---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:24000
- loss:TripletLoss
widget:
- source_sentence: 'query: Bytte regulator varmekabler'
sentences:
- 'query: legge varmekabler i takrenner i sameie'
- 'query: Garasjeport'
- 'query: Skriftlig vurdering av fuktskade/vannskade i sokkeleilighet.'
- source_sentence: 'query: Opprette hybler i enebolig.'
sentences:
- 'query: Helrenovering av bad 2,4 m^2 og toalettrom'
- 'query: Innvendig paneling av hytte på Budor'
- 'query: Vurdere muligheter for lading av elbil/hybrid'
- source_sentence: 'query: Mikrosement'
sentences:
- 'query: Legge plater med sløyfer til vannbåren varme 45 m2'
- 'query: Mikrosement på bad'
- 'query: * Fortsatt ledig: Spraylakkere 4 spisestuestoler'
- source_sentence: 'query: Ny hage til nytt hus ca 400 kvm'
sentences:
- 'query: Nytt lag med singel i innkjørsel'
- 'query: Skifte bordkledning'
- 'query: Reparere murtrapp IG legge skiferstein'
- source_sentence: 'query: Betongskjæring'
sentences:
- 'query: * Fortsatt ledig: Membran legging'
- 'query: Drenering av hus'
- 'query: Saging av hull til vindu'
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("ostoveland/test4")
# Run inference
sentences = [
'query: Betongskjæring',
'query: Saging av hull til vindu',
'query: Drenering av hus',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 24,000 training samples
* Columns: sentence_0
, sentence_1
, and sentence_2
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details |
query: Installere radonsug/radonvifte i kjeller
| query: Radon sikring enebolig
| query: Mikrosement på bad
|
| query: Bytte nedre del av en takrenne i klassisk bygård (fra 2. etasje)
| query: Pipebeslag
| query: Riving av bad
|
| query: Gjerde
| query: Flettverkgjerde 65 m
| query: glassplate til salongbord
|
* Loss: [TripletLoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters