--- 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:1044013 - 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-hu results: - task: type: triplet name: Triplet dataset: name: all nli dev type: all-nli-dev metrics: - type: cosine_accuracy value: 0.9918 name: Cosine Accuracy - type: dot_accuracy value: 0.0102 name: Dot Accuracy - type: manhattan_accuracy value: 0.99 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.99 name: Euclidean Accuracy - type: max_accuracy value: 0.9918 name: Max Accuracy - task: type: triplet name: Triplet dataset: name: all nli test type: all-nli-test metrics: - type: cosine_accuracy value: 0.9937878787878788 name: Cosine Accuracy - type: dot_accuracy value: 0.00803030303030303 name: Dot Accuracy - type: manhattan_accuracy value: 0.9928787878787879 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9924242424242424 name: Euclidean Accuracy - type: max_accuracy value: 0.9937878787878788 name: Max Accuracy --- # paraphrase-multilingual-MiniLM-L12-v2-hu 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-v2") # 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 | 0.9918 | | dot_accuracy | 0.0102 | | manhattan_accuracy | 0.99 | | euclidean_accuracy | 0.99 | | **max_accuracy** | **0.9918** | #### 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 | 0.9938 | | dot_accuracy | 0.008 | | manhattan_accuracy | 0.9929 | | euclidean_accuracy | 0.9924 | | **max_accuracy** | **0.9938** | ## Training Details ### Training Dataset #### train * Dataset: train * Size: 1,044,013 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 | - | | 0.0123 | 100 | 2.5472 | - | - | - | | 0.0245 | 200 | 2.0478 | - | - | - | | 0.0368 | 300 | 1.8426 | - | - | - | | 0.0490 | 400 | 1.698 | - | - | - | | 0.0613 | 500 | 1.5715 | - | - | - | | 0.0736 | 600 | 1.4616 | - | - | - | | 0.0858 | 700 | 1.6106 | - | - | - | | 0.0981 | 800 | 1.5849 | - | - | - | | 0.1103 | 900 | 1.5374 | - | - | - | | 0.1226 | 1000 | 1.3653 | - | - | - | | 0.1349 | 1100 | 1.274 | - | - | - | | 0.1471 | 1200 | 1.1907 | - | - | - | | 0.1594 | 1300 | 1.2155 | - | - | - | | 0.1716 | 1400 | 1.2786 | - | - | - | | 0.1839 | 1500 | 1.1062 | - | - | - | | 0.1962 | 1600 | 1.0289 | - | - | - | | 0.2084 | 1700 | 1.0013 | - | - | - | | 0.2207 | 1800 | 0.9209 | - | - | - | | 0.2329 | 1900 | 0.8095 | - | - | - | | 0.2452 | 2000 | 0.9753 | 0.1916 | 0.9558 | - | | 0.2574 | 2100 | 0.8728 | - | - | - | | 0.2697 | 2200 | 0.8343 | - | - | - | | 0.2820 | 2300 | 0.7203 | - | - | - | | 0.2942 | 2400 | 0.6901 | - | - | - | | 0.3065 | 2500 | 0.6606 | - | - | - | | 0.3187 | 2600 | 0.7205 | - | - | - | | 0.3310 | 2700 | 0.7479 | - | - | - | | 0.3433 | 2800 | 0.6677 | - | - | - | | 0.3555 | 2900 | 1.2531 | - | - | - | | 0.3678 | 3000 | 1.3619 | - | - | - | | 0.3800 | 3100 | 1.3923 | - | - | - | | 0.3923 | 3200 | 1.412 | - | - | - | | 0.4046 | 3300 | 1.3904 | - | - | - | | 0.4168 | 3400 | 1.3782 | - | - | - | | 0.4291 | 3500 | 1.3601 | - | - | - | | 0.4413 | 3600 | 1.3582 | - | - | - | | 0.4536 | 3700 | 1.3402 | - | - | - | | 0.4659 | 3800 | 1.32 | - | - | - | | 0.4781 | 3900 | 1.3277 | - | - | - | | 0.4904 | 4000 | 1.3112 | 0.0699 | 0.987 | - | | 0.5026 | 4100 | 1.2992 | - | - | - | | 0.5149 | 4200 | 1.3005 | - | - | - | | 0.5272 | 4300 | 1.2978 | - | - | - | | 0.5394 | 4400 | 1.272 | - | - | - | | 0.5517 | 4500 | 1.2864 | - | - | - | | 0.5639 | 4600 | 1.2519 | - | - | - | | 0.5762 | 4700 | 1.1924 | - | - | - | | 0.5885 | 4800 | 1.1778 | - | - | - | | 0.6007 | 4900 | 1.1801 | - | - | - | | 0.6130 | 5000 | 1.1666 | - | - | - | | 0.6252 | 5100 | 1.1682 | - | - | - | | 0.6375 | 5200 | 1.1518 | - | - | - | | 0.6497 | 5300 | 1.1606 | - | - | - | | 0.6620 | 5400 | 1.1534 | - | - | - | | 0.6743 | 5500 | 1.1473 | - | - | - | | 0.6865 | 5600 | 1.1596 | - | - | - | | 0.6988 | 5700 | 1.1536 | - | - | - | | 0.7110 | 5800 | 1.1517 | - | - | - | | 0.7233 | 5900 | 1.1517 | - | - | - | | 0.7356 | 6000 | 1.153 | 0.0359 | 0.9896 | - | | 0.7478 | 6100 | 1.142 | - | - | - | | 0.7601 | 6200 | 1.093 | - | - | - | | 0.7723 | 6300 | 1.1764 | - | - | - | | 0.7846 | 6400 | 1.1868 | - | - | - | | 0.7969 | 6500 | 1.0308 | - | - | - | | 0.8091 | 6600 | 1.0122 | - | - | - | | 0.8214 | 6700 | 1.0084 | - | - | - | | 0.8336 | 6800 | 1.0151 | - | - | - | | 0.8459 | 6900 | 1.0121 | - | - | - | | 0.8582 | 7000 | 1.0071 | - | - | - | | 0.8704 | 7100 | 1.1543 | - | - | - | | 0.8827 | 7200 | 1.1915 | - | - | - | | 0.8949 | 7300 | 1.2224 | - | - | - | | 0.9072 | 7400 | 1.1463 | - | - | - | | 0.9195 | 7500 | 1.0254 | - | - | - | | 0.9317 | 7600 | 1.2396 | - | - | - | | 0.9440 | 7700 | 1.1225 | - | - | - | | 0.9562 | 7800 | 0.7177 | - | - | - | | 0.9685 | 7900 | 0.0681 | - | - | - | | 0.9808 | 8000 | 0.0264 | 0.0317 | 0.9918 | - | | 0.9930 | 8100 | 0.078 | - | - | - | | 1.0 | 8157 | - | - | - | 0.9938 | ### 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: 3.0.2 - 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} } ```