| --- |
| language: |
| - multilingual |
| license: mit |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:74864 |
| - loss:CoSENTLoss |
| base_model: intfloat/multilingual-e5-small |
| widget: |
| - source_sentence: Légumes mijotés Jardinière et haricots blancs |
| sentences: |
| - AMSCAN GOLD PLSTC FORKS | PARTY SUPPLY | 240 CT. |
| - 辣椒酱 |
| - Pizza de verduras brasadas |
| - source_sentence: VTech Crazy Legs Learning Bugs, Pink |
| sentences: |
| - LEGO Creator Expert Garagem de Canto 10264 Kit de Construção, Novo 2019 (2569 |
| Peças), Embalagem Sem Frustrações |
| - Silver Glitter Hanging Fans (4 ct) |
| - VTech Aspirateur Pop et Compte |
| - source_sentence: Pacon Tru-Ray Construction Paper, 18-Inches by 24-Inches, 50-Count, |
| Red (103094) |
| sentences: |
| - Funko POP Televisione Westworld Bernard Lowe Action figure |
| - Carta da costruzione Tru-Ray pesante, colori assortiti caldi, 12" x 18", 50 fogli |
| - Max Factory Kizuna Ai Figma Action Figure |
| - source_sentence: Zesty Cilantro Salsa, Medium |
| sentences: |
| - Melange de fruits |
| - Salsa de Texas |
| - T.S. Shure Rubber Band Powered Rescue Flier Model Plane Kit |
| - source_sentence: Fun World Angelic Maiden Child Costume |
| sentences: |
| - Melissa & Doug Personalized Pattern Blocks & Boards Classic Toy |
| - Winter sprats gerookt |
| - Rubie's Costume Co - Girls Gypsy Costume |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@1 |
| - cosine_map@3 |
| - cosine_map@5 |
| - cosine_map@10 |
| model-index: |
| - name: multilingual-e5-small embeddings (CoSENTLoss on graded listwise pairs) |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: ir eval |
| type: ir_eval |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.91015625 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.95703125 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.97265625 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 1.0 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.91015625 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.5104166666666666 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.40078125000000003 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.296875 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.13477527216379598 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.1739842681808551 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.1983227020362507 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.2486998357621607 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.4650339807377877 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.937943328373016 |
| name: Cosine Mrr@10 |
| - type: cosine_map@1 |
| value: 0.91015625 |
| name: Cosine Map@1 |
| - type: cosine_map@3 |
| value: 0.5282118055555556 |
| name: Cosine Map@3 |
| - type: cosine_map@5 |
| value: 0.42098524305555557 |
| name: Cosine Map@5 |
| - type: cosine_map@10 |
| value: 0.3311448220781368 |
| name: Cosine Map@10 |
| --- |
| |
| # multilingual-e5-small embeddings (CoSENTLoss on graded listwise pairs) |
|
|
| 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) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 384 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| - **Language:** multilingual |
| - **License:** mit |
|
|
| ### 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': 256, 'do_lower_case': False, 'architecture': '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("Antix5/product-embed-multi-e5-small") |
| # Run inference |
| sentences = [ |
| 'Fun World Angelic Maiden Child Costume', |
| "Rubie's Costume Co - Girls Gypsy Costume", |
| 'Melissa & Doug Personalized Pattern Blocks & Boards Classic Toy', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 384] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.7135, 0.6875], |
| # [0.7135, 1.0000, 0.6791], |
| # [0.6875, 0.6791, 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.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Information Retrieval |
|
|
| * Dataset: `ir_eval` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:----------| |
| | cosine_accuracy@1 | 0.9102 | |
| | cosine_accuracy@3 | 0.957 | |
| | cosine_accuracy@5 | 0.9727 | |
| | cosine_accuracy@10 | 1.0 | |
| | cosine_precision@1 | 0.9102 | |
| | cosine_precision@3 | 0.5104 | |
| | cosine_precision@5 | 0.4008 | |
| | cosine_precision@10 | 0.2969 | |
| | cosine_recall@1 | 0.1348 | |
| | cosine_recall@3 | 0.174 | |
| | cosine_recall@5 | 0.1983 | |
| | cosine_recall@10 | 0.2487 | |
