--- language: [] library_name: sentence-transformers tags: - mteb - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:100K - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### 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: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, '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("Gameselo/STS-multilingual-mpnet-base-v2") # Run inference sentences = [ '一个女人正在洗澡。', 'A woman is taking a bath.', 'En jente børster håret sitt', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.9551 | | **spearman_cosine** | **0.9593** | | pearson_manhattan | 0.927 | | spearman_manhattan | 0.9383 | | pearson_euclidean | 0.9278 | | spearman_euclidean | 0.9394 | | pearson_dot | 0.876 | | spearman_dot | 0.8865 | | pearson_max | 0.9551 | | spearman_max | 0.9593 | #### Evalutation results vs SOTA results * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.948 | | **spearman_cosine** | **0.9515** | | pearson_manhattan | 0.9252 | | spearman_manhattan | 0.9352 | | pearson_euclidean | 0.9258 | | spearman_euclidean | 0.9364 | | pearson_dot | 0.8443 | | spearman_dot | 0.8435 | | pearson_max | 0.948 | | spearman_max | 0.9515 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 226,547 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:-------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------| | Bir kadın makineye dikiş dikiyor. | Bir kadın biraz et ekiyor. | 0.12 | | Snowden 'gegeven vluchtelingendocument door Ecuador'. | Snowden staat op het punt om uit Moskou te vliegen | 0.24000000953674316 | | Czarny pies idzie mostem przez wodę | Czarny pies nie idzie mostem przez wodę | 0.74000000954 | * Loss: [AnglELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_angle_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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 - `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`: 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, '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_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:-----------------------:|:------------------------:| | 0.5650 | 500 | 10.9426 | - | - | | 1.0 | 885 | - | 0.9202 | - | | 1.1299 | 1000 | 9.7184 | - | - | | 1.6949 | 1500 | 9.5348 | - | - | | 2.0 | 1770 | - | 0.9400 | - | | 2.2599 | 2000 | 9.4412 | - | - | | 2.8249 | 2500 | 9.3097 | - | - | | 3.0 | 2655 | - | 0.9489 | - | | 3.3898 | 3000 | 9.2357 | - | - | | 3.9548 | 3500 | 9.1594 | - | - | | 4.0 | 3540 | - | 0.9528 | - | | 4.5198 | 4000 | 9.0963 | - | - | | 5.0 | 4425 | - | 0.9553 | - | | 5.0847 | 4500 | 9.0382 | - | - | | 5.6497 | 5000 | 8.9837 | - | - | | 6.0 | 5310 | - | 0.9567 | - | | 6.2147 | 5500 | 8.9403 | - | - | | 6.7797 | 6000 | 8.8841 | - | - | | 7.0 | 6195 | - | 0.9581 | - | | 7.3446 | 6500 | 8.8513 | - | - | | 7.9096 | 7000 | 8.81 | - | - | | 8.0 | 7080 | - | 0.9582 | - | | 8.4746 | 7500 | 8.8069 | - | - | | 9.0 | 7965 | - | 0.9589 | - | | 9.0395 | 8000 | 8.7616 | - | - | | 9.6045 | 8500 | 8.7521 | - | - | | 10.0 | 8850 | - | 0.9593 | 0.6266 | ### Framework Versions - Python: 3.9.7 - Sentence Transformers: 3.0.0 - Transformers: 4.40.1 - PyTorch: 2.3.0+cu121 - Accelerate: 0.29.3 - Datasets: 2.19.0 - 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", } ``` #### AnglELoss ```bibtex @misc{li2023angleoptimized, title={AnglE-optimized Text Embeddings}, author={Xianming Li and Jing Li}, year={2023}, eprint={2309.12871}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```