---
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:561155
- loss:CosineSimilarityLoss
widget:
- source_sentence: 地下室那个主管道铁管子烂的不行了
  sentences:
  - 怎么停水了？
  - 怎么停水了？
  - 维修人员什么时候能到
- source_sentence: 什么时候能给加装过滤网啊，我都等好久了
  sentences:
  - 怎么停水了？
  - 您可以拨打营业大厅电话进行处理，电话号码是，824，777，6
  - 你好
- source_sentence: 咋没水了呢
  sentences:
  - 我家水管冻了
  - 怎么停水了？
  - 我家漏水了
- source_sentence: 你好我想问一下怎么在网上交不了费啊
  sentences:
  - 水表怎么安装和更换？
  - 查询用户编号需要营业大厅办理，请拨打8247776
  - 我家漏水了
- source_sentence: 大概几点来水？
  sentences:
  - 本人到供水公司办理，需携带房本复印件、本人身份证复印件到供水公司办理， 待办需携带房本复印件、房主身份证复印件、代办人身份证复印件
  - 线上报装登录佳木斯供水公众号， 点击“报装申请”，提交后，工作人员会在24小时内联系您。线下报装需本人到供水公司申请，居民用户携带本人身份证复印件和房照复印件，非居民用户携带营业执照复印件、法人复印件及公章。
  - 我家漏水了
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## 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 = [
    '大概几点来水？',
    '本人到供水公司办理，需携带房本复印件、本人身份证复印件到供水公司办理， 待办需携带房本复印件、房主身份证复印件、代办人身份证复印件',
    '我家漏水了',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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</details>
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You can finetune this model on your own dataset.

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

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 561,155 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                                                                             | float                                                         |
  | details | <ul><li>min: 3 tokens</li><li>mean: 14.72 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 20.05 tokens</li><li>max: 107 tokens</li></ul> | <ul><li>min: 0.3</li><li>mean: 0.3</li><li>max: 0.7</li></ul> |
* Samples:
  | sentence_0                               | sentence_1                                                  | label            |
  |:-----------------------------------------|:------------------------------------------------------------|:-----------------|
  | <code>查询一下我的户号</code>                    | <code>我要投诉</code>                                           | <code>0.3</code> |
  | <code>XX现在正在漏水，不确定是供水还是供热，方便去看一下吗</code> | <code>怎么停水了？</code>                                         | <code>0.3</code> |
  | <code>我家突然没有水压了，滴答水</code>               | <code>居民用水2.95元/吨，非居民请登录微信公众号佳木斯供水-网上营业厅-业务办理-水价信息查寻</code> | <code>0.3</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 3
- `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
- `use_ipex`: False
- `bf16`: False
- `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}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step   | Training Loss |
|:------:|:------:|:-------------:|
| 0.0143 | 500    | 0.2112        |
| 0.0285 | 1000   | 0.1787        |
| 0.0428 | 1500   | 0.1278        |
| 0.0570 | 2000   | 0.0723        |
| 0.0713 | 2500   | 0.0294        |
| 0.0855 | 3000   | 0.0126        |
| 0.0998 | 3500   | 0.0074        |
| 0.1140 | 4000   | 0.0053        |
| 0.1283 | 4500   | 0.0043        |
| 0.1426 | 5000   | 0.0036        |
| 0.1568 | 5500   | 0.0031        |
| 0.1711 | 6000   | 0.0029        |
| 0.1853 | 6500   | 0.0028        |
| 0.1996 | 7000   | 0.0026        |
| 0.2138 | 7500   | 0.0024        |
| 0.2281 | 8000   | 0.0022        |
| 0.2424 | 8500   | 0.002         |
| 0.2566 | 9000   | 0.002         |
| 0.2709 | 9500   | 0.0019        |
| 0.2851 | 10000  | 0.0019        |
| 0.2994 | 10500  | 0.002         |
| 0.3136 | 11000  | 0.0017        |
| 0.3279 | 11500  | 0.0018        |
| 0.3421 | 12000  | 0.0017        |
