Antix5/product-embed-multi-e5-small

multilingual-e5-small embeddings (CoSENTLoss on graded listwise pairs)

This is a sentence-transformers model finetuned from 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
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Language: multilingual
• License: mit

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]])

Evaluation

Metrics

Information Retrieval

• Dataset: ir_eval
• Evaluated with 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

Training Details

Training Dataset

Unnamed Dataset

• Size: 74,864 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 1000 samples:

  	text1	text2	label
  type	string	string	float
  details	min: 4 tokens mean: 19.67 tokens max: 54 tokens	min: 3 tokens mean: 15.59 tokens max: 72 tokens	min: 0.0 mean: 0.53 max: 1.0

• Samples:

  text1	text2	label
  Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch	Premier 26764 Tourbillon pour voiture, Santa, 25 x 19-1/2 pouces	1.0
  Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch	BNTS, ЧИПСЫ ИЗ ФАСОЛИ NV И МОРСКАЯ СОЛЬ	0.0
  Premier 26764 Car Spinner, Santa, 25 by 19-1/2-Inch	Beanitos, Чипс из фасоли navy, Сыр на чо	0.0

• Loss: CoSENTLoss with these parameters:

  {
      "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

Click to expand

• 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: {}

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

@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

@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}
}