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Model: KBLab/sentence-bert-swedish-cased
Source: Original Platform

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+  "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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README.md

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+---
+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 |
+
+<!--- Describe your model here -->
+
+## 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
+
+<!--- Describe how your model was evaluated -->
+
+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": "<class 'torch.optim.adamw.AdamW'>",
+    "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
+
+<!--- Describe where people can find more information -->
+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/)).

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