--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:43318 - loss:MultipleNegativesRankingLoss base_model: intfloat/multilingual-e5-large widget: - source_sentence: 'query: 3PL 사용 시의 비용 절감 메커니즘은 어떤 것이 있나요?' sentences: - 'passage: 3 Dimension-Through Silicon Via (Technical)' - 'passage: Third Party Logistics (상업)' - 'passage: Authorization Account Answer (Technical)' - source_sentence: 'query: How can ACE be utilized?' sentences: - 'passage: Audio Connecting Equipment (Applicational)' - 'passage: Access Class-Barring (활용)' - 'passage: Abort Accept (기술)' - source_sentence: 'query: What makes the 1x RTT technology significant?' sentences: - 'passage: Ab Wire Test (Conceptual)' - 'passage: CDMA2000 1x Radio Transmission Technology (Conceptual)' - 'passage: Authentication, Authorization, Accounting (기술)' - source_sentence: 'query: 2WPD의 전력 분배 방식은 어떻게 이루어지나요?' sentences: - 'passage: Triple Digital Encryption Standard (기술)' - 'passage: Air Baffle (Conceptual)' - 'passage: 2 Way Power Divider (기술)' - source_sentence: 'query: 3D-TSV가 반도체 설계에 미치는 영향은 무엇인가요?' sentences: - 'passage: Available Bit Rate (Applicational)' - 'passage: Average Bouncing Busy Hour (개념)' - 'passage: 3 Dimension-Through Silicon Via (기술)' 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@100 model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-large results: - task: type: information-retrieval name: Information Retrieval dataset: name: e5 eval real type: e5-eval-real metrics: - type: cosine_accuracy@1 value: 0.9683 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9981 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9997 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9999 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9683 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3326999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.19994000000000006 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9683 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9981 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9997 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9999 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9873905751741222 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9830366666666664 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9830414285714285 name: Cosine Map@100 --- # 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) on the train dataset. 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) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - train ### 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': '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("sentence_transformers_model_id") # Run inference sentences = [ 'query: 3D-TSV가 반도체 설계에 미치는 영향은 무엇인가요?', 'passage: 3 Dimension-Through Silicon Via (기술)', 'passage: Available Bit Rate (Applicational)', ] 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, 0.8098, -0.1741], # [ 0.8098, 1.0000, -0.2449], # [-0.1741, -0.2449, 1.0000]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `e5-eval-real` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.9683 | | cosine_accuracy@3 | 0.9981 | | cosine_accuracy@5 | 0.9997 | | cosine_accuracy@10 | 0.9999 | | cosine_precision@1 | 0.9683 | | cosine_precision@3 | 0.3327 | | cosine_precision@5 | 0.1999 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 0.9683 | | cosine_recall@3 | 0.9981 | | cosine_recall@5 | 0.9997 | | cosine_recall@10 | 0.9999 | | **cosine_ndcg@10** | **0.9874** | | cosine_mrr@10 | 0.983 | | cosine_map@100 | 0.983 | ## Training Details ### Training Dataset #### train * Dataset: train * Size: 43,318 training samples * Columns: 0 and 1 * Approximate statistics based on the first 1000 samples: | | 0 | 1 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | 0 | 1 | |:------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------| | query: ABPL은 ATM의 기초 속도를 지원하는 물리 계층 장치로 어떻게 구성되나요? | passage: ATM Base Rate Physical Layer Unit (기술) | | query: How is the ABPL configured as a physical layer unit supporting the ATM base rate? | passage: ATM Base Rate Physical Layer Unit (Technical) | | query: ABPL의 역할은 ATM 네트워크에서 무엇을 의미하나요? | passage: ATM Base Rate Physical Layer Unit (개념) | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 1e-05 - `weight_decay`: 0.01 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: 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`: 64 - `per_device_eval_batch_size`: 64 - `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`: 1e-05 - `weight_decay`: 0.01 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `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`: True - `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`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | e5-eval-real_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------------------:| | 0.0015 | 1 | 2.8346 | - | | 0.1477 | 100 | 1.1145 | - | | 0.2954 | 200 | 0.0332 | 0.9633 | | 0.4431 | 300 | 0.0185 | - | | 0.5908 | 400 | 0.0154 | 0.9782 | | 0.7386 | 500 | 0.0116 | - | | 0.8863 | 600 | 0.0107 | 0.9810 | | 1.0340 | 700 | 0.0078 | - | | 1.1817 | 800 | 0.0076 | 0.9830 | | 1.3294 | 900 | 0.0045 | - | | 1.4771 | 1000 | 0.0043 | 0.9851 | | 1.6248 | 1100 | 0.0034 | - | | 1.7725 | 1200 | 0.0037 | 0.9862 | | 1.9202 | 1300 | 0.0031 | - | | 2.0679 | 1400 | 0.0034 | 0.9870 | | 2.2157 | 1500 | 0.0029 | - | | 2.3634 | 1600 | 0.0025 | 0.9872 | | 2.5111 | 1700 | 0.0027 | - | | 2.6588 | 1800 | 0.0022 | 0.9875 | | 2.8065 | 1900 | 0.0027 | - | | 2.9542 | 2000 | 0.0025 | 0.9875 | | -1 | -1 | - | 0.9874 | ### 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: 3.6.0 - 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```