--- base_model: intfloat/multilingual-e5-small language: - multilingual library_name: sentence-transformers license: apache-2.0 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 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:2320 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'MVGO; medium vacuum gas oil' sentences: - 과분해 - Medium Vacuum Gas Oil(MVGO) ; - '선적 전 또는 양하 후에 화물창에 잔존하는 소량의 액체화물 양을 결정하는 수학 적인 계산 수식' - source_sentence: PLE; plain large end sentences: - Plain Large End ; - '부하중 변압기 Tap 변환기 ; 변압기 권선의 Tap을 무정전으로 변경하는 장치' - Cone Roof Tank에서 Tank내의 Vapor가 외부로 나갈 수 있도록 만들어 놓은 구멍 - source_sentence: Fluidization sentences: - '핵심성과지표; 어떤 계획이나 목표가 성공하였는지 또는 성공하고 있는지를 확인하려면 그 성공 을 구성하는 요소들을 측정하는 지표를 찾아 측정하여야 하는데, 이들 지표 중 성 공을 확인할 수 있는 가장 결정적인 지표를 KPI라고 부릅니다.' - '전압변동에 영향을 주는 무효전력을 줄이기 위한 조상설비의 일종으로 정지형 무 효전력 보상장치' - 고체층을 액체나 기체로 확대시키거나 현탁시켜 유통하도록 하는 것 - source_sentence: 'SH; surface hardened steel body' sentences: - Surface Hardened Steel Body ; - 분산제 ; 슬러지 생성을 방지하기 위하여 Oil에 넣어주는 약품 - '작업위험성평가; 현장에서 수행되는 작업을 포함한 전반적인 직무 활동에 대하여 위험요인을 분석 하여 현재 안전조치를 검토하고 안전대책을 마련하는 기법' - source_sentence: U-205200 sentences: - 물속의 (-)ion을 OH-로 치환해 주는 이온교환수지탑 - 차단기, 스위치류 , 스위치 - 올레핀 송유/동력 Nitrogen Section model-index: - name: Multilingual base soil embedding model (quantized) results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.2441860465116279 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.31007751937984496 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3643410852713178 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4108527131782946 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2441860465116279 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.10335917312661498 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07286821705426358 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.041085271317829464 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2441860465116279 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.31007751937984496 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3643410852713178 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4108527131782946 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3172493867293268 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.28840746893072483 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3003133446683658 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.2054263565891473 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.28294573643410853 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3178294573643411 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.38372093023255816 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2054263565891473 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09431524547803617 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06356589147286822 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.03837209302325582 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2054263565891473 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.28294573643410853 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3178294573643411 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.38372093023255816 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2850988708112555 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.25465270087363123 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.26532412971784447 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.1937984496124031 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2713178294573643 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.29844961240310075 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.3488372093023256 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1937984496124031 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.0904392764857881 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.059689922480620154 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.03488372093023256 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1937984496124031 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2713178294573643 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.29844961240310075 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3488372093023256 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.26467320016495083 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2385474344776671 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2482312240959752 name: Cosine Map@100 --- # Multilingual base soil embedding model (quantized) 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) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity - **Language:** multilingual - **License:** apache-2.0 ### 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': 512, 'do_lower_case': False}) with Transformer model: 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("ValentinaKim/Multilingual-base-soil-embedding") # Run inference sentences = [ 'U-205200', '올레핀 송유/동력 Nitrogen Section', '차단기, 스위치류 , 스위치', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.2442 | | cosine_accuracy@3 | 0.3101 | | cosine_accuracy@5 | 0.3643 | | cosine_accuracy@10 | 0.4109 | | cosine_precision@1 | 0.2442 | | cosine_precision@3 | 0.1034 | | cosine_precision@5 | 0.0729 | | cosine_precision@10 | 0.0411 | | cosine_recall@1 | 0.2442 | | cosine_recall@3 | 0.3101 | | cosine_recall@5 | 0.3643 | | cosine_recall@10 | 0.4109 | | cosine_ndcg@10 | 0.3172 | | cosine_mrr@10 | 0.2884 | | **cosine_map@100** | **0.3003** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.2054 | | cosine_accuracy@3 | 0.2829 | | cosine_accuracy@5 | 0.3178 | | cosine_accuracy@10 | 0.3837 | | cosine_precision@1 | 0.2054 | | cosine_precision@3 | 0.0943 | | cosine_precision@5 | 0.0636 | | cosine_precision@10 | 0.0384 | | cosine_recall@1 | 0.2054 | | cosine_recall@3 | 0.2829 | | cosine_recall@5 | 0.3178 | | cosine_recall@10 | 0.3837 | | cosine_ndcg@10 | 0.2851 | | cosine_mrr@10 | 0.2547 | | **cosine_map@100** | **0.2653** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1938 | | cosine_accuracy@3 | 0.2713 | | cosine_accuracy@5 | 0.2984 | | cosine_accuracy@10 | 0.3488 | | cosine_precision@1 | 0.1938 | | cosine_precision@3 | 0.0904 | | cosine_precision@5 | 0.0597 | | cosine_precision@10 | 0.0349 | | cosine_recall@1 | 0.1938 | | cosine_recall@3 | 0.2713 | | cosine_recall@5 | 0.2984 | | cosine_recall@10 | 0.3488 | | cosine_ndcg@10 | 0.2647 | | cosine_mrr@10 | 0.2385 | | **cosine_map@100** | **0.2482** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 2,320 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:--------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Deionizer | 탈이온장치 ; Demineralizer와 동일 | | Sub-CC; sub-contracting
committee | 외주 계약의 투명성과 공정성을 확보하기 위한 Sub-계약위원회로서 위원 및 위원
장은 CEO가 임명한다. CC이원원 부문장 이상 임원으로 하고 간사는 구매관리팀
장이 한다. | | In-line Sampler | 원유 속의 물과 침전물의 함량을 측정하기 위하여 원유하역 Line에 설치해 놓은
시료채취기 | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 10 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `tf32`: False - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_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`: 10 - `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`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `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} - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_64_cosine_map@100 | |:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:| | 0.8767 | 4 | - | 0.2156 | 0.2448 | 0.1831 | | 1.9726 | 9 | - | 0.2511 | 0.2765 | 0.2154 | | 2.1918 | 10 | 7.6309 | - | - | - | | 2.8493 | 13 | - | 0.2531 | 0.2852 | 0.2345 | | 3.9452 | 18 | - | 0.2617 | 0.2914 | 0.2353 | | 4.3836 | 20 | 5.3042 | - | - | - | | 4.8219 | 22 | - | 0.2626 | 0.2946 | 0.2422 | | 5.9178 | 27 | - | 0.2629 | 0.2987 | 0.2481 | | 6.5753 | 30 | 4.2433 | - | - | - | | 6.7945 | 31 | - | 0.2684 | 0.2988 | 0.2495 | | 7.8904 | 36 | - | 0.2652 | 0.3003 | 0.2488 | | 8.7671 | 40 | 3.9117 | 0.2653 | 0.3003 | 0.2482 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.41.2 - PyTorch: 2.1.2+cu121 - Accelerate: 1.0.0 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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} } ```