| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:68828 |
| | - loss:MultipleNegativesRankingLoss |
| | base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| | widget: |
| | - source_sentence: Men is de toegangssleutels verloren |
| | sentences: |
| | - De centrale verwarming |
| | - niet dringend |
| | - Weg |
| | - source_sentence: De bovenste constructie |
| | sentences: |
| | - Voldoende warm water in de hele woning |
| | - daklekkage |
| | - lek in kraan |
| | - source_sentence: De box in het souterrain |
| | sentences: |
| | - Vloer |
| | - lift niet |
| | - Nood uitgang |
| | - source_sentence: balkon |
| | sentences: |
| | - de brievenbus |
| | - uitgang garage dicht |
| | - afvoer de douche |
| | - source_sentence: De deur naar de kelderboxen is stuk |
| | sentences: |
| | - deur met dranger |
| | - De beugel om de plek vrij te houden |
| | - kelderboxen deur |
| | 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: 0.982086820083682 |
| | name: Cosine Accuracy |
| | - type: cosine_accuracy_threshold |
| | value: 0.733125627040863 |
| | name: Cosine Accuracy Threshold |
| | - type: cosine_f1 |
| | value: 0.9821498371335505 |
| | name: Cosine F1 |
| | - type: cosine_f1_threshold |
| | value: 0.733125627040863 |
| | name: Cosine F1 Threshold |
| | - type: cosine_precision |
| | value: 0.9787068293949623 |
| | name: Cosine Precision |
| | - type: cosine_recall |
| | value: 0.9856171548117155 |
| | name: Cosine Recall |
| | - type: cosine_ap |
| | value: 0.9972864020390366 |
| | name: Cosine Ap |
| | - type: cosine_mcc |
| | value: 0.964197674565882 |
| | 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_v0.0.4") |
| | # Run inference |
| | sentences = [ |
| | 'De deur naar de kelderboxen is stuk', |
| | 'kelderboxen deur', |
| | 'deur met dranger', |
| | ] |
| | 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 | 0.9821 | |
| | | cosine_accuracy_threshold | 0.7331 | |
| | | cosine_f1 | 0.9821 | |
| | | cosine_f1_threshold | 0.7331 | |
| | | cosine_precision | 0.9787 | |
| | | cosine_recall | 0.9856 | |
| | | **cosine_ap** | **0.9973** | |
| | | cosine_mcc | 0.9642 | |
| | |
| | <!-- |
| | ## 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: 68,828 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: 7.03 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.41 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | label | |
| | |:------------------------------------------------------|:-------------------------|:-----------------| |
| | | <code>De sluiting van de toegangspoort is stuk</code> | <code>slot defect</code> | <code>1.0</code> | |
| | | <code>Woning</code> | <code>trapafgang</code> | <code>0.0</code> | |
| | | <code>De sleutels zijn kwijt</code> | <code>Nie</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 | |
| | |:------:|:-----:|:-------------:|:---------:| |
| | | 0.1998 | 215 | - | 0.7638 | |
| | | 0.3996 | 430 | - | 0.8723 | |
