--- pipeline_tag: sentence-similarity lang: - sv tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers widget: - source_sentence: Mannen åt mat. sentences: - Han förtärde en närande och nyttig måltid. - Det var ett sunkigt hak med ganska gott käk. - Han inmundigade middagen tillsammans med ett glas rödvin. - Potatischips är jättegoda. - Tryck på knappen för att få tala med kundsupporten. example_title: Mat - source_sentence: Kan jag deklarera digitalt från utlandet? sentences: - Du som befinner dig i utlandet kan deklarera digitalt på flera olika sätt. - >- Du som har kvarskatt att betala ska göra en inbetalning till ditt skattekonto. - >- Efter att du har deklarerat går vi igenom uppgifterna i din deklaration och räknar ut din skatt. - >- I din deklaration som du får från oss har vi räknat ut vad du ska betala eller få tillbaka. - Tryck på knappen för att få tala med kundsupporten. example_title: Skatteverket FAQ - source_sentence: Hon kunde göra bakåtvolter. sentences: - Hon var atletisk. - Hon var bra på gymnastik. - Hon var inte atletisk. - Hon var oförmögen att flippa baklänges. example_title: Gymnastik license: apache-2.0 language: - sv --- # KBLab/sentence-bert-swedish-cased 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. 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/). **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. | Model version | Teacher Model | Max Sequence Length | |---------------|---------|----------| | v1.0 | [paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) | 256 | | v1.1 | [paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) | 384 | | v2.0 | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 384 | ## 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 = ["Det här är en exempelmening", "Varje exempel blir konverterad"] model = SentenceTransformer('KBLab/sentence-bert-swedish-cased') embeddings = model.encode(sentences) print(embeddings) ``` ### Loading an older model version (Sentence-Transformers) 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: ```bash git clone --depth 1 --branch v1.0 https://huggingface.co/KBLab/sentence-bert-swedish-cased ``` Then you can load the model by pointing to the local folder where you cloned the model: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("path_to_model_folder/sentence-bert-swedish-cased") ``` ## 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 = ['Det här är en exempelmening', 'Varje exempel blir konverterad'] # Load model from HuggingFace Hub # To load an older version, e.g. v1.0, add the argument revision="v1.0" tokenizer = AutoTokenizer.from_pretrained('KBLab/sentence-bert-swedish-cased') model = AutoModel.from_pretrained('KBLab/sentence-bert-swedish-cased') # 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, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ### Loading an older model (Hugginfface Transformers) 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: ```python AutoTokenizer.from_pretrained('KBLab/sentence-bert-swedish-cased', revision="v1.0") AutoModel.from_pretrained('KBLab/sentence-bert-swedish-cased', revision="v1.0") ``` ## Evaluation Results 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. | Model version | Pearson | Spearman | |---------------|---------|----------| | v1.0 | 0.9183 | 0.9114 | | v1.1 | 0.9183 | 0.9114 | | v2.0 | **0.9283** | **0.9130** | The following code snippet can be used to reproduce the above results: ```python from sentence_transformers import SentenceTransformer import pandas as pd df = pd.read_csv( "sweparaphrase-dev-165.csv", sep="\t", header=None, names=[ "original_id", "source", "type", "sentence_swe1", "sentence_swe2", "score", "sentence1", "sentence2", ], ) model = SentenceTransformer("KBLab/sentence-bert-swedish-cased") sentences1 = df["sentence_swe1"].tolist() sentences2 = df["sentence_swe2"].tolist() # Compute embedding for both lists embeddings1 = model.encode(sentences1, convert_to_tensor=True) embeddings2 = model.encode(sentences2, convert_to_tensor=True) # Compute cosine similarity after normalizing embeddings1 /= embeddings1.norm(dim=-1, keepdim=True) embeddings2 /= embeddings2.norm(dim=-1, keepdim=True) cosine_scores = embeddings1 @ embeddings2.t() sentence_pair_scores = cosine_scores.diag() df["model_score"] = sentence_pair_scores.cpu().tolist() print(df[["score", "model_score"]].corr(method="spearman")) print(df[["score", "model_score"]].corr(method="pearson")) ``` ### Sweparaphrase v2.0 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. | Model version | Data split | Pearson | Spearman | |---------------|------------|------------|------------| | v1.0 | train | 0.8355 | 0.8256 | | v1.1 | train | **0.8383** | **0.8302** | | v2.0 | train | 0.8209 | 0.8059 | | v1.0 | dev | 0.8682 | 0.8774 | | v1.1 | dev | **0.8739** | **0.8833** | | v2.0 | dev | 0.8638 | 0.8668 | | v1.0 | test | 0.8356 | 0.8476 | | v1.1 | test | **0.8393** | **0.8550** | | v2.0 | test | 0.8232 | 0.8213 | ### SweFAQ v2.0 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. | Model version | Data split | Accuracy | |---------------|------------|------------| | v1.0 | train | 0.5262 | | v1.1 | train | 0.6236 | | v2.0 | train | **0.7106** | | v1.0 | dev | 0.4636 | | v1.1 | dev | 0.5818 | | v2.0 | dev | **0.6727** | | v1.0 | test | 0.4495 | | v1.1 | test | 0.5229 | | v2.0 | test | **0.5871** | 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). ## Training 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/). 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. The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 180513 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MSELoss.MSELoss` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "", "optimizer_params": { "eps": 1e-06, "lr": 8e-06 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 5000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, '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 This model was trained by KBLab, a data lab at the National Library of Sweden. You can cite the article on our blog: https://kb-labb.github.io/posts/2021-08-23-a-swedish-sentence-transformer/ . ``` @misc{rekathati2021introducing, author = {Rekathati, Faton}, title = {The KBLab Blog: Introducing a Swedish Sentence Transformer}, url = {https://kb-labb.github.io/posts/2021-08-23-a-swedish-sentence-transformer/}, year = {2021} } ``` ## Acknowledgements 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/)).