| | **cosine_ndcg@10** | **0.465** | |
| | cosine_mrr@10 | 0.9379 | |
| | cosine_map@1 | 0.9102 | |
| | cosine_map@3 | 0.5282 | |
| | cosine_map@5 | 0.421 | |
| | cosine_map@10 | 0.3311 | |
| |
| <!-- |
| ## 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: 74,864 training samples |
| * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | text1 | text2 | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 4 tokens</li><li>mean: 19.67 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.59 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | text1 | text2 | label | |
| |:-----------------------------------------------------------------|:------------------------------------------------------------------------------|:-----------------| |
| | <code>Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch</code> | <code>Premier 26764 Tourbillon pour voiture, Santa, 25 x 19-1/2 pouces</code> | <code>1.0</code> | |
| | <code>Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch</code> | <code>BNTS, ЧИПСЫ ИЗ ФАСОЛИ NV И МОРСКАЯ СОЛЬ</code> | <code>0.0</code> | |
| | <code>Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch</code> | <code>Beanitos, Чипс из фасоли navy, Сыр на чо</code> | <code>0.0</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 |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 256 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 2 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 32 |
| - `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 |
| - `torch_empty_cache_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`: 2 |
| - `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} |
| - `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`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | ir_eval_cosine_ndcg@10 | |
| |:------:|:----:|:-------------:|:----------------------:| |
| | 0.0004 | 1 | 5.9178 | - | |
| | 0.0427 | 100 | 5.7854 | - | |
| | 0.0855 | 200 | 5.7118 | - | |
| | 0.1282 | 300 | 5.6765 | - | |
| | 0.1709 | 400 | 5.647 | - | |
| | 0.2137 | 500 | 5.6046 | - | |
| | 0.2564 | 600 | 5.5859 | - | |
| | 0.2991 | 700 | 5.5586 | - | |
| | 0.3419 | 800 | 5.5319 | - | |
| | 0.3846 | 900 | 5.564 | - | |
| | 0.4274 | 1000 | 5.577 | 0.4854 | |
| | 0.4701 | 1100 | 5.5229 | - | |
| | 0.5128 | 1200 | 5.5294 | - | |
| | 0.5556 | 1300 | 5.4836 | - | |
| | 0.5983 | 1400 | 5.4851 | - | |
| | 0.6410 | 1500 | 5.4646 | - | |
| | 0.6838 | 1600 | 5.4784 | - | |
| | 0.7265 | 1700 | 5.481 | - | |
| | 0.7692 | 1800 | 5.4923 | - | |
| | 0.8120 | 1900 | 5.4696 | - | |
| | 0.8547 | 2000 | 5.4932 | 0.4749 | |
| | 0.8974 | 2100 | 5.4752 | - | |
| | 0.9402 | 2200 | 5.459 | - | |
| | 0.9829 | 2300 | 5.4371 | - | |
| | 1.0256 | 2400 | 5.3701 | - | |
| | 1.0684 | 2500 | 5.3562 | - | |
| | 1.1111 | 2600 | 5.4101 | - | |
| | 1.1538 | 2700 | 5.3829 | - | |
| | 1.1966 | 2800 | 5.3687 | - | |
| | 1.2393 | 2900 | 5.36 | - | |
| | 1.2821 | 3000 | 5.3446 | 0.4725 | |
| | 1.3248 | 3100 | 5.3757 | - | |
| | 1.3675 | 3200 | 5.3821 | - | |
| | 1.4103 | 3300 | 5.3918 | - | |
| | 1.4530 | 3400 | 5.3083 | - | |
| | 1.4957 | 3500 | 5.3389 | - | |
| | 1.5385 | 3600 | 5.3037 | - | |
| | 1.5812 | 3700 | 5.3424 | - | |
| | 1.6239 | 3800 | 5.3383 | - | |
| | 1.6667 | 3900 | 5.3252 | - | |
| | 1.7094 | 4000 | 5.3358 | 0.4676 | |
| | 1.7521 | 4100 | 5.2704 | - | |
| | 1.7949 | 4200 | 5.3415 | - | |
| | 1.8376 | 4300 | 5.361 | - | |
| | 1.8803 | 4400 | 5.3654 | - | |
| | 1.9231 | 4500 | 5.3386 | - | |
| | 1.9658 | 4600 | 5.3392 | - | |
| | -1 | -1 | - | 0.4650 | |
| |
| |
| ### Framework Versions |
| - Python: 3.12.11 |
| - Sentence Transformers: 5.1.1 |
| - Transformers: 4.56.2 |
| - PyTorch: 2.8.0+cu126 |
| - Accelerate: 1.10.1 |
| - Datasets: 2.20.0 |
| - Tokenizers: 0.22.1 |
| |
| ## 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 |
| @article{10531646, |
| author={Huang, Xiang and Peng, Hao and Zou, Dongcheng and Liu, Zhiwei and Li, Jianxin and Liu, Kay and Wu, Jia and Su, Jianlin and Yu, Philip S.}, |
| journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, |
| title={CoSENT: Consistent Sentence Embedding via Similarity Ranking}, |
| year={2024}, |
| doi={10.1109/TASLP.2024.3402087} |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
| <!-- |
| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
| |
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| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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