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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:69363
	- loss:EpochLossWrapper
	base_model: intfloat/multilingual-e5-large
	widget:
	- source_sentence: 'query: MSI 微星 MSI PRO MP165 E6 可攜式螢幕 (16型/FHD/Type C/喇叭/IPS)+SPACE
	LED觸控護眼三色螢幕掛燈 33cm款'
	sentences:
	- 'passage: 六合一清潔組【Apple】AirPods 4'
	- 'passage: 【MSI 微星】PRO MP251 E2 25型平面商用螢幕(FHD/IPS/120Hz/DP+HDMI+D-Sub/眼部護理技術)'
	- 'passage: 【Apple】Apple Watch Series 11 GPS+行動網路 42mm(鋁金屬錶殼搭配運動型錶帶)'
	- source_sentence: 'query: DAIKIN 大金 7坪閃流放電空氣清淨機 MC30YSCT'
	sentences:
	- 'passage: 三合一充電座組【Apple】Apple Watch Ultra 3 GPS+行動網路 49mm(鈦金屬錶殼搭配越野錶環)'
	- 'passage: 【LG 樂金】58H快配17公斤◆AI DD™智慧直驅變頻洗衣機 ◆曜石黑(WT-VDN17M)'
	- 'passage: 【LG 樂金】PuriCare Hit 一級能效超淨化大白抗敏空氣清淨機適用18坪 / 台 AS601HWG0'
	- source_sentence: 'query: Logitech 羅技 G304 LIGHTSPEED 無線電競遊戲滑鼠黑色'
	sentences:
	- 'passage: 【grantclassic】6入濾心套餐組喝不停 AquaLux 寵物智能陶瓷飲水機 + 6入專用濾心(2L/官方品牌館)'
	- 'passage: 【Logitech 羅技】M190無線滑鼠(黑色)'
	- 'passage: 【beyerdynamic】DT 71 IE 鼓組&貝斯入耳式監聽耳機 #劇院視聽 #HIFI #高解析音質 #有線耳機(公司貨保證)'
	- source_sentence: 'query: KINTO / TRAVEL TUMBLER 隨行保溫瓶500ml-灰'
	sentences:
	- 'passage: 【小不記】1080P旗艦款-多媒體投影儀 4K畫質智慧投影機 LED鏡像同屏(家用投影機露營投影機投影機)'
	- 'passage: 手提陶瓷咖啡保溫杯500ml(內附吸管/五色任選/保冰杯/保溫杯/減塑/保溫瓶)'
	- 'passage: 【技嘉】顯卡+電源組合★ RTX 5080 GAMING OC 16G 顯示卡+海韻 ATX3 Core GX-850 金牌電源供應器'
	- source_sentence: 'query: PHILIPS 飛利浦 Sonicare 智慧感應型電動牙刷新月白 (HX6877/27)'
	sentences:
	- 'passage: 【MSI 微星】MSI 微星 MAG 274UPDF E16M 電競螢幕 27吋 160Hz 4k IPS 0.5ms HDR 可旋轉'
	- 'passage: 【SHARP 夏普】12公升自動除菌離子除濕機(DW-LT12ST-W)'
	- 'passage: 【Philips 飛利浦】官方直Sonicare抗敏Pro 鑽石智能音波電動牙刷/護敏刷HX3892/02(雙入組)'
	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) <!-- at revision 0dc5580a448e4284468b8909bae50fa925907bc5 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### 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: PHILIPS 飛利浦 Sonicare 智慧感應型電動牙刷新月白 (HX6877/27)',
	'passage: 【Philips 飛利浦】官方直Sonicare抗敏Pro 鑽石智能音波電動牙刷/護敏刷HX3892/02(雙入組)',
	'passage: 【SHARP 夏普】12公升自動除菌離子除濕機(DW-LT12ST-W)',
	]
	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, 0.1586, 0.0201],
	# [0.1586, 1.0000, 0.1386],
	# [0.0201, 0.1386, 1.0000]])
	```

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	### Downstream Usage (Sentence Transformers)

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	<details><summary>Click to expand</summary>

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 69,363 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 30.67 tokens</li><li>max: 86 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 35.13 tokens</li><li>max: 61 tokens</li></ul> \| <ul><li>0: ~95.50%</li><li>1: ~4.50%</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:--------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------\|:---------------\|
	\| <code>query: Logitech 羅技 M221 靜音無線滑鼠-黑</code> \| <code>passage: 【Logitech 羅技】Pebble M350s 無線藍牙滑鼠 - 珍珠白(不附接收器)</code> \| <code>0</code> \|
	\| <code>query: Kleenex 舒潔萬用輕巧包抽取衛生紙(110+20抽x20包/串)</code> \| <code>passage: 【Kleenex 舒潔】棉柔舒適抽取衛生紙110抽*60包</code> \| <code>0</code> \|
	\| <code>query: Apple 蘋果 Apple Watch Series 11 GPS + LTE 46mm 玫瑰金色鋁金屬錶殼搭配淡胭粉色運動錶帶</code> \| <code>passage: 【Apple】Apple Watch S11 GPS + 行動網路 46mm(鋁金屬錶殼搭配運動型錶帶)</code> \| <code>0</code> \|
	* Loss: <code>__main__.EpochLossWrapper</code>

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `num_train_epochs`: 15
	- `fp16`: True
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `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`: 32
	- `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`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 0.2306 \| 500 \| 0.0168 \|
	\| 0.4613 \| 1000 \| 0.0042 \|
	\| 0.6919 \| 1500 \| 0.0033 \|
	\| 0.9225 \| 2000 \| 0.0033 \|
	\| 1.0 \| 2168 \| - \|


	### 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",
	}
	```

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