--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:269012 - loss:CoSENTLoss base_model: intfloat/e5-large-v2 widget: - source_sentence: smart cutting machine for crafts sentences: - HyperX Cloud Alpha Wireless Gaming Headset - Rubbermaid Brilliance 20-Piece Food Storage Set - Men's Wick Short Sleeve Crew - Light Merino Wool Camo Hunting Shirt, UV Protection Moisture Management Base Layer - source_sentence: high capacity portable hard drive sentences: - Mr. Heater Big Buddy Portable Propane Heater - Samsung Galaxy Watch 5 Pro - Sun Bum Original SPF 45 Sunscreen Mist - Broad Spectrum Moisturizing Facial Sunscreen Spray with Vitamin E - Hawaii 104 Act Compliant (Made without Octinoxate & Oxybenzone) - Travel Friendly - 3.4 oz - source_sentence: fluid acrylics for pouring art sentences: - Linen Suit for Men 2 Pieces Slim Fit Casual Suits Groomsmen Tuxedos Wedding Party Blazer Pants Set Beige - Mejuri Small Hoop Earrings in Gold - Singer Start 1304 Sewing Machine - source_sentence: premium wireless gaming headset sentences: - Vornado MVH Whole Room Heater - Westinghouse 11000 Peak Watt Tri-Fuel Portable Inverter Generator, Remote Start, Transfer Switch Ready, Gas/Propane/Natural Gas Powered, Low THD, Safe for Electronics, Parallel Capable, CO Sensor - Rattaner Patio Wicker Furniture Set 6 Pieces Outdoor HDPE Wicker Conversation Couch Sectional Chair Sofa Set with Grey Cushions - source_sentence: travel system with stroller and car seat sentences: - Chemex Classic Series Pour-Over Glass Coffeemaker - David Yurman Cable Classic Bracelet - Legion Stonehenge Paper Pad pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on intfloat/e5-large-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2). It maps sentences & paragraphs to a 1024-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/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 1024, '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 = [ 'travel system with stroller and car seat', 'Chemex Classic Series Pour-Over Glass Coffeemaker', 'Legion Stonehenge Paper Pad', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.5295, 0.5210], # [0.5295, 1.0000, 0.5429], # [0.5210, 0.5429, 1.0000]]) ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 269,012 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 | |:---------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------| | razor set with handle and blades | Hahnemühle Watercolor Journal | -0.8008412511835391 | | mini perfume atomizer for refillable travel scent | LISAPACK Perfume Travel Refillable Bottle - Atomizer Cologne Spray for Men Portable - Mini Sprayer Empty for Refill - Small Size 8ML Striped (Grey, Black, Silver) | 0.85625 | | pour-over glass coffeemaker | Shark Navigator Lift-Away Professional NV356E Vacuum | 0.131319533933279 | * 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 - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 1 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `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`: 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`: 1 - `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 - `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`: 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} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `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 - `hub_revision`: None - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.0595 | 500 | 5.606 | | 0.1189 | 1000 | 5.5059 | | 0.1784 | 1500 | 5.4614 | | 0.2379 | 2000 | 5.4299 | | 0.2974 | 2500 | 5.415 | | 0.3568 | 3000 | 5.4104 | | 0.4163 | 3500 | 5.3718 | | 0.4758 | 4000 | 5.3755 | | 0.5353 | 4500 | 5.3545 | | 0.5947 | 5000 | 5.3498 | | 0.6542 | 5500 | 5.3392 | | 0.7137 | 6000 | 5.3521 | | 0.7732 | 6500 | 5.3248 | | 0.8326 | 7000 | 5.3044 | | 0.8921 | 7500 | 5.2916 | | 0.9516 | 8000 | 5.2891 | ### Framework Versions - Python: 3.12.11 - Sentence Transformers: 5.1.0 - Transformers: 4.56.0 - PyTorch: 2.8.0+cu126 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## 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", } ``` #### 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}, } ```