| 0.3564 | 12500  | 0.0018        |
| 0.3707 | 13000  | 0.0014        |
| 0.3849 | 13500  | 0.0014        |
| 0.3992 | 14000  | 0.0014        |
| 0.4134 | 14500  | 0.0016        |
| 0.4277 | 15000  | 0.0013        |
| 0.4419 | 15500  | 0.0013        |
| 0.4562 | 16000  | 0.0012        |
| 0.4704 | 16500  | 0.0012        |
| 0.4847 | 17000  | 0.0013        |
| 0.4990 | 17500  | 0.0014        |
| 0.5132 | 18000  | 0.0011        |
| 0.5275 | 18500  | 0.0012        |
| 0.5417 | 19000  | 0.0011        |
| 0.5560 | 19500  | 0.0011        |
| 0.5702 | 20000  | 0.0009        |
| 0.5845 | 20500  | 0.0011        |
| 0.5988 | 21000  | 0.0009        |
| 0.6130 | 21500  | 0.001         |
| 0.6273 | 22000  | 0.001         |
| 0.6415 | 22500  | 0.0009        |
| 0.6558 | 23000  | 0.0011        |
| 0.6700 | 23500  | 0.0009        |
| 0.6843 | 24000  | 0.0011        |
| 0.6985 | 24500  | 0.0008        |
| 0.7128 | 25000  | 0.0008        |
| 0.7271 | 25500  | 0.0007        |
| 0.7413 | 26000  | 0.0007        |
| 0.7556 | 26500  | 0.0007        |
| 0.7698 | 27000  | 0.0007        |
| 0.7841 | 27500  | 0.0008        |
| 0.7983 | 28000  | 0.0007        |
| 0.8126 | 28500  | 0.0006        |
| 0.8268 | 29000  | 0.0008        |
| 0.8411 | 29500  | 0.0007        |
| 0.8554 | 30000  | 0.0006        |
| 0.8696 | 30500  | 0.0007        |
| 0.8839 | 31000  | 0.0005        |
| 0.8981 | 31500  | 0.0007        |
| 0.9124 | 32000  | 0.0006        |
| 0.9266 | 32500  | 0.0006        |
| 0.9409 | 33000  | 0.0006        |
| 0.9552 | 33500  | 0.0005        |
| 0.9694 | 34000  | 0.0006        |
| 0.9837 | 34500  | 0.0006        |
| 0.9979 | 35000  | 0.0006        |
| 1.0122 | 35500  | 0.0006        |
| 1.0264 | 36000  | 0.0005        |
| 1.0407 | 36500  | 0.0004        |
| 1.0549 | 37000  | 0.0007        |
| 1.0692 | 37500  | 0.0005        |
| 1.0835 | 38000  | 0.0005        |
| 1.0977 | 38500  | 0.0004        |
| 1.1120 | 39000  | 0.0005        |
| 1.1262 | 39500  | 0.0005        |
| 1.1405 | 40000  | 0.0004        |
| 1.1547 | 40500  | 0.0005        |
| 1.1690 | 41000  | 0.0004        |
| 1.1832 | 41500  | 0.0005        |
| 1.1975 | 42000  | 0.0005        |
| 1.2118 | 42500  | 0.0005        |
| 1.2260 | 43000  | 0.0005        |
| 1.2403 | 43500  | 0.0004        |
| 1.2545 | 44000  | 0.0005        |
| 1.2688 | 44500  | 0.0005        |
| 1.2830 | 45000  | 0.0004        |
| 1.2973 | 45500  | 0.0005        |
| 1.3116 | 46000  | 0.0006        |
| 1.3258 | 46500  | 0.0004        |
| 1.3401 | 47000  | 0.0004        |
| 1.3543 | 47500  | 0.0004        |
| 1.3686 | 48000  | 0.0004        |
| 1.3828 | 48500  | 0.0003        |
| 1.3971 | 49000  | 0.0003        |
| 1.4113 | 49500  | 0.0004        |
| 1.4256 | 50000  | 0.0004        |
| 1.4399 | 50500  | 0.0004        |
| 1.4541 | 51000  | 0.0004        |
| 1.4684 | 51500  | 0.0003        |
| 1.4826 | 52000  | 0.0003        |
| 1.4969 | 52500  | 0.0006        |
| 1.5111 | 53000  | 0.0004        |
| 1.5254 | 53500  | 0.0004        |
| 1.5396 | 54000  | 0.0003        |
| 1.5539 | 54500  | 0.0003        |
| 1.5682 | 55000  | 0.0003        |
| 1.5824 | 55500  | 0.0004        |
| 1.5967 | 56000  | 0.0004        |
| 1.6109 | 56500  | 0.0004        |
| 1.6252 | 57000  | 0.0003        |
| 1.6394 | 57500  | 0.0003        |
| 1.6537 | 58000  | 0.0003        |
| 1.6679 | 58500  | 0.0004        |
| 1.6822 | 59000  | 0.0005        |
| 1.6965 | 59500  | 0.0004        |
| 1.7107 | 60000  | 0.0003        |
