| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:33054 |
| | - loss:MultipleNegativesRankingLoss |
| | base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| | widget: |
| | - source_sentence: Het communicatiesysteem of de belplaat |
| | sentences: |
| | - Nee, het is een ander soort probleem |
| | - Sluiting |
| | - Alle lampen op de loopbruggen zijn kapot |
| | - source_sentence: Appartement |
| | sentences: |
| | - De bewoners van de bovenliggende woning |
| | - trap |
| | - Onveilig |
| | - source_sentence: afzuiging |
| | sentences: |
| | - lucht afvoer |
| | - weg rijden |
| | - Verloren |
| | - source_sentence: Buis |
| | sentences: |
| | - Zelf |
| | - De verlichting van alle loopbruggen werkt niet |
| | - Klem |
| | - source_sentence: De verlichting van de gang werkt niet |
| | sentences: |
| | - Donker |
| | - Het raamglas is gebroken |
| | - Vast |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - cosine_accuracy |
| | - cosine_accuracy_threshold |
| | - cosine_f1 |
| | - cosine_f1_threshold |
| | - cosine_precision |
| | - cosine_recall |
| | - cosine_ap |
| | - cosine_mcc |
| | model-index: |
| | - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| | results: |
| | - task: |
| | type: binary-classification |
| | name: Binary Classification |
| | dataset: |
| | name: Unknown |
| | type: unknown |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.9692433315187806 |
| | name: Cosine Accuracy |
| | - type: cosine_accuracy_threshold |
| | value: 0.7461047172546387 |
| | name: Cosine Accuracy Threshold |
| | - type: cosine_f1 |
| | value: 0.9689816085643701 |
| | name: Cosine F1 |
| | - type: cosine_f1_threshold |
| | value: 0.7461047172546387 |
| | name: Cosine F1 Threshold |
| | - type: cosine_precision |
| | value: 0.9772978959025471 |
| | name: Cosine Precision |
| | - type: cosine_recall |
| | value: 0.9608056614044638 |
| | name: Cosine Recall |
| | - type: cosine_ap |
| | value: 0.9924589219952974 |
| | name: Cosine Ap |
| | - type: cosine_mcc |
| | value: 0.9386203213441777 |
| | name: Cosine Mcc |
| | --- |
| | |
| | # 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 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d --> |
| | - **Maximum Sequence Length:** 64 tokens |
| | - **Output Dimensionality:** 384 dimensions |
| | - **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': 64, '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("PrabalAryal/Sentence_Transformer_v0.0.3") |
| | # Run inference |
| | sentences = [ |
| | 'De verlichting van de gang werkt niet', |
| | 'Donker', |
| | 'Het raamglas is gebroken', |
| | ] |
| | 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 |
| |
|
| | #### Binary Classification |
| |
|
| | * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------------|:-----------| |
| | | cosine_accuracy | 0.9692 | |
| | | cosine_accuracy_threshold | 0.7461 | |
| | | cosine_f1 | 0.969 | |
| | | cosine_f1_threshold | 0.7461 | |
| | | cosine_precision | 0.9773 | |
| | | cosine_recall | 0.9608 | |
| | | **cosine_ap** | **0.9925** | |
| | | cosine_mcc | 0.9386 | |
| | |
| | <!-- |
| | ## 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 Dataset |
| | |
| | #### Unnamed Dataset |
| | |
| | * Size: 33,054 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: 7.39 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.78 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | label | |
| | |:---------------------------------------------------------|:-----------------------------|:-----------------| |
| | | <code>Defect</code> | <code>Lek</code> | <code>1.0</code> | |
| | | <code>Dakbedekking</code> | <code>Weggewaaid</code> | <code>1.0</code> | |
| | | <code>Het slot werkt niet, de deur is geblokkeerd</code> | <code>druppende kraan</code> | <code>0.0</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" |
| | } |
| | ``` |
| | |
