Trendyol/TY-ecomm-embed-multilingual-base-v1.2.0

• Optimized for e-commerce semantic search
• Enhanced Turkish and multilingual query understanding
• Supports query rephrasing and paraphrase mining
• Robust for product tagging and attribute extraction
• Suitable for clustering and product categorization
• High-performance in semantic textual similarity
• 384-token input support
• 768-dimensional dense vector outputs
• Built-in cosine similarity for inference

Model Details

Model Description

• Model Type: Sentence Transformer

• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 768 dimensions
• Matryoshka Dimensions: 768, 512, 128
• Similarity Function: Cosine Similarity
• Training Datasets:
  • Multilingual and Turkish search terms
  • Turkish instruction datasets
  • Turkish summarization datasets
  • Turkish e-commerce rephrase datasets
  • Turkish question-answer pairs
  • and more!

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': 384, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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)

Bias, Risks and Limitations

• Domain bias: Performance may degrade for content outside the e-commerce or product-related domains, such as legal, medical, or highly technical texts.
• Language coverage: Although multilingual data was included, majority of the dataset is created in Turkish.
• Input length limitations: Inputs exceeding the maximum sequence length (384 tokens) will be truncated, potentially losing critical context in long texts.
• Spurious similarity: Semantic similarity may incorrectly assign high similarity scores to unrelated but lexically similar or frequently co-occurring phrases in training data.

Recommendations

• Human Oversight: We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
• Application-Specific Testing: Developers intending to use Trendyol embedding models should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s outputs may occasionally be biased or inaccurate.
• Responsible Development and Deployment: It is the responsibility of developers and users of Trendyol embedding models to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences.

Training Details

• Loss: MatryoshkaLoss with these parameters:
  json

  1{ 2 "loss": "CachedMultipleNegativesSymmetricRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 128 7 ], 8 "matryoshka_weights": [ 9 1, 10 1, 11 1 12 ], 13 "n_dims_per_step": -1 14}

Training Hyperparameters

Non-Default Hyperparameters

• overwrite_output_dir: True
• eval_strategy: steps
• per_device_train_batch_size: 2048
• per_device_eval_batch_size: 128
• learning_rate: 0.0005
• num_train_epochs: 1
• warmup_ratio: 0.01
• fp16: True
• ddp_timeout: 300000
• batch_sampler: no_duplicates

All Hyperparameters

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.5.1
• Datasets: 2.21.0
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss