--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:910013 - loss:CosineSimilarityLoss base_model: intfloat/multilingual-e5-small widget: - source_sentence: business healing sentences: - modify ict system capacity - objetividade, inovadora,estudiosa,pesquisadora e organizada - business consulting - source_sentence: architecture acoustics sentences: - disicpline leader - 生产工艺开发及优化 - data analysis - source_sentence: arbitru natatie sentences: - criação cinematográfica - quarterly distribution - улучшение путешествий клиентов с помощью дополненной реальности - source_sentence: configuración de software antivirus sentences: - protocol & coordination - laurea magistrale biologia - deploy anti-virus software - source_sentence: child maltreatment counselling sentences: - book covers, flyers, posters, banners - tool and die making - cmc pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-small results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.9579653395486292 name: Pearson Cosine - type: spearman_cosine value: 0.8788941637037295 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.9579215714676803 name: Pearson Cosine - type: spearman_cosine value: 0.8795799743051839 name: Spearman Cosine --- # SentenceTransformer based on intfloat/multilingual-e5-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) - **Maximum Sequence Length:** 30 tokens - **Output Dimensionality:** 384 dimensions - **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': 30, '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}) (2): Normalize() ) ``` ## 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 = [ 'child maltreatment counselling', 'cmc', 'book covers, flyers, posters, banners', ] 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 * Datasets: `sts-dev` and `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | sts-dev | sts-test | |:--------------------|:-----------|:-----------| | pearson_cosine | 0.958 | 0.9579 | | **spearman_cosine** | **0.8789** | **0.8796** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 910,013 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:----------------------------------------------------------------------------------------------|:--------------------------------------------------|:------------------| | edición de fotografias, fondos | material selection and cognition | 0.0 | | professional alarm installer,service tech.,customer service relations,sales,cctv | quantity surveying & reading charts | 0.1 | | diagnostico ecografico | waste identification system downtime | 0.19 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 113,751 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:----------------------------------------------------------|:-------------------------|:------------------| | a2 dutch | a2 dutch | 0.98 | | design of mine dumps | 设计矿山废料堆 | 1.0 | | create soil and plant improvement programmes | 创建土壤和植物改良计划 | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 1e-05 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `fp16`: True #### 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`: 32 - `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`: 1e-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`: 4 - `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`: 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} - `tp_size`: 0 - `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 - `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 - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:-----:|:-------------:|:---------------:|:-----------------------:|:------------------------:| | 0.0352 | 500 | 0.1991 | - | - | - | | 0.0703 | 1000 | 0.0513 | - | - | - | | 0.1055 | 1500 | 0.0362 | - | - | - | | 0.1407 | 2000 | 0.0331 | - | - | - | | 0.1758 | 2500 | 0.0305 | - | - | - | | 0.2110 | 3000 | 0.029 | - | - | - | | 0.2461 | 3500 | 0.0273 | - | - | - | | 0.2813 | 4000 | 0.0268 | - | - | - | | 0.3165 | 4500 | 0.0255 | - | - | - | | 0.3516 | 5000 | 0.0245 | - | - | - | | 0.3868 | 5500 | 0.0238 | - | - | - | | 0.4220 | 6000 | 0.0236 | - | - | - | | 0.4571 | 6500 | 0.0233 | - | - | - | | 0.4923 | 7000 | 0.0222 | - | - | - | | 0.5275 | 7500 | 0.0225 | - | - | - | | 0.5626 | 8000 | 0.0219 | - | - | - | | 0.5978 | 8500 | 