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| | |
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 64 |
| | - `per_device_eval_batch_size`: 64 |
| | - `num_train_epochs`: 10 |
| | - `fp16`: True |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 64 |
| | - `per_device_eval_batch_size`: 64 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_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`: 10 |
| | - `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`: True |
| | - `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`: None |
| | - `hub_always_push`: False |
| | - `hub_revision`: None |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `include_for_metrics`: [] |
| | - `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 |
| | - `include_tokens_per_second`: False |
| | - `include_num_input_tokens_seen`: False |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `use_liger_kernel`: False |
| | - `liger_kernel_config`: None |
| | - `eval_use_gather_object`: False |
| | - `average_tokens_across_devices`: False |
| | - `prompts`: None |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | Training Loss | cosine_ap | |
| | |:------:|:----:|:-------------:|:---------:| |
| | | 0.1992 | 103 | - | 0.7450 | |
| | | 0.3985 | 206 | - | 0.8186 | |
| | | 0.5977 | 309 | - | 0.8716 | |
| | | 0.7969 | 412 | - | 0.9074 | |
| | | 0.9671 | 500 | 4.302 | - | |
| | | 0.9961 | 515 | - | 0.9307 | |
| | | 1.0 | 517 | - | 0.9315 | |
| | | 1.1954 | 618 | - | 0.9434 | |
| | | 1.3946 | 721 | - | 0.9555 | |
| | | 1.5938 | 824 | - | 0.9637 | |
| | | 1.7930 | 927 | - | 0.9676 | |
| | | 1.9342 | 1000 | 3.5031 | - | |
| | | 1.9923 | 1030 | - | 0.9725 | |
| | | 2.0 | 1034 | - | 0.9731 | |
| | | 2.1915 | 1133 | - | 0.9741 | |
| | | 2.3907 | 1236 | - | 0.9753 | |
| | | 2.5899 | 1339 | - | 0.9802 | |
| | | 2.7892 | 1442 | - | 0.9806 | |
| | | 2.9014 | 1500 | 3.2728 | - | |
| | | 2.9884 | 1545 | - | 0.9843 | |
| | | 3.0 | 1551 | - | 0.9846 | |
| | | 3.1876 | 1648 | - | 0.9839 | |
| | | 3.3868 | 1751 | - | 0.9844 | |
| | | 3.5861 | 1854 | - | 0.9847 | |
| | | 3.7853 | 1957 | - | 0.9868 | |
| | | 3.8685 | 2000 | 3.1567 | - | |
| | | 3.9845 | 2060 | - | 0.9882 | |
| | | 4.0 | 2068 | - | 0.9876 | |
| | | 4.1838 | 2163 | - | 0.9880 | |
| | | 4.3830 | 2266 | - | 0.9880 | |
| | | 4.5822 | 2369 | - | 0.9887 | |
| | | 4.7814 | 2472 | - | 0.9887 | |
| | | 4.8356 | 2500 | 3.0525 | - | |
| | | 4.9807 | 2575 | - | 0.9899 | |
| | | 5.0 | 2585 | - | 0.9901 | |
| | | 5.1799 | 2678 | - | 0.9896 | |
| | | 5.3791 | 2781 | - | 0.9894 | |
| | | 5.5783 | 2884 | - | 0.9904 | |
| | | 5.7776 | 2987 | - | 0.9906 | |
| | | 5.8027 | 3000 | 3.0061 | - | |
| | | 5.9768 | 3090 | - | 0.9910 | |
| | | 6.0 | 3102 | - | 0.9911 | |
| | | 6.1760 | 3193 | - | 0.9904 | |
| | | 6.3752 | 3296 | - | 0.9907 | |
| | | 6.5745 | 3399 | - | 0.9915 | |
| | | 6.7698 | 3500 | 2.9548 | - | |
| | | 6.7737 | 3502 | - | 0.9915 | |
| | | 6.9729 | 3605 | - | 0.9917 | |
| | | 7.0 | 3619 | - | 0.9917 | |
| | | 7.1721 | 3708 | - | 0.9912 | |
| | | 7.3714 | 3811 | - | 0.9915 | |
| | | 7.5706 | 3914 | - | 0.9916 | |
| | | 7.7369 | 4000 | 2.9023 | - | |
| | | 7.7698 | 4017 | - | 0.9917 | |
| | | 7.9691 | 4120 | - | 0.9919 | |
| | | 8.0 | 4136 | - | 0.9921 | |
| | | 8.1683 | 4223 | - | 0.9919 | |
| | | 8.3675 | 4326 | - | 0.9919 | |
| | | 8.5667 | 4429 | - | 0.9925 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.11.13 |
| | - Sentence Transformers: 4.1.0 |
| | - Transformers: 4.53.3 |
| | - PyTorch: 2.6.0+cu124 |
| | - Accelerate: 1.9.0 |
| | - Datasets: 4.4.1 |
| | - Tokenizers: 0.21.2 |
| | |
| | ## 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", |
| | } |
| | ``` |
| | |
| | #### 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} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## 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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