| --- |
| base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| 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:2400 |
| - loss:TripletLoss |
| - loss:MultipleNegativesRankingLoss |
| - loss:CoSENTLoss |
| widget: |
| - source_sentence: Flislegging av hall |
| sentences: |
| - 'query: tapetsering av rom med grunnflate 4x4.5 meter minus tre dører' |
| - 'query: fliser i hall' |
| - 'query: fornye markiseduk' |
| - source_sentence: Betongskjæring av rømningsvindu |
| sentences: |
| - Installere ventilasjonssystem |
| - Installere nytt vindu i trevegg |
| - Skjære ut rømningsvindu i betongvegg |
| - source_sentence: Ny garasje leddport |
| sentences: |
| - Installere garasjeport |
| - Bygge ny garasje |
| - Legge nytt tak |
| - source_sentence: Legge varmefolie i gang og stue. |
| sentences: |
| - Strø grusveier med salt |
| - Legge varmekabler |
| - Installere gulvvarme |
| - source_sentence: Oppgradere kjeller til boareale |
| sentences: |
| - Oppussing av kjeller for boligformål |
| - elektriker på bolig på 120kvm |
| - Installere dusjkabinett |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| results: |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: test triplet evaluation |
| type: test-triplet-evaluation |
| metrics: |
| - type: cosine_accuracy |
| value: 0.7470049330514447 |
| name: Cosine Accuracy |
| - type: dot_accuracy |
| value: 0.31853417899929526 |
| name: Dot Accuracy |
| - type: manhattan_accuracy |
| value: 0.740662438336857 |
| name: Manhattan Accuracy |
| - type: euclidean_accuracy |
| value: 0.7420718816067653 |
| name: Euclidean Accuracy |
| - type: max_accuracy |
| value: 0.7470049330514447 |
| name: Max Accuracy |
| --- |
| |
| # 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) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 384 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': 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/test3") |
| # Run inference |
| sentences = [ |
| 'Oppgradere kjeller til boareale', |
| 'Oppussing av kjeller for boligformål', |
| 'Installere dusjkabinett', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## 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 | 0.747 | |
| | dot_accuracy | 0.3185 | |
| | manhattan_accuracy | 0.7407 | |
| | euclidean_accuracy | 0.7421 | |
| | **max_accuracy** | **0.747** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Datasets |
| |
| #### Unnamed Dataset |
| |
| |
| * Size: 800 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: 9.91 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.87 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.14 tokens</li><li>max: 31 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | sentence_2 | |
| |:----------------------------------------------|:-------------------------------------------|:------------------------------------------| |
| | <code>Oppussing av stue</code> | <code>Renovere stue</code> | <code>Male stue</code> | |
| | <code>Sameie søker vaktmestertjenester</code> | <code>Trenger vaktmester til sameie</code> | <code>Renholdstjenester for sameie</code> | |
| | <code>Sprenge og klargjøre til garasje</code> | <code>Grave ut til garasje</code> | <code>Bygge garasje</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: 800 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: 10.36 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.36 tokens</li><li>max: 26 tokens</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | |
| |:------------------------------------------------------------------------|:---------------------------------------------------------------------| |
| | <code>Helsparkle rom med totale veggflater på ca 20 m2</code> | <code>query: helsparkling av rom med 20 m2 veggflater</code> | |
| | <code>Reparere skifer tak og tak vindu</code> | <code>query: fikse takvindu og skifertak</code> | |
| | <code>Pigge opp flisgulv, fjerne gips vegger og gipstak - 11 kvm</code> | <code>query: fjerne flisgulv, gipsvegger og gipstak på 11 kvm</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: 800 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: 4 tokens</li><li>mean: 10.32 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.18 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 0.1</li><li>mean: 0.51</li><li>max: 0.95</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:--------------------------------------|:---------------------------------------------------|:------------------| |
| | <code>Legging av våtromsbelegg</code> | <code>Renovering av bad</code> | <code>0.65</code> | |
| | <code>overvåkingskamera 3stk</code> | <code>installasjon av 3 overvåkingskameraer</code> | <code>0.95</code> | |
| | <code>Bytte lamper i portrom</code> | <code>Male portrom</code> | <code>0.15</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`: 1 |
| - `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`: 1 |
| - `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 | test-triplet-evaluation_max_accuracy | |
| |:-----:|:----:|:------------------------------------:| |
| | 1.0 | 75 | 0.7470 | |
| |
| |
| ### 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}, |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
| <!-- |
| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
| |
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| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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