---
base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:756057
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 府君奈何以盖世之才欲立忠于垂亡之国
  sentences:
  - 将远方进贡来的奇兽飞禽以及白山鸡等物纵还山林比起雍畤的祭祀礼数颇有增加
  - 您为什么以盖绝当世的奇才却打算向这个面临灭亡的国家尽效忠心呢
  - 大统年间他出任岐州刺史在任不久就因为能力强而闻名
- source_sentence: 将率既至授单于印绂诏令上故印绂
  sentences:
  - 已经到达的五威将到达后授给单于新印信宣读诏书要求交回汉朝旧印信
  - 于是拜陶隗为西南面招讨使
  - 司马错建议秦惠王攻打蜀国张仪说 还不如进攻韩国
- source_sentence: 行醮礼皇太子诣醴席乐作
  sentences:
  - 闰七月十七日上宣宗废除皇后胡氏尊谥
  - 等到看见西羌鼠窃狗盗父不父子不子君臣没有分别四夷之人西羌最为低下
  - 行醮礼皇太子来到酒醴席奏乐
- source_sentence: 领军臧盾太府卿沈僧果等并被时遇孝绰尤轻之
  sentences:
  - 过了几天太宰官又来要国书并且说 我国自太宰府以东上国使臣没有到过今大朝派使臣来若不见国书何以相信
  - 所以丹阳葛洪解释说浑天仪注说 天体像鸡蛋地就像是鸡蛋中的蛋黄独处于天体之内天是大的而地是小的
  - 领军臧盾太府卿沈僧果等都是因赶上时机而得到官职的孝绰尤其轻蔑他们每次在朝中集合会面虽然一起做官但从不与他们说话
- source_sentence: 九月辛未太祖曾孙舒国公从式进封安定郡王
  sentences:
  - 九月初二太祖曾孙舒国公从式进封安定郡王
  - 杨难当在汉中大肆烧杀抢劫然后率众离开了汉中向西返回仇池留下赵温据守梁州又派他的魏兴太守薛健屯驻黄金山
  - 正统元年普定蛮夷阿迟等反叛非法称王四处出击攻打掠夺
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **Maximum Sequence Length:** 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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})
  (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 = [
    '九月辛未太祖曾孙舒国公从式进封安定郡王',
    '九月初二太祖曾孙舒国公从式进封安定郡王',
    '杨难当在汉中大肆烧杀抢劫然后率众离开了汉中向西返回仇池留下赵温据守梁州又派他的魏兴太守薛健屯驻黄金山',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 756,057 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 20.76 tokens</li><li>max: 199 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 31.27 tokens</li><li>max: 384 tokens</li></ul> |
* Samples:
  | anchor                                    | positive                                                    |
  |:------------------------------------------|:------------------------------------------------------------|
  | <code>虏怀兼弱之威挟广地之计强兵大众亲自凌殄旍鼓弥年矢石不息</code>  | <code>魏人怀有兼并弱小的威严胸藏拓展土地的计谋强人的军队亲自出征侵逼消灭旌旗战鼓连年出动战事不停息</code> |
  | <code>孟子曰 以善服人者未有能服人者也以善养人然后能服天下</code>   | <code>孟子说 用自己的善良使人们服从的人没有能使人服从的用善良影响教导人们才能使天下的人们都信服</code>  |
  | <code>开庆初大元兵渡江理宗议迁都平江庆元后谏不可恐摇动民心乃止</code> | <code>开庆初年大元朝部队渡过长江理宗打算迁都到平江庆元皇后劝谏不可迁都深恐动摇民心理宗才作罢</code>    |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 84,007 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                           |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                             |
  | details | <ul><li>min: 4 tokens</li><li>mean: 20.23 tokens</li><li>max: 138 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 31.42 tokens</li><li>max: 384 tokens</li></ul> |
* Samples:
  | anchor                                            | positive                                                          |
  |:--------------------------------------------------|:------------------------------------------------------------------|
  | <code>雒阳户五万二千八百三十九</code>                         | <code>雒阳有五万二千八百三十九户</code>                                        |
  | <code>拜南青州刺史在任有政绩</code>                          | <code>任南青州刺史很有政绩</code>                                           |
  | <code>第六品以下加不得服金钅奠绫锦锦绣七缘绮貂豽裘金叉环铒及以金校饰器物张绛帐</code> | <code>官位在第六品以下的官员再增加不得穿用金钿绫锦锦绣七缘绮貂钠皮衣金叉缳饵以及用金装饰的器物张绛帐等衣服物品</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `batch_sampler`: no_duplicates

#### 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`: 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
- `learning_rate`: 5e-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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: 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`: True
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch      | Step      | Training Loss | loss     |
|:----------:|:---------:|:-------------:|:--------:|
| 0.0021     | 100       | 0.6475        | -        |
