1.1** correlates the most with human assessment of text similarity on SweParaphrase v2.0. Below, we present zero-shot evaluation results on all data splits. They display the model's performance out of the box, without any fine-tuning.
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| Model version | Data split | Pearson | Spearman |
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|---------------|------------|------------|------------|
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| v1.0 | train | 0.8355 | 0.8256 |
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| v1.1 | train | **0.8383** | **0.8302** |
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| v2.0 | train | 0.8209 | 0.8059 |
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| v1.0 | dev | 0.8682 | 0.8774 |
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| v1.1 | dev | **0.8739** | **0.8833** |
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| v2.0 | dev | 0.8638 | 0.8668 |
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| v1.0 | test | 0.8356 | 0.8476 |
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| v1.1 | test | **0.8393** | **0.8550** |
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| v2.0 | test | 0.8232 | 0.8213 |
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### SweFAQ v2.0
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When it comes to retrieval tasks, **v2.0** performs the best by quite a substantial margin. It is better at matching the correct answer to a question compared to v1.1 and v1.0.
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| Model version | Data split | Accuracy |
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|---------------|------------|------------|
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| v1.0 | train | 0.5262 |
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| v1.1 | train | 0.6236 |
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| v2.0 | train | **0.7106** |
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| v1.0 | dev | 0.4636 |
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| v1.1 | dev | 0.5818 |
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| v2.0 | dev | **0.6727** |
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| v1.0 | test | 0.4495 |
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| v1.1 | test | 0.5229 |
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| v2.0 | test | **0.5871** |
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Examples how to evaluate the models on some of the test sets of the SuperLim suites can be found on the following links: [evaluate_faq.py](https://github.com/kb-labb/swedish-sbert/blob/main/evaluate_faq.py) (Swedish FAQ), [evaluate_swesat.py](https://github.com/kb-labb/swedish-sbert/blob/main/evaluate_swesat.py) (SweSAT synonyms), [evaluate_supersim.py](https://github.com/kb-labb/swedish-sbert/blob/main/evaluate_supersim.py) (SuperSim).
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## Training
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An article with more details on data and v1.0 of the model can be found on the [KBLab blog](https://kb-labb.github.io/posts/2021-08-23-a-swedish-sentence-transformer/).
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Around 14.6 million sentences from English-Swedish parallel corpuses were used to train the model. Data was sourced from the [Open Parallel Corpus](https://opus.nlpl.eu/) (OPUS) and downloaded via the python package [opustools](https://pypi.org/project/opustools/). Datasets used were: JW300, Europarl, DGT-TM, EMEA, ELITR-ECA, TED2020, Tatoeba and OpenSubtitles.
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 180513 with parameters:
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```
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{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.MSELoss.MSELoss`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 2,
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"evaluation_steps": 1000,
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
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"optimizer_params": {
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"eps": 1e-06,
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"lr": 8e-06
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 5000,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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This model was trained by KBLab, a data lab at the National Library of Sweden.
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You can cite the article on our blog: https://kb-labb.github.io/posts/2021-08-23-a-swedish-sentence-transformer/ .
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```
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@misc{rekathati2021introducing,
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author = {Rekathati, Faton},
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title = {The KBLab Blog: Introducing a Swedish Sentence Transformer},
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url = {https://kb-labb.github.io/posts/2021-08-23-a-swedish-sentence-transformer/},
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year = {2021}
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}
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```
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## Acknowledgements
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We gratefully acknowledge the HPC RIVR consortium ([www.hpc-rivr.si](https://www.hpc-rivr.si/)) and EuroHPC JU ([eurohpc-ju.europa.eu/](https://eurohpc-ju.europa.eu/)) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science ([www.izum.si](https://www.izum.si/)).
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