| 1.7250 | 60500  | 0.0004        |
| 1.7392 | 61000  | 0.0002        |
| 1.7535 | 61500  | 0.0003        |
| 1.7677 | 62000  | 0.0003        |
| 1.7820 | 62500  | 0.0003        |
| 1.7963 | 63000  | 0.0003        |
| 1.8105 | 63500  | 0.0003        |
| 1.8248 | 64000  | 0.0003        |
| 1.8390 | 64500  | 0.0003        |
| 1.8533 | 65000  | 0.0002        |
| 1.8675 | 65500  | 0.0004        |
| 1.8818 | 66000  | 0.0002        |
| 1.8960 | 66500  | 0.0002        |
| 1.9103 | 67000  | 0.0002        |
| 1.9246 | 67500  | 0.0003        |
| 1.9388 | 68000  | 0.0003        |
| 1.9531 | 68500  | 0.0003        |
| 1.9673 | 69000  | 0.0002        |
| 1.9816 | 69500  | 0.0003        |
| 1.9958 | 70000  | 0.0003        |
| 2.0101 | 70500  | 0.0003        |
| 2.0243 | 71000  | 0.0003        |
| 2.0386 | 71500  | 0.0002        |
| 2.0529 | 72000  | 0.0004        |
| 2.0671 | 72500  | 0.0003        |
| 2.0814 | 73000  | 0.0003        |
| 2.0956 | 73500  | 0.0002        |
| 2.1099 | 74000  | 0.0002        |
| 2.1241 | 74500  | 0.0003        |
| 2.1384 | 75000  | 0.0002        |
| 2.1527 | 75500  | 0.0003        |
| 2.1669 | 76000  | 0.0002        |
| 2.1812 | 76500  | 0.0003        |
| 2.1954 | 77000  | 0.0002        |
| 2.2097 | 77500  | 0.0002        |
| 2.2239 | 78000  | 0.0003        |
| 2.2382 | 78500  | 0.0001        |
| 2.2524 | 79000  | 0.0002        |
| 2.2667 | 79500  | 0.0003        |
| 2.2810 | 80000  | 0.0003        |
| 2.2952 | 80500  | 0.0002        |
| 2.3095 | 81000  | 0.0003        |
| 2.3237 | 81500  | 0.0003        |
| 2.3380 | 82000  | 0.0002        |
| 2.3522 | 82500  | 0.0002        |
| 2.3665 | 83000  | 0.0003        |
| 2.3807 | 83500  | 0.0003        |
| 2.3950 | 84000  | 0.0002        |
| 2.4093 | 84500  | 0.0002        |
| 2.4235 | 85000  | 0.0002        |
| 2.4378 | 85500  | 0.0002        |
| 2.4520 | 86000  | 0.0002        |
| 2.4663 | 86500  | 0.0002        |
| 2.4805 | 87000  | 0.0002        |
| 2.4948 | 87500  | 0.0004        |
| 2.5091 | 88000  | 0.0003        |
| 2.5233 | 88500  | 0.0003        |
| 2.5376 | 89000  | 0.0001        |
| 2.5518 | 89500  | 0.0002        |
| 2.5661 | 90000  | 0.0002        |
| 2.5803 | 90500  | 0.0002        |
| 2.5946 | 91000  | 0.0002        |
| 2.6088 | 91500  | 0.0002        |
| 2.6231 | 92000  | 0.0002        |
| 2.6374 | 92500  | 0.0002        |
| 2.6516 | 93000  | 0.0002        |
| 2.6659 | 93500  | 0.0003        |
| 2.6801 | 94000  | 0.0005        |
| 2.6944 | 94500  | 0.0003        |
| 2.7086 | 95000  | 0.0003        |
| 2.7229 | 95500  | 0.0003        |
| 2.7371 | 96000  | 0.0002        |
| 2.7514 | 96500  | 0.0002        |
| 2.7657 | 97000  | 0.0001        |
| 2.7799 | 97500  | 0.0002        |
| 2.7942 | 98000  | 0.0002        |
| 2.8084 | 98500  | 0.0003        |
| 2.8227 | 99000  | 0.0001        |
| 2.8369 | 99500  | 0.0002        |
| 2.8512 | 100000 | 0.0002        |
| 2.8655 | 100500 | 0.0002        |
| 2.8797 | 101000 | 0.0001        |
| 2.8940 | 101500 | 0.0002        |
| 2.9082 | 102000 | 0.0002        |
| 2.9225 | 102500 | 0.0002        |
| 2.9367 | 103000 | 0.0003        |
| 2.9510 | 103500 | 0.0002        |
| 2.9652 | 104000 | 0.0002        |
| 2.9795 | 104500 | 0.0003        |
| 2.9938 | 105000 | 0.0003        |

</details>

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.45.0.dev0
- PyTorch: 2.2.1
- Accelerate: 0.31.0
- Datasets: 2.17.1
- 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",
}
```

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