| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - dense |
| | - generated_from_trainer |
| | - dataset_size:269337 |
| | - loss:CoSENTLoss |
| | base_model: intfloat/multilingual-e5-large |
| | widget: |
| | - source_sentence: motion-activated security light with adjustable settings |
| | sentences: |
| | - LED Black Motion Sensor 2-Light Bullet Flood Light- 3000K Adjustable Dual Head |
| | Outdoor Security Light, Dusk to Dawn, Waterproof, Hardwired Spotlight for Yard, |
| | Patio, Garage, Landscape |
| | - Waterpik Cordless Advanced Water Flosser |
| | - Tabi Ballet Flats Shoes for Women Rounde Toe Wide Width Split Toe Low Heel Comfortable |
| | Flats Shoes |
| | - source_sentence: microdevice for line smoothing |
| | sentences: |
| | - SkinMedica TNS Advanced+ Serum |
| | - Waterproof Beach Bag for Women with Phone Pouch, Large Tote Bag for Pool, Travel |
| | and Vacation |
| | - Fisher-Price 4-in-1 Step 'n Play Piano |
| | - source_sentence: hair strengthening serum |
| | sentences: |
| | - Yaheetech Adjustable Dumbbell Set Free Weight Dumbbells 40lbs/52.5lbs/90lbs Fast |
| | Adjust Dumbbells Dumbbell Weight Set, with Tray for Men/Women Strength Training |
| | Equipment |
| | - DeLonghi Dedica Arte Espresso Machine |
| | - Opalescence Go Teeth Whitening Trays |
| | - source_sentence: slime making kit with glue and additives |
| | sentences: |
| | - Faber-Castell Polychromos Color Pencils Set of 120 |
| | - Keter Delivery Box for Porch with Lockable Secure Storage Compartment to Keep |
| | Packages Safe, One Size, Brown |
| | - Stillman & Birn Zeta Series Sketchbook |
| | - source_sentence: antioxidant serum for skin protection |
| | sentences: |
| | - Louisville Ladder 16-foot Fiberglass Extension Ladder |
| | - Crayola Light Up Tracing Pad |
| | - Logitech MX Master 3S Wireless Mouse |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | --- |
| | |
| | # SentenceTransformer based on intfloat/multilingual-e5-large |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). 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/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision 0dc5580a448e4284468b8909bae50fa925907bc5 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 1024 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### 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': 'XLMRobertaModel'}) |
| | (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("IshTale/MultiEccomerceEmbeddingModel") |
| | # Run inference |
| | sentences = [ |
| | 'antioxidant serum for skin protection', |
| | 'Louisville Ladder 16-foot Fiberglass Extension Ladder', |
| | 'Logitech MX Master 3S Wireless Mouse', |
| | ] |
| | 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, 0.4999, 0.4880], |
| | # [0.4999, 1.0000, 0.6445], |
| | # [0.4880, 0.6445, 1.0000]]) |
| | ``` |
| |
|
| | <!-- |
| | ### Direct Usage (Transformers) |
| |
|
| | <details><summary>Click to see the direct usage in Transformers</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Downstream Usage (Sentence Transformers) |
| |
|
| | You can finetune this model on your own dataset. |
| |
|
| | <details><summary>Click to expand</summary> |
| |
|
| | </details> |
| | --> |
| |
|
| | <!-- |
| | ### Out-of-Scope Use |
| |
|
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| |
|
| | <!-- |
| | ## Bias, Risks and Limitations |
| |
|
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| |
|
| | <!-- |
| | ### Recommendations |
| |
|
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| |
|
| | ## Training Details |
| |
|
| | ### Training Dataset |
| |
|
| | #### Unnamed Dataset |
| |
|
| | * Size: 269,337 training samples |
| | * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence_0 | sentence_1 | label | |
| | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 5 tokens</li><li>mean: 11.2 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 24.29 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: -1.0</li><li>mean: 0.05</li><li>max: 0.99</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | label | |
| | |:--------------------------------------------------------|:----------------------------------------------------|:----------------------------------| |
| | | <code>motorized Nerf blaster with dinosaur theme</code> | <code>B. Toys by Battat Wooden Activity Cube</code> | <code>-0.07861651138439901</code> | |
| | | <code>smart mirror with adjustable lighting</code> | <code>Pfaff Passport 2.0 Sewing Machine</code> | <code>-0.835469516572358</code> | |
| | | <code>black tea with orange rind and spices</code> | <code>Valrhona Cocoa Powder</code> | <code>-0.13135949520666002</code> | |
| | * Loss: [<code>CoSENTLoss</code>](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 |
| | <details><summary>Click to expand</summary> |
| | |
| | - `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`: {} |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Training Loss | |
| | |:------:|:----:|:-------------:| |
| | | 0.0594 | 500 | 5.6346 | |
| | | 0.1188 | 1000 | 5.5107 | |
| | | 0.1782 | 1500 | 5.4706 | |
| | | 0.2376 | 2000 | 5.4402 | |
| | | 0.2970 | 2500 | 5.4039 | |
| | | 0.3564 | 3000 | 5.4252 | |
| | | 0.4158 | 3500 | 5.3693 | |
| | | 0.4752 | 4000 | 5.3776 | |
| | | 0.5346 | 4500 | 5.3672 | |
| | | 0.5940 | 5000 | 5.4059 | |
| | | 0.6534 | 5500 | 5.336 | |
| | | 0.7128 | 6000 | 5.3467 | |
| | | 0.7722 | 6500 | 5.3086 | |
| |
|
| |
|
| | ### Framework Versions |
| | - Python: 3.12.11 |
| | - Sentence Transformers: 5.1.0 |
| | - Transformers: 4.56.1 |
| | - 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}, |
| | } |
| | ``` |
| |
|
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| | ## Glossary |
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