metadata

base_model: NbAiLab/nb-sbert-base
language:
  - 'no'
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 model finetuned from 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
Maximum Sequence Length: 75 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Evaluation

Metrics

Triplet

Dataset: test-triplet-evaluation
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9859
dot_accuracy	0.0169
manhattan_accuracy	0.9845
euclidean_accuracy	0.9838
max_accuracy	0.9859

Training Details

Training Datasets

Unnamed Dataset

Size: 55,426 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	min: 3 tokens mean: 11.65 tokens max: 44 tokens	min: 4 tokens mean: 10.92 tokens max: 31 tokens	min: 3 tokens mean: 10.49 tokens max: 35 tokens

Samples:

sentence_0	sentence_1	sentence_2
`Bygge støttemur`	`Støttemur`	`Bytte lås på dörr`
`Understell bord i stål`	`Lage stålunderstell til bord`	`Bygge trebord`
`Reparasjon vannbåren varme`	`Vannbåren varme til enebolig`	`* Fortsatt ledig: ombygning av eksisterende kjeller`

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Unnamed Dataset

Size: 22,563 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 4 tokens
mean: 11.09 tokens
max: 37 tokens

min: 8 tokens
mean: 12.94 tokens
max: 30 tokens

	sentence_0	sentence_1
type	string	string
details	min: 4 tokens mean: 11.09 tokens max: 37 tokens	min: 8 tokens mean: 12.94 tokens max: 30 tokens

Samples:

sentence_0	sentence_1
`utforing av gavlvegg`	`query: utforing av vegg`
`Montere kjøkken`	`query: kjøkkenmontering`
`Sette opp lettvegg med skyvedør, bygge bod i carport, forlenge tak på carport`	`query: bygge bod i carport`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Unnamed Dataset

Size: 18,735 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 3 tokens
mean: 13.08 tokens
max: 46 tokens

min: 4 tokens
mean: 9.52 tokens
max: 27 tokens

min: 0.05
mean: 0.51
max: 0.95

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 13.08 tokens max: 46 tokens	min: 4 tokens mean: 9.52 tokens max: 27 tokens	min: 0.05 mean: 0.51 max: 0.95

Samples:

sentence_0	sentence_1	label
`Renovering av hus - plantegninger og fasade`	`elektriker på bolig på 120kvm`	`0.15`
`Blending av innvendig dør`	`Tette igjen døråpning`	`0.75`
`Fortsatt ledig: Kappe teglstein på pipeløp`	`Murearbeid`	`0.45`

Loss: CoSENTLoss with these parameters:

{
    "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

Click to expand

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

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

@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

@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

@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

@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},
}