| 0.0042     | 200       | 0.5193        | -        |
| 0.0063     | 300       | 0.4132        | -        |
| 0.0085     | 400       | 0.3981        | -        |
| 0.0106     | 500       | 0.4032        | -        |
| 0.0127     | 600       | 0.3627        | -        |
| 0.0148     | 700       | 0.3821        | -        |
| 0.0169     | 800       | 0.3767        | -        |
| 0.0190     | 900       | 0.3731        | -        |
| 0.0212     | 1000      | 0.3744        | -        |
| 0.0233     | 1100      | 0.3115        | -        |
| 0.0254     | 1200      | 0.3998        | -        |
| 0.0275     | 1300      | 0.3103        | -        |
| 0.0296     | 1400      | 0.3251        | -        |
| 0.0317     | 1500      | 0.2833        | -        |
| 0.0339     | 1600      | 0.3335        | -        |
| 0.0360     | 1700      | 0.3281        | -        |
| 0.0381     | 1800      | 0.423         | -        |
| 0.0402     | 1900      | 0.3687        | -        |
| 0.0423     | 2000      | 0.3452        | -        |
| 0.0444     | 2100      | 0.8643        | -        |
| 0.0466     | 2200      | 0.4279        | -        |
| 0.0487     | 2300      | 0.4188        | -        |
| 0.0508     | 2400      | 0.3676        | -        |
| 0.0529     | 2500      | 0.3279        | -        |
| 0.0550     | 2600      | 0.3415        | -        |
| 0.0571     | 2700      | 1.5834        | -        |
| 0.0593     | 2800      | 2.7778        | -        |
| 0.0614     | 2900      | 2.7734        | -        |
| 0.0635     | 3000      | 2.7732        | -        |
| 0.0656     | 3100      | 2.7751        | -        |
| 0.0677     | 3200      | 2.7731        | -        |
| 0.0698     | 3300      | 2.773         | -        |
| 0.0720     | 3400      | 2.7727        | -        |
| 0.0741     | 3500      | 2.7534        | -        |
| 0.0762     | 3600      | 2.2219        | -        |
| 0.0783     | 3700      | 0.5137        | -        |
| 0.0804     | 3800      | 0.4143        | -        |
| 0.0825     | 3900      | 0.4002        | -        |
| 0.0846     | 4000      | 0.368         | -        |
| 0.0868     | 4100      | 0.3879        | -        |
| 0.0889     | 4200      | 0.3519        | -        |
| 0.0910     | 4300      | 0.364         | -        |
| 0.0931     | 4400      | 0.3618        | -        |
| 0.0952     | 4500      | 0.3545        | -        |
| 0.0973     | 4600      | 0.379         | -        |
| 0.0995     | 4700      | 0.3837        | -        |
| 0.1016     | 4800      | 0.3553        | -        |
| 0.1037     | 4900      | 0.3519        | -        |
| 0.1058     | 5000      | 0.3416        | 0.3487   |
| 0.1079     | 5100      | 0.3763        | -        |
| 0.1100     | 5200      | 0.3748        | -        |
| 0.1122     | 5300      | 0.3564        | -        |
| 0.1143     | 5400      | 0.336         | -        |
| 0.1164     | 5500      | 0.3601        | -        |
| 0.1185     | 5600      | 0.3521        | -        |
| 0.1206     | 5700      | 0.376         | -        |
| 0.1227     | 5800      | 0.3011        | -        |
| 0.1249     | 5900      | 0.345         | -        |
| 0.1270     | 6000      | 0.3211        | -        |
| 0.1291     | 6100      | 0.3673        | -        |
| 0.1312     | 6200      | 0.3762        | -        |
| 0.1333     | 6300      | 0.3562        | -        |
| 0.1354     | 6400      | 0.2761        | -        |
| 0.1376     | 6500      | 0.3186        | -        |
| 0.1397     | 6600      | 0.3582        | -        |
| 0.1418     | 6700      | 0.3454        | -        |
| 0.1439     | 6800      | 0.3429        | -        |
