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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  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  bolig  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

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

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
  • 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
  • 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},
}