--- base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 language: - hu library_name: sentence-transformers license: apache-2.0 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:857856 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Emberek várnak a lámpánál kerékpárral. sentences: - Az emberek piros lámpánál haladnak. - Az emberek a kerékpárjukon vannak. - Egy fekete kutya úszik a vízben egy teniszlabdával a szájában - source_sentence: A kutya a vízben van. sentences: - Két férfi takarítja a havat a tetőről, az egyik egy emelőben ül, a másik pedig a tetőn. - A macska a vízben van, és dühös. - Egy kutya van a vízben, a szájában egy faág. - source_sentence: A nő feketét visel. sentences: - Egy barna kutya fröcsköl, ahogy úszik a vízben. - Egy tetoválással rendelkező nő, aki fekete tank tetején néz a földre. - 'Egy kékbe öltözött nő intenzív arckifejezéssel üti a teniszlabdát. A képen:' - source_sentence: Az emberek alszanak. sentences: - Három ember beszélget egy városi utcán. - A nő fehéret visel. - Egy apa és a fia ölelgeti alvás közben. - source_sentence: Az emberek alszanak. sentences: - Egy feketébe öltözött nő cigarettát és bevásárlótáskát tart a kezében, miközben egy idősebb nő átmegy az utcán. - Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné. - Egy apa és a fia ölelgeti alvás közben. model-index: - name: paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: triplet name: Triplet dataset: name: all nli dev type: all-nli-dev metrics: - type: cosine_accuracy value: 0.992 name: Cosine Accuracy - type: dot_accuracy value: 0.0108 name: Dot Accuracy - type: manhattan_accuracy value: 0.9908 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9908 name: Euclidean Accuracy - type: max_accuracy value: 0.992 name: Max Accuracy - task: type: triplet name: Triplet dataset: name: all nli test type: all-nli-test metrics: - type: cosine_accuracy value: 0.9913636363636363 name: Cosine Accuracy - type: dot_accuracy value: 0.013939393939393939 name: Dot Accuracy - type: manhattan_accuracy value: 0.990909090909091 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9910606060606061 name: Euclidean Accuracy - type: max_accuracy value: 0.9913636363636363 name: Max Accuracy --- # 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) on the train dataset. 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) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - train - **Language:** hu - **License:** apache-2.0 ### 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("karsar/paraphrase-multilingual-MiniLM-L12-hu_v1") # Run inference sentences = [ 'Az emberek alszanak.', 'Egy apa és a fia ölelgeti alvás közben.', 'Egy csoport ember ül egy nyitott, térszerű területen, mögötte nagy bokrok és egy sor viktoriánus stílusú épület, melyek közül sokat a kép jobb oldalán lévő erős elmosódás tesz kivehetetlenné.', ] 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] ``` ## Evaluation ### Metrics #### Triplet * Dataset: `all-nli-dev` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:----------| | cosine_accuracy | 0.992 | | dot_accuracy | 0.0108 | | manhattan_accuracy | 0.9908 | | euclidean_accuracy | 0.9908 | | **max_accuracy** | **0.992** | #### Triplet * Dataset: `all-nli-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.9914 | | dot_accuracy | 0.0139 | | manhattan_accuracy | 0.9909 | | euclidean_accuracy | 0.9911 | | **max_accuracy** | **0.9914** | ## Training Details ### Training Dataset #### train * Dataset: train * Size: 857,856 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------| | Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett. | Egy ember a szabadban, lóháton. | Egy ember egy étteremben van, és omlettet rendel. | | Gyerekek mosolyogva és integetett a kamera | Gyermekek vannak jelen | A gyerekek homlokot rántanak | | Egy fiú ugrál a gördeszkát a közepén egy piros híd. | A fiú gördeszkás trükköt csinál. | A fiú korcsolyázik a járdán. | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### train * Dataset: train * Size: 