| 0.1460     | 6900      | 0.2932        | -        |
| 0.1481     | 7000      | 0.3357        | -        |
| 0.1503     | 7100      | 0.2979        | -        |
| 0.1524     | 7200      | 0.313         | -        |
| 0.1545     | 7300      | 0.3364        | -        |
| 0.1566     | 7400      | 0.3459        | -        |
| 0.1587     | 7500      | 0.279         | -        |
| 0.1608     | 7600      | 0.3274        | -        |
| 0.1629     | 7700      | 0.3367        | -        |
| 0.1651     | 7800      | 0.2935        | -        |
| 0.1672     | 7900      | 0.3415        | -        |
| 0.1693     | 8000      | 0.2838        | -        |
| 0.1714     | 8100      | 0.2667        | -        |
| 0.1735     | 8200      | 0.3051        | -        |
| 0.1756     | 8300      | 0.3197        | -        |
| 0.1778     | 8400      | 0.3086        | -        |
| 0.1799     | 8500      | 0.3186        | -        |
| 0.1820     | 8600      | 0.3063        | -        |
| 0.1841     | 8700      | 0.2967        | -        |
| 0.1862     | 8800      | 0.3069        | -        |
| 0.1883     | 8900      | 0.3391        | -        |
| 0.1905     | 9000      | 0.335         | -        |
| 0.1926     | 9100      | 0.3115        | -        |
| 0.1947     | 9200      | 0.3214        | -        |
| 0.1968     | 9300      | 0.278         | -        |
| 0.1989     | 9400      | 0.2833        | -        |
| 0.2010     | 9500      | 0.303         | -        |
| 0.2032     | 9600      | 0.3238        | -        |
| 0.2053     | 9700      | 0.2622        | -        |
| 0.2074     | 9800      | 0.3295        | -        |
| 0.2095     | 9900      | 0.2699        | -        |
| 0.2116     | 10000     | 0.2426        | 0.2962   |
| 0.2137     | 10100     | 0.262         | -        |
| 0.2159     | 10200     | 0.3199        | -        |
| 0.2180     | 10300     | 0.3677        | -        |
| 0.2201     | 10400     | 0.2423        | -        |
| 0.2222     | 10500     | 0.3446        | -        |
| 0.2243     | 10600     | 0.3002        | -        |
| 0.2264     | 10700     | 0.2863        | -        |
| 0.2286     | 10800     | 0.2692        | -        |
| 0.2307     | 10900     | 0.3157        | -        |
| 0.2328     | 11000     | 0.3172        | -        |
| 0.2349     | 11100     | 0.3622        | -        |
| 0.2370     | 11200     | 0.3019        | -        |
| 0.2391     | 11300     | 0.2789        | -        |
| 0.2412     | 11400     | 0.2872        | -        |
| 0.2434     | 11500     | 0.2823        | -        |
| 0.2455     | 11600     | 0.3017        | -        |
| 0.2476     | 11700     | 0.2573        | -        |
| 0.2497     | 11800     | 0.3104        | -        |
| 0.2518     | 11900     | 0.2857        | -        |
| 0.2539     | 12000     | 0.2898        | -        |
| 0.2561     | 12100     | 0.2389        | -        |
| 0.2582     | 12200     | 0.3137        | -        |
| 0.2603     | 12300     | 0.3029        | -        |
| 0.2624     | 12400     | 0.2894        | -        |
| 0.2645     | 12500     | 0.2665        | -        |
| 0.2666     | 12600     | 0.2705        | -        |
| 0.2688     | 12700     | 0.2673        | -        |
| 0.2709     | 12800     | 0.248         | -        |
| 0.2730     | 12900     | 0.2417        | -        |
| 0.2751     | 13000     | 0.2852        | -        |
| 0.2772     | 13100     | 0.2619        | -        |
| 0.2793     | 13200     | 0.3157        | -        |
| 0.2815     | 13300     | 0.2464        | -        |
| 0.2836     | 13400     | 0.2837        | -        |
| 0.2857     | 13500     | 0.3202        | -        |
| 0.2878     | 13600     | 0.2618        | -        |
