--- 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](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) - **Maximum Sequence Length:** 75 tokens - **Output Dimensionality:** 768 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': 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] ``` ## Evaluation ### Metrics #### Triplet * Dataset: `test-triplet-evaluation` * Evaluated with [TripletEvaluator](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** | ## 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 | | | | * 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](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: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * 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](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: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------| | type | string | string | float | | details | | | | * 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](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
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 ```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}, } ```