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---
base_model: NbAiLab/nb-sbert-base
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:96724
- loss:TripletLoss
- loss:MultipleNegativesRankingLoss
- loss:CoSENTLoss
widget:
- source_sentence: Fjerne 60 cm snø fra enebolig på 100 kvadratmeter
sentences:
- 'query: montere solskjerming inne'
- 'query: 150 meter grøfting'
- 'query: Snømåking på enebolig, 100 kvadratmeter'
- source_sentence: Renovering av bad
sentences:
- Asfaltere innkjørsel
- Nye garasjeporter m/åpner
- Totalrenovering av lite bad i Lillestrøm
- source_sentence: Lite tilbygg til eksisterende bolig
sentences:
- Renovere bolig
- Vi skal pusse opp kjøkken
- Bygge tilbygg
- source_sentence: Gulvlegging 6 kvm gang
sentences:
- Installere gulvvarme
- Montering av 8 spotlights brannsikre (4stk. på kjøket) og (2 stk i gangen)
- Legge parkett i gang
- source_sentence: Fullføre utvendig forefallent arbeid
sentences:
- Bytte av vinduer i hus
- elektriker på bolig på 120kvm
- Renovere bad
model-index:
- name: SentenceTransformer based on NbAiLab/nb-sbert-base
results:
- task:
type: triplet
name: Triplet
dataset:
name: test triplet evaluation
type: test-triplet-evaluation
metrics:
- type: cosine_accuracy
value: 0.9859055673009162
name: Cosine Accuracy
- type: dot_accuracy
value: 0.016913319238900635
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.9844961240310077
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.9837914023960536
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9859055673009162
name: Max Accuracy
---
# SentenceTransformer based on NbAiLab/nb-sbert-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base). It maps sentences & paragraphs to a 768-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:** [NbAiLab/nb-sbert-base](https://huggingface.co/NbAiLab/nb-sbert-base) <!-- at revision 56ae460305b0787432b6498e5adc17447e66fe66 -->
- **Maximum Sequence Length:** 75 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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': 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, '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/SBertBaseMittanbudver1")
# Run inference
sentences = [
'Fullføre utvendig forefallent arbeid',
'elektriker på bolig på 120kvm',
'Renovere bad',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## Evaluation
### Metrics
#### Triplet
* Dataset: `test-triplet-evaluation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy | 0.9859 |
| dot_accuracy | 0.0169 |
| manhattan_accuracy | 0.9845 |
| euclidean_accuracy | 0.9838 |
| **max_accuracy** | **0.9859** |
<!--
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## Training Details
### Training Datasets
#### Unnamed Dataset
* Size: 55,426 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 11.65 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.92 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.49 tokens</li><li>max: 35 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:----------------------------------------|:------------------------------------------|:-----------------------------------------------------------------|
| <code>Bygge støttemur</code> | <code>Støttemur</code> | <code>Bytte lås på dörr</code> |
| <code>Understell bord i stål</code> | <code>Lage stålunderstell til bord</code> | <code>Bygge trebord</code> |
| <code>Reparasjon vannbåren varme</code> | <code>Vannbåren varme til enebolig</code> | <code>* Fortsatt ledig: ombygning av eksisterende kjeller</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
#### Unnamed Dataset
* Size: 22,563 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 11.09 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.94 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------|:----------------------------------------|
| <code>utforing av gavlvegg</code> | <code>query: utforing av vegg</code> |
| <code>Montere kjøkken</code> | <code>query: kjøkkenmontering</code> |
| <code>Sette opp lettvegg med skyvedør, bygge bod i carport, forlenge tak på carport</code> | <code>query: bygge bod i carport</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### Unnamed Dataset
* Size: 18,735 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 13.08 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.52 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.05</li><li>mean: 0.51</li><li>max: 0.95</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------|:-------------------------------------------|:------------------|
| <code>Renovering av hus - plantegninger og fasade</code> | <code>elektriker på bolig på 120kvm</code> | <code>0.15</code> |
| <code>Blending av innvendig dør</code> | <code>Tette igjen døråpning</code> | <code>0.75</code> |
| <code>Fortsatt ledig: Kappe teglstein på pipeløp</code> | <code>Murearbeid</code> | <code>0.45</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 6
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 6
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss | test-triplet-evaluation_max_accuracy |
|:------:|:-----:|:-------------:|:------------------------------------:|
| 0.2844 | 500 | 3.6092 | - |
| 0.5688 | 1000 | 2.9852 | - |
| 0.8532 | 1500 | 2.7542 | - |
| 1.0011 | 1760 | - | 0.9831 |
| 1.1365 | 2000 | 2.5467 | - |
| 1.4209 | 2500 | 2.3263 | - |
| 1.7053 | 3000 | 2.2608 | - |
| 1.9898 | 3500 | 2.2042 | - |
| 2.0011 | 3520 | - | 0.9859 |
| 2.2730 | 4000 | 2.1615 | - |
| 2.5575 | 4500 | 2.0934 | - |
| 2.8419 | 5000 | 2.1226 | - |
| 3.0011 | 5280 | - | 0.9859 |
| 3.1251 | 5500 | 2.1977 | - |
| 3.4096 | 6000 | 2.1209 | - |
| 3.6940 | 6500 | 2.1006 | - |
| 3.9784 | 7000 | 2.1495 | - |
| 4.0011 | 7040 | - | 0.9859 |
| 4.2617 | 7500 | 2.1792 | - |
| 4.5461 | 8000 | 2.0958 | - |
| 4.8305 | 8500 | 2.1065 | - |
| 5.0011 | 8800 | - | 0.9859 |
| 5.1138 | 9000 | 2.1762 | - |
| 5.3982 | 9500 | 2.1347 | - |
| 5.6826 | 10000 | 2.1198 | - |
| 5.9670 | 10500 | 2.1251 | - |
| 5.9943 | 10548 | - | 0.9859 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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