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Antix5
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product-embed-multi-e5-small

Sentence Similarity
sentence-transformers
Safetensors
multilingual
bert
feature-extraction
dense
Generated from Trainer
dataset_size:74864
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
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product-embed-multi-e5-small / README.md
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 ---
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]])
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## 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
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### Recommendations
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## 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}
}
```
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