--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:68541 - loss:EpochLossWrapper base_model: intfloat/multilingual-e5-large widget: - source_sentence: 'query: ACER 宏碁 SA243Y G0B 護眼螢幕(24型/FHD/120Hz/1ms/IPS)' sentences: - 'passage: 【尚朋堂】專業型電烤箱SO-459I' - 'passage: 【Acer 宏碁】KA242Y G0 24型護眼螢幕(23.8吋/FHD/120Hz/1ms/IPS/喇叭)' - 'passage: 台灣出貨 瑜珈墊 瑜伽墊(加厚20mm 贈送收納袋+綁帶 健身墊 SGS檢測瑜珈墊 NBR環保瑜珈墊 運動墊 15mm)' - source_sentence: 'query: Seagate 希捷 One Touch Hub 10TB 超大容量硬碟 (STLC10000400)' sentences: - 'passage: 【Pets Galaxy 珮慈星系】寵物推車 狗狗推車 貓咪推車 狗推車 寵物外出 貓推車 可拆可折疊 多貓多狗適用 透氣大空間 雙層寵物推車' - 'passage: 【SEAGATE 希捷】One Touch Hub 8TB 3.5吋外接硬碟(STLC8000400)' - 'passage: 【Nintendo 任天堂】預購 NS2 任天堂 Switch2《 薩爾達無雙 封印戰記 》中文一般版 遊戲片 11/6發售' - source_sentence: 'query: SONY 索尼 BRAVIA 3 75吋 X1 4K HDR Google TV 顯示器 Y-75S30' sentences: - 'passage: 【Panasonic 國際牌】★新版★日本製5-8坪 調光調色吸頂燈 經典白(新版LGC61201A09 非舊版LGC61101A09)' - 'passage: 【Logitech 羅技】K380s 跨平台藍牙鍵盤(石墨灰)' - 'passage: 【Panasonic 國際牌】75型4K HDR Google TV聯網顯示器 無視訊盒(TN-75W80BGT)' - source_sentence: 'query: ACER 宏碁 EK241Y G 護眼螢幕(24型/FHD/120Hz/1ms/IPS)' sentences: - 'passage: 【Acer 宏碁】E271 G0 電腦螢幕(27型/FHD/120Hz/5ms/IPS)' - 'passage: 【魔術靈】殺菌瞬潔馬桶清潔劑(500ml)' - 'passage: 【陳傑憲代言 TECO 東元】6L 一級能效除濕機(MD1233W)' - source_sentence: 'query: iRobot 【美國機器人】Roomba 105 Combo 掃拖機器人 送 Roomba Combo Essentail 掃拖機器人' sentences: - 'passage: 【CHANEL 香奈兒】ALLURE男性運動淡香水(50ml-國際航空版)' - 'passage: 【LG 樂金】家電速配15公斤+10公斤〔Wash & Dryer〕免曬衣乾衣機+WiFi蒸洗脫變頻滾筒洗衣機-白(WD-S15NW+WR' - 'passage: 【iRobot】特談 Roomba Combo Essential 掃拖機器人(18倍吸力/超薄8公分/3段吸力水量/電力110分)' pipeline_tag: sentence-similarity library_name: sentence-transformers --- # 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). 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:** 128 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity ### 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': 128, '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: iRobot 【美國機器人】Roomba 105 Combo 掃拖機器人 送 Roomba Combo Essentail 掃拖機器人', 'passage: 【iRobot】特談 Roomba Combo Essential 掃拖機器人(18倍吸力/超薄8公分/3段吸力水量/電力110分)', 'passage: 【CHANEL 香奈兒】ALLURE男性運動淡香水(50ml-國際航空版)', ] 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, 0.4818, 0.0740], # [0.4818, 1.0000, 0.1612], # [0.0740, 0.1612, 1.0000]]) ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 68,541 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|:---------------| | query: ATEX Lourdes MINI220口袋型筋膜按摩槍 AX-HX336 /不求人筋膜槍 | passage: 【HOME GYM CLUB】KH-320筋膜槍 按摩槍 舒緩壓力按摩槍 震動按摩槍 筋膜按摩槍 運動按摩器(筋膜槍 按摩槍 運動按摩器) | 0 | | query: QMAT 10mm厚瑜珈墊 台灣製(附贈瑜珈繩揹帶及收納拉鍊袋 雙面雙壓紋止滑) | passage: 【TAIMAT】吠陀天然橡膠瑜伽墊(台灣製造 附贈簡易揹帶) | 0 | | query: 數位相機 數位相機 隨身入門級拍照攝影 卡片機 旅遊便攜 高清自拍照相機 | passage: 【優品生活館】數碼相機(相機 數位相機 照相機 高清錄像 學生相機 家用相機 專業日本芯片) | 0 | * Loss: __main__.EpochLossWrapper ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 15 - `fp16`: True - `multi_dataset_batch_sampler`: round_robin #### 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`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 15 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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 - `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 - `project`: huggingface - `trackio_space_id`: trackio - `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`: no - `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`: True - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.4669 | 500 | 0.013 | | 0.9337 | 1000 | 0.0034 | | 1.0 | 1071 | - | ### Framework Versions - Python: 3.11.14 - Sentence Transformers: 5.1.1 - Transformers: 4.57.1 - PyTorch: 2.9.0+cu128 - Accelerate: 1.10.1 - Datasets: 4.2.0 - 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", } ```