| | | 0.4647 | 500 | 4.4585 | - | |
| | | 0.5994 | 645 | - | 0.9176 | |
| | | 0.7993 | 860 | - | 0.9475 | |
| | | 0.9294 | 1000 | 3.6015 | - | |
| | | 0.9991 | 1075 | - | 0.9595 | |
| | | 1.0 | 1076 | - | 0.9593 | |
| | | 1.1989 | 1290 | - | 0.9705 | |
| | | 1.3941 | 1500 | 3.3729 | - | |
| | | 1.3987 | 1505 | - | 0.9793 | |
| | | 1.5985 | 1720 | - | 0.9818 | |
| | | 1.7983 | 1935 | - | 0.9854 | |
| | | 1.8587 | 2000 | 3.2631 | - | |
| | | 1.9981 | 2150 | - | 0.9866 | |
| | | 2.0 | 2152 | - | 0.9866 | |
| | | 2.1980 | 2365 | - | 0.9890 | |
| | | 2.3234 | 2500 | 3.1295 | - | |
| | | 2.3978 | 2580 | - | 0.9884 | |
| | | 2.5976 | 2795 | - | 0.9916 | |
| | | 2.7881 | 3000 | 3.0907 | - | |
| | | 2.7974 | 3010 | - | 0.9916 | |
| | | 2.9972 | 3225 | - | 0.9922 | |
| | | 3.0 | 3228 | - | 0.9922 | |
| | | 3.1970 | 3440 | - | 0.9928 | |
| | | 3.2528 | 3500 | 3.0105 | - | |
| | | 3.3968 | 3655 | - | 0.9932 | |
| | | 3.5967 | 3870 | - | 0.9937 | |
| | | 3.7175 | 4000 | 2.977 | - | |
| | | 3.7965 | 4085 | - | 0.9939 | |
| | | 3.9963 | 4300 | - | 0.9944 | |
| | | 4.0 | 4304 | - | 0.9945 | |
| | | 4.1822 | 4500 | 2.9488 | - | |
| | | 4.1961 | 4515 | - | 0.9947 | |
| | | 4.3959 | 4730 | - | 0.9950 | |
| | | 4.5957 | 4945 | - | 0.9952 | |
| | | 4.6468 | 5000 | 2.914 | - | |
| | | 4.7955 | 5160 | - | 0.9954 | |
| | | 4.9954 | 5375 | - | 0.9956 | |
| | | 5.0 | 5380 | - | 0.9956 | |
| | | 5.1115 | 5500 | 2.8927 | - | |
| | | 5.1952 | 5590 | - | 0.9960 | |
| | | 5.3950 | 5805 | - | 0.9959 | |
| | | 5.5762 | 6000 | 2.8505 | - | |
| | | 5.5948 | 6020 | - | 0.9963 | |
| | | 5.7946 | 6235 | - | 0.9961 | |
| | | 5.9944 | 6450 | - | 0.9962 | |
| | | 6.0 | 6456 | - | 0.9962 | |
| | | 6.0409 | 6500 | 2.8462 | - | |
| | | 6.1942 | 6665 | - | 0.9963 | |
| | | 6.3941 | 6880 | - | 0.9965 | |
| | | 6.5056 | 7000 | 2.8024 | - | |
| | | 6.5939 | 7095 | - | 0.9967 | |
| | | 6.7937 | 7310 | - | 0.9969 | |
| | | 6.9703 | 7500 | 2.8184 | - | |
| | | 6.9935 | 7525 | - | 0.9968 | |
| | | 7.0 | 7532 | - | 0.9967 | |
| | | 7.1933 | 7740 | - | 0.9967 | |
| | | 7.3931 | 7955 | - | 0.9967 | |
| | | 7.4349 | 8000 | 2.7761 | - | |
| | | 7.5929 | 8170 | - | 0.9968 | |
| | | 7.7928 | 8385 | - | 0.9969 | |
| | | 7.8996 | 8500 | 2.7736 | - | |
| | | 7.9926 | 8600 | - | 0.9970 | |
| | | 8.0 | 8608 | - | 0.9971 | |
| | | 8.1924 | 8815 | - | 0.9972 | |
| | | 8.3643 | 9000 | 2.7627 | - | |
| | | 8.3922 | 9030 | - | 0.9970 | |
| | | 8.5920 | 9245 | - | 0.9972 | |
| | | 8.7918 | 9460 | - | 0.9972 | |
| | | 8.8290 | 9500 | 2.7604 | - | |
| | | 8.9916 | 9675 | - | 0.9972 | |
| | | 9.0 | 9684 | - | 0.9972 | |
| | | 9.1914 | 9890 | - | 0.9971 | |
| | | 9.2937 | 10000 | 2.7467 | - | |
| | | 9.3913 | 10105 | - | 0.9972 | |
| | | 9.5911 | 10320 | - | 0.9973 | |
| | | 9.7584 | 10500 | 2.7441 | - | |
| | | 9.7909 | 10535 | - | 0.9973 | |
| | |
| | |
| | ### 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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