0.0212 | - | - | - | | 0.6330 | 9000 | 0.0215 | - | - | - | | 0.6681 | 9500 | 0.0207 | - | - | - | | 0.7033 | 10000 | 0.0204 | - | - | - | | 0.7384 | 10500 | 0.0203 | - | - | - | | 0.7736 | 11000 | 0.0203 | - | - | - | | 0.8088 | 11500 | 0.0202 | - | - | - | | 0.8439 | 12000 | 0.0202 | - | - | - | | 0.8791 | 12500 | 0.0196 | - | - | - | | 0.9143 | 13000 | 0.0193 | - | - | - | | 0.9494 | 13500 | 0.0193 | - | - | - | | 0.9846 | 14000 | 0.0193 | - | - | - | | 1.0 | 14219 | - | 0.0170 | 0.8694 | - | | 1.0198 | 14500 | 0.0188 | - | - | - | | 1.0549 | 15000 | 0.0178 | - | - | - | | 1.0901 | 15500 | 0.0179 | - | - | - | | 1.1253 | 16000 | 0.0178 | - | - | - | | 1.1604 | 16500 | 0.0178 | - | - | - | | 1.1956 | 17000 | 0.0172 | - | - | - | | 1.2307 | 17500 | 0.0172 | - | - | - | | 1.2659 | 18000 | 0.0175 | - | - | - | | 1.3011 | 18500 | 0.0178 | - | - | - | | 1.3362 | 19000 | 0.0174 | - | - | - | | 1.3714 | 19500 | 0.0175 | - | - | - | | 1.4066 | 20000 | 0.0171 | - | - | - | | 1.4417 | 20500 | 0.0175 | - | - | - | | 1.4769 | 21000 | 0.0173 | - | - | - | | 1.5121 | 21500 | 0.0171 | - | - | - | | 1.5472 | 22000 | 0.0174 | - | - | - | | 1.5824 | 22500 | 0.0172 | - | - | - | | 1.6176 | 23000 | 0.0168 | - | - | - | | 1.6527 | 23500 | 0.0165 | - | - | - | | 1.6879 | 24000 | 0.0169 | - | - | - | | 1.7230 | 24500 | 0.0169 | - | - | - | | 1.7582 | 25000 | 0.0171 | - | - | - | | 1.7934 | 25500 | 0.0165 | - | - | - | | 1.8285 | 26000 | 0.0165 | - | - | - | | 1.8637 | 26500 | 0.0165 | - | - | - | | 1.8989 | 27000 | 0.0165 | - | - | - | | 1.9340 | 27500 | 0.0164 | - | - | - | | 1.9692 | 28000 | 0.0164 | - | - | - | | 2.0 | 28438 | - | 0.0153 | 0.8751 | - | | 2.0044 | 28500 | 0.0162 | - | - | - | | 2.0395 | 29000 | 0.0156 | - | - | - | | 2.0747 | 29500 | 0.0154 | - | - | - | | 2.1099 | 30000 | 0.0157 | - | - | - | | 2.1450 | 30500 | 0.016 | - | - | - | | 2.1802 | 31000 | 0.015 | - | - | - | | 2.2153 | 31500 | 0.0155 | - | - | - | | 2.2505 | 32000 | 0.0154 | - | - | - | | 2.2857 | 32500 | 0.0152 | - | - | - | | 2.3208 | 33000 | 0.0152 | - | - | - | | 2.3560 | 33500 | 0.0152 | - | - | - | | 2.3912 | 34000 | 0.0154 | - | - | - | | 2.4263 | 34500 | 0.0153 | - | - | - | | 2.4615 | 35000 | 0.0154 | - | - | - | | 2.4967 | 35500 | 0.015 | - | - | - | | 2.5318 | 36000 | 0.0153 | - | - | - | | 2.5670 | 36500 | 0.0149 | - | - | - | | 2.6022 | 37000 | 0.015 | - | - | - | | 2.6373 | 37500 | 0.0152 | - | - | - | | 2.6725 | 38000 | 0.0152 | - | - | - | | 2.7076 | 38500 | 0.015 | - | - | - | | 2.7428 | 39000 | 0.0151 | - | - | - | | 2.7780 | 39500 | 0.0155 | - | - | - | | 2.8131 | 40000 | 0.0148 | - | - | - | | 2.8483 | 40500 | 0.0149 | - | - | - | | 2.8835 | 41000 | 0.0147 | - | - | - | | 2.9186 | 41500 | 0.015 | - | - | - | | 2.9538 | 42000 | 0.0148 | - | - | - | | 2.9890 | 42500 | 0.0146 | - | - | - | | 3.0 | 42657 | - | 0.0146 | 0.8775 | - | | 3.0241 | 43000 | 0.0142 | - | - | - | | 3.0593 | 43500 | 0.0144 | - | - | - | | 3.0945 | 44000 | 0.0146 | - | - | - | | 3.1296 | 44500 | 0.0142 | - | - | - | | 3.1648 | 45000 | 0.0144 | - | - | - | | 3.1999 | 45500 | 0.0141 | - | - | - | | 3.2351 | 46000 | 0.0142 | - | - | - | | 3.2703 | 46500 | 0.0142 | - | - | - | | 3.3054 | 47000 | 0.0142 | - | - | - | | 3.3406 | 47500 | 0.0145 | - | - | - | | 3.3758 | 48000 | 0.0142 | - | - | - | | 3.4109 | 48500 | 0.0143 | - | - | - | | 3.4461 | 49000 | 0.0145 | - | - | - | | 3.4813 | 49500 | 0.0142 | - | - | - | | 3.5164 | 50000 | 0.014 | - | - | - | | 3.5516 | 50500 | 0.0141 | - | - | - | | 3.5868 | 51000 | 0.0144 | - | - | - | | 3.6219 | 51500 | 0.0143 | - | - | - | | 3.6571 | 52000 | 0.0143 | - | - | - | | 3.6922 | 52500 | 0.0142 | - | - | - | | 3.7274 | 53000 | 0.014 | - | - | - | | 3.7626 | 53500 | 0.0142 | - | - | - | | 3.7977 | 54000 | 0.0141 | - | - | - | | 3.8329 | 54500 | 0.0141 | - | - | - | | 3.8681 | 55000 | 0.014 | - | - | - | | 3.9032 | 55500 | 0.0143 | - | - | - | | 3.9384 | 56000 | 0.0142 | - | - | - | | 3.9736 | 56500 | 0.0141 | - | - | - | | 4.0 | 56876 | - | 0.0146 | 0.8789 | - | | -1 | -1 | - | - | - | 0.8796 |
### Framework Versions - Python: 3.11.11 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.2 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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", } ```