--- language: - multilingual - zh - ja - ar - ko - de - fr - es - pt - hi - id - it - tr - ru - bn - ur - mr - ta - vi - fa - pl - uk - nl - sv - he - sw - ps library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:10K - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) - **Languages:** multilingual, zh, ja, ar, ko, de, fr, es, pt, hi, id, it, tr, ru, bn, ur, mr, ta, vi, fa, pl, uk, nl, sv, he, sw, ps ### 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("sentence_transformers_model_id") # Run inference sentences = [ 'Ben vatansızım.', 'I am stateless.', 'Kendi tekniğini tercih ediyor.', ] 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 #### Semantic Similarity * Dataset: `tr_ling` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.0376 | | spearman_cosine | 0.048 | | pearson_manhattan | 0.0347 | | spearman_manhattan | 0.0377 | | pearson_euclidean | 0.037 | | spearman_euclidean | 0.039 | | pearson_dot | 0.0674 | | spearman_dot | 0.0682 | | pearson_max | 0.0674 | | **spearman_max** | **0.0682** | ## Training Details ### Training Dataset #### MoritzLaurer/multilingual-nli-26lang-2mil7 * Dataset: [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) at [510a233](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7/tree/510a233972a0d7ff0f767d82f46e046832c10538) * Size: 25,000 training samples * Columns: premise_original, hypothesis_original, score, sentence1, and sentence2 * Approximate statistics based on the first 1000 samples: | | premise_original | hypothesis_original | score | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | int | string | string | | details | | | | | | * Samples: | premise_original | hypothesis_original | score | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------| | N, the total number of LC50 values used in calculating the CV(%) varied with organism and toxicant because some data were rejected due to water hardness, lack of concentration measurements, and/or because some of the LC50s were not calculable. | Most discarded data was rejected due to water hardness. | 1 | N, CV'nin hesaplanmasında kullanılan LC50 değerlerinin toplam sayısı (%) organizma ve toksik madde ile çeşitlidir, çünkü bazı veriler su sertliği, konsantrasyon ölçümlerinin eksikliği ve / veya LC50'lerin bazıları hesaplanamaz olduğu için reddedilmiştir. | Atılan verilerin çoğu su sertliği nedeniyle reddedildi. | | As the home of the Venus de Milo and Mona Lisa, the Louvre drew almost unmanageable crowds until President Mitterrand ordered its re-organization in the 1980s. | The Louvre is home of the Venus de Milo and Mona Lisa. | 0 | Venus de Milo ve Mona Lisa'nın evi olarak Louvre, Başkan Mitterrand'ın 1980'lerde yeniden düzenlenmesini emredene kadar neredeyse yönetilemez kalabalıklar çekti. | Louvre, Venus de Milo ve Mona Lisa'nın evidir. | | A year ago, the wife of the Oxford don noticed that the pattern on Kleenex quilted tissue uncannily resembled the Penrose Arrowed Rhombi tilings pattern, which Sir Roger had invented--and copyrighted--in 1974. | It has been recently found out a similarity between the pattern on the recent Kleenex quilted tissue and the one of the Penrose Arrowed Rhombi tilings. | 0 | Bir yıl önce Oxford'un karısı, Kleenex kapitone dokudaki desenin 1974'te Sir Roger'ın icat ettiği -ve telif hakkı olan - Penrose Arrowed Rhombi tilings desenine benzediğini fark etti. | Yakın zamanda, son Kleenex kapitone dokudaki desen ile Penrose Arrowed Rhombi döşemelerinden biri arasında bir benzerlik bulunmuştur. | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### MoritzLaurer/multilingual-nli-26lang-2mil7 * Dataset: [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) at [510a233](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7/tree/510a233972a0d7ff0f767d82f46e046832c10538) * Size: 5,000 evaluation samples * Columns: premise_original, hypothesis_original, score, sentence1, and sentence2 * Approximate statistics based on the first 1000 samples: | | premise_original | hypothesis_original | score | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | int | string | string | | details | | | | | | * Samples: | premise_original | hypothesis_original | score | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------|:------------------------------------------------------------------------------|:-----------------------------------------------------------------| | But the racism charge isn't quirky or wacky--it's demagogy. | The accusation of prejudice based on a pedestrian kind of hatred. | 0 | Ama ırkçılık suçlaması tuhaf ya da tuhaf değil, bu bir demagoji. | Yaya nefretine dayanan önyargı suçlaması. | | Why would Gates allow the publication of such a book with his byline and photo on the dust jacket? | Gates' byline and photo are on the dust jacket | 0 | Gates neden böyle bir kitabın basılmasına izin versin ki? | Gates'in çizgisi ve fotoğrafı toz ceketin üzerinde. | | I am a nonsmoker and allergic to cigarette smoke. | I do not smoke. | 0 | Sigara içmeyen biriyim ve sigara dumanına alerjim var. | Sigara içmiyorum. | * Loss: [CoSENTLoss](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 - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 64 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `warmup_ratio`: 0.1 - `fp16`: True - `load_best_model_at_end`: True - `ddp_find_unused_parameters`: False #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `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 - `learning_rate`: 2e-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`: 5 - `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`: 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`: True - `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`: False - `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`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | tr_ling_spearman_max | |:------:|:----:|:-------------:|:------:|:--------------------:| | 0.0320 | 25 | 17.17 | - | - | | 0.0639 | 50 | 16.4932 | - | - | | 0.0959 | 75 | 16.5976 | - | - | | 0.1279 | 100 | 15.6991 | - | - | | 0.1598 | 125 | 14.876 | - | - | | 0.1918 | 150 | 14.4828 | - | - | | 0.2238 | 175 | 12.7061 | - | - | | 0.2558 | 200 | 10.8687 | - | - | | 0.2877 | 225 | 8.3797 | - | - | | 0.3197 | 250 | 6.2029 | - | - | | 0.3517 | 275 | 5.8228 | - | - | | 0.3836 | 300 | 5.811 | - | - | | 0.4156 | 325 | 5.8079 | - | - | | 0.4476 | 350 | 5.8077 | - | - | | 0.4795 | 375 | 5.8035 | - | - | | 0.5115 | 400 | 5.8072 | - | - | | 0.5435 | 425 | 5.8033 | - | - | | 0.5754 | 450 | 5.8086 | - | - | | 0.6074 | 475 | 5.81 | - | - | | 0.6394 | 500 | 5.7949 | - | - | | 0.6714 | 525 | 5.8079 | - | - | | 0.7033 | 550 | 5.8057 | - | - | | 0.7353 | 575 | 5.8097 | - | - | | 0.7673 | 600 | 5.7986 | - | - | | 0.7992 | 625 | 5.8051 | - | - | | 0.8312 | 650 | 5.8041 | - | - | | 0.8632 | 675 | 5.7907 | - | - | | 0.8951 | 700 | 5.7991 | - | - | | 0.9271 | 725 | 5.8035 | - | - | | 0.9591 | 750 | 5.7945 | - | - | | 0.9910 | 775 | 5.8077 | - | - | | 1.0 | 782 | - | 5.8024 | 0.0330 | | 1.0230 | 800 | 5.6703 | - | - | | 1.0550 | 825 | 5.8052 | - | - | | 1.0870 | 850 | 5.7936 | - | - | | 1.1189 | 875 | 5.7924 | - | - | | 1.1509 | 900 | 5.7806 | - | - | | 1.1829 | 925 | 5.7835 | - | - | | 1.2148 | 950 | 5.7619 | - | - | | 1.2468 | 975 | 5.8038 | - | - | | 1.2788 | 1000 | 5.779 | - | - | | 1.3107 | 1025 | 5.7904 | - | - | | 1.3427 | 1050 | 5.7696 | - | - | | 1.3747 | 1075 | 5.7919 | - | - | | 1.4066 | 1100 | 5.7785 | - | - | | 1.4386 | 1125 | 5.7862 | - | - | | 1.4706 | 1150 | 5.7703 | - | - | | 1.5026 | 1175 | 5.773 | - | - | | 1.5345 | 1200 | 5.7627 | - | - | | 1.5665 | 1225 | 5.7596 | - | - | | 1.5985 | 1250 | 5.7882 | - | - | | 1.6304 | 1275 | 5.7828 | - | - | | 1.6624 | 1300 | 5.771 | - | - | | 1.6944 | 1325 | 5.788 | - | - | | 1.7263 | 1350 | 5.7719 | - | - | | 1.7583 | 1375 | 5.7846 | - | - | | 1.7903 | 1400 | 5.7838 | - | - | | 1.8223 | 1425 | 5.7912 | - | - | | 1.8542 | 1450 | 5.7686 | - | - | | 1.8862 | 1475 | 5.7938 | - | - | | 1.9182 | 1500 | 5.7847 | - | - | | 1.9501 | 1525 | 5.7952 | - | - | | 1.9821 | 1550 | 5.7528 | - | - | | 2.0 | 1564 | - | 5.7933 | 0.0682 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - Transformers: 4.41.0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.30.1 - Datasets: 2.19.1 - 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", } ``` #### 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}, } ```