| 0.2899     | 13700     | 0.2823        | -        |
| 0.2920     | 13800     | 0.2634        | -        |
| 0.2942     | 13900     | 0.2747        | -        |
| 0.2963     | 14000     | 0.2835        | -        |
| 0.2984     | 14100     | 0.2594        | -        |
| 0.3005     | 14200     | 0.2744        | -        |
| 0.3026     | 14300     | 0.2722        | -        |
| 0.3047     | 14400     | 0.2514        | -        |
| 0.3069     | 14500     | 0.2809        | -        |
| 0.3090     | 14600     | 0.2949        | -        |
| 0.3111     | 14700     | 0.2687        | -        |
| 0.3132     | 14800     | 0.3           | -        |
| 0.3153     | 14900     | 0.2684        | -        |
| 0.3174     | 15000     | 0.2894        | 0.2790   |
| 0.3195     | 15100     | 0.2676        | -        |
| 0.3217     | 15200     | 0.2519        | -        |
| 0.3238     | 15300     | 0.2698        | -        |
| 0.3259     | 15400     | 0.2898        | -        |
| 0.3280     | 15500     | 0.2359        | -        |
| 0.3301     | 15600     | 0.2866        | -        |
| 0.3322     | 15700     | 0.3098        | -        |
| 0.3344     | 15800     | 0.2809        | -        |
| 0.3365     | 15900     | 0.3081        | -        |
| 0.3386     | 16000     | 0.266         | -        |
| 0.3407     | 16100     | 0.2523        | -        |
| 0.3428     | 16200     | 0.3215        | -        |
| 0.3449     | 16300     | 0.2883        | -        |
| 0.3471     | 16400     | 0.2897        | -        |
| 0.3492     | 16500     | 0.3174        | -        |
| 0.3513     | 16600     | 0.2878        | -        |
| 0.3534     | 16700     | 0.267         | -        |
| 0.3555     | 16800     | 0.2452        | -        |
| 0.3576     | 16900     | 0.2429        | -        |
| 0.3598     | 17000     | 0.2178        | -        |
| 0.3619     | 17100     | 0.2798        | -        |
| 0.3640     | 17200     | 0.2367        | -        |
| 0.3661     | 17300     | 0.2554        | -        |
| 0.3682     | 17400     | 0.2883        | -        |
| 0.3703     | 17500     | 0.2567        | -        |
| 0.3725     | 17600     | 0.27          | -        |
| 0.3746     | 17700     | 0.2837        | -        |
| 0.3767     | 17800     | 0.2783        | -        |
| 0.3788     | 17900     | 0.2517        | -        |
| 0.3809     | 18000     | 0.2545        | -        |
| 0.3830     | 18100     | 0.2632        | -        |
| 0.3852     | 18200     | 0.2074        | -        |
| 0.3873     | 18300     | 0.2276        | -        |
| 0.3894     | 18400     | 0.3022        | -        |
| 0.3915     | 18500     | 0.2381        | -        |
| 0.3936     | 18600     | 0.2552        | -        |
| 0.3957     | 18700     | 0.2579        | -        |
| 0.3978     | 18800     | 0.2655        | -        |
| 0.4000     | 18900     | 0.252         | -        |
| 0.4021     | 19000     | 0.2876        | -        |
| 0.4042     | 19100     | 0.2037        | -        |
| 0.4063     | 19200     | 0.251         | -        |
| 0.4084     | 19300     | 0.2588        | -        |
| 0.4105     | 19400     | 0.201         | -        |
| 0.4127     | 19500     | 0.2828        | -        |
| 0.4148     | 19600     | 0.2637        | -        |
| 0.4169     | 19700     | 0.3233        | -        |
| 0.4190     | 19800     | 0.2475        | -        |
| 0.4211     | 19900     | 0.2618        | -        |
| 0.4232     | 20000     | 0.3272        | 0.2519   |
| 0.4254     | 20100     | 0.3074        | -        |
| 0.4275     | 20200     | 0.2994        | -        |
| 0.4296     | 20300     | 0.2624        | -        |
| 0.4317     | 20400     | 0.2389        | -        |