5,000 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:----------------------------------------------|:---------------------------------------------------------------| | Egy lóháton ülő ember átugrik egy lerombolt repülőgép felett. | Egy ember a szabadban, lóháton. | Egy ember egy étteremben van, és omlettet rendel. | | Gyerekek mosolyogva és integetett a kamera | Gyermekek vannak jelen | A gyerekek homlokot rántanak | | Egy fiú ugrál a gördeszkát a közepén egy piros híd. | A fiú gördeszkás trükköt csinál. | A fiú korcsolyázik a járdán. | * Loss: [MultipleNegativesRankingLoss](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`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `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`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: True - `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 - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | train loss | all-nli-dev_max_accuracy | all-nli-test_max_accuracy | |:------:|:----:|:-------------:|:----------:|:------------------------:|:-------------------------:| | 0 | 0 | - | - | 0.7574 | - | | 0.0149 | 100 | 2.5002 | - | - | - | | 0.0298 | 200 | 1.9984 | - | - | - | | 0.0448 | 300 | 1.8094 | - | - | - | | 0.0597 | 400 | 1.6704 | - | - | - | | 0.0746 | 500 | 1.5518 | - | - | - | | 0.0895 | 600 | 1.449 | - | - | - | | 0.1044 | 700 | 1.5998 | - | - | - | | 0.1194 | 800 | 1.5725 | - | - | - | | 0.1343 | 900 | 1.5341 | - | - | - | | 0.1492 | 1000 | 1.3423 | - | - | - | | 0.1641 | 1100 | 1.2485 | - | - | - | | 0.1791 | 1200 | 1.1527 | - | - | - | | 0.1940 | 1300 | 1.1672 | - | - | - | | 0.2089 | 1400 | 1.2426 | - | - | - | | 0.2238 | 1500 | 1.0948 | - | - | - | | 0.2387 | 1600 | 1.0069 | - | - | - | | 0.2537 | 1700 | 0.976 | - | - | - | | 0.2686 | 1800 | 0.897 | - | - | - | | 0.2835 | 1900 | 0.7825 | - | - | - | | 0.2984 | 2000 | 0.9421 | 0.1899 | 0.9568 | - | | 0.3133 | 2100 | 0.8651 | - | - | - | | 0.3283 | 2200 | 0.8184 | - | - | - | | 0.3432 | 2300 | 0.699 | - | - | - | | 0.3581 | 2400 | 0.6704 | - | - | - | | 0.3730 | 2500 | 0.6477 | - | - | - | | 0.3879 | 2600 | 0.7077 | - | - | - | | 0.4029 | 2700 | 0.7364 | - | - | - | | 0.4178 | 2800 | 0.665 | - | - | - | | 0.4327 | 2900 | 1.2512 | - | - | - | | 0.4476 | 3000 | 1.3693 | - | - | - | | 0.4625 | 3100 | 1.3959 | - | - | - | | 0.4775 | 3200 | 1.4175 | - | - | - | | 0.4924 | 3300 | 1.402 | - | - | - | | 0.5073 | 3400 | 1.3832 | - | - | - | | 0.5222 | 3500 | 1.3671 | - | - | - | | 0.5372 | 3600 | 1.3666 | - | - | - | | 0.5521 | 3700 | 1.3479 | - | - | - | | 0.5670 | 3800 | 1.3272 | - | - | - | | 0.5819 | 3900 | 1.3353 | - | - | - | | 0.5968 | 4000 | 1.3177 | 0.0639 | 0.9902 | - | | 0.6118 | 4100 | 1.3068 | - | - | - | | 0.6267 | 4200 | 1.3054 | - | - | - | | 0.6416 | 4300 | 1.3098 | - | - | - | | 0.6565 | 4400 | 1.2839 | - | - | - | | 0.6714 | 4500 | 1.2976 | - | - | - | | 0.6864 | 4600 | 1.2669 | - | - | - | | 0.7013 | 4700 | 1.208 | - | - | - | | 0.7162 | 4800 | 1.194 | - | - | - | | 0.7311 | 4900 | 1.1974 | - | - | - | | 0.7460 | 5000 | 1.1834 | - | - | - | | 0.7610 | 5100 | 1.1876 | - | - | - | | 0.7759 | 5200 | 1.1743 | - | - | - | | 0.7908 | 5300 | 1.1839 | - | - | - | | 0.8057 | 5400 | 1.1778 | - | - | - | | 0.8207 | 5500 | 1.1711 | - | - | - | | 0.8356 | 5600 | 1.1809 | - | - | - | | 0.8505 | 5700 | 1.1825 | - | - | - | | 0.8654 | 5800 | 1.1795 | - | - | - | | 0.8803 | 5900 | 1.1788 | - | - | - | | 0.8953 | 6000 | 1.1819 | 0.0371 | 0.992 | - | | 0.9102 | 6100 | 1.1741 | - | - | - | | 0.9251 | 6200 | 1.1871 | - | - | - | | 0.9400 | 6300 | 0.498 | - | - | - | | 0.9549 | 6400 | 0.093 | - | - | - | | 0.9699 | 6500 | 0.1597 | - | - | - | | 0.9848 | 6600 | 0.2033 | - | - | - | | 0.9997 | 6700 | 0.16 | - | - | - | | 1.0 | 6702 | - | - | - | 0.9914 | ### Framework Versions - Python: 3.11.8 - Sentence Transformers: 3.1.1 - Transformers: 4.44.0 - PyTorch: 2.3.0.post101 - Accelerate: 0.33.0 - Datasets: 2.18.0 - Tokenizers: 0.19.0 ## 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} } ```