README.md

---
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) <!-- 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: 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]])
```

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

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 68,541 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: 29.86 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 34.55 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>0: ~95.20%</li><li>1: ~4.80%</li></ul> |
* Samples:
  | sentence_0                                                        | sentence_1                                                                                  | label          |
  |:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|:---------------|
  | <code>query: ATEX Lourdes MINI220口袋型筋膜按摩槍 AX-HX336 /不求人筋膜槍</code> | <code>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)</code> | <code>0</code> |
  | <code>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)</code>      | <code>passage: 【TAIMAT】吠陀天然橡膠瑜伽墊(台灣製造 附贈簡易揹帶)</code>                                        | <code>0</code> |
  | <code>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</code>          | <code>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)</code>                        | <code>0</code> |
* Loss: <code>__main__.EpochLossWrapper</code>

### 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
<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`: 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`: {}

</details>

### 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",
}
```

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