| 0.4338     | 20500     | 0.2809        | -        |
| 0.4359     | 20600     | 0.2659        | -        |
| 0.4381     | 20700     | 0.2508        | -        |
| 0.4402     | 20800     | 0.2542        | -        |
| 0.4423     | 20900     | 0.2525        | -        |
| 0.4444     | 21000     | 0.257         | -        |
| 0.4465     | 21100     | 0.2242        | -        |
| 0.4486     | 21200     | 0.2307        | -        |
| 0.4508     | 21300     | 0.2721        | -        |
| 0.4529     | 21400     | 0.2489        | -        |
| 0.4550     | 21500     | 0.2933        | -        |
| 0.4571     | 21600     | 0.2448        | -        |
| 0.4592     | 21700     | 0.2619        | -        |
| 0.4613     | 21800     | 0.2488        | -        |
| 0.4635     | 21900     | 0.2411        | -        |
| 0.4656     | 22000     | 0.2964        | -        |
| 0.4677     | 22100     | 0.2062        | -        |
| 0.4698     | 22200     | 0.2665        | -        |
| 0.4719     | 22300     | 0.263         | -        |
| 0.4740     | 22400     | 0.2418        | -        |
| 0.4762     | 22500     | 0.2879        | -        |
| 0.4783     | 22600     | 0.2406        | -        |
| 0.4804     | 22700     | 0.2448        | -        |
| 0.4825     | 22800     | 0.243         | -        |
| 0.4846     | 22900     | 0.2863        | -        |
| 0.4867     | 23000     | 0.2833        | -        |
| 0.4888     | 23100     | 0.2784        | -        |
| 0.4910     | 23200     | 0.2789        | -        |
| 0.4931     | 23300     | 0.2495        | -        |
| 0.4952     | 23400     | 0.2872        | -        |
| 0.4973     | 23500     | 0.2487        | -        |
| 0.4994     | 23600     | 0.2669        | -        |
| 0.5015     | 23700     | 0.2748        | -        |
| 0.5037     | 23800     | 0.246         | -        |
| 0.5058     | 23900     | 0.2512        | -        |
| 0.5079     | 24000     | 0.222         | -        |
| 0.5100     | 24100     | 0.2662        | -        |
| 0.5121     | 24200     | 0.2238        | -        |
| 0.5142     | 24300     | 0.2399        | -        |
| 0.5164     | 24400     | 0.2595        | -        |
| 0.5185     | 24500     | 0.3002        | -        |
| 0.5206     | 24600     | 0.2553        | -        |
| 0.5227     | 24700     | 0.226         | -        |
| 0.5248     | 24800     | 0.2823        | -        |
| 0.5269     | 24900     | 0.2737        | -        |
| 0.5291     | 25000     | 0.2237        | 0.2492   |
| 0.5312     | 25100     | 0.2642        | -        |
| 0.5333     | 25200     | 0.2486        | -        |
| 0.5354     | 25300     | 0.2527        | -        |
| 0.5375     | 25400     | 0.2363        | -        |
| 0.5396     | 25500     | 0.2443        | -        |
| 0.5418     | 25600     | 0.2485        | -        |
| 0.5439     | 25700     | 0.2434        | -        |
| 0.5460     | 25800     | 0.2631        | -        |
| 0.5481     | 25900     | 0.284         | -        |
| 0.5502     | 26000     | 0.217         | -        |
| 0.5523     | 26100     | 0.2246        | -        |
| 0.5545     | 26200     | 0.2614        | -        |
| 0.5566     | 26300     | 0.2722        | -        |
| 0.5587     | 26400     | 0.2114        | -        |
| 0.5608     | 26500     | 0.2623        | -        |
| 0.5629     | 26600     | 0.2475        | -        |
| 0.5650     | 26700     | 0.2449        | -        |
| 0.5671     | 26800     | 0.2423        | -        |
| 0.5693     | 26900     | 0.2435        | -        |
| 0.5714     | 27000     | 0.2446        | -        |
| 0.5735     | 27100     | 0.2248        | -        |
| 0.5756     | 27200     | 0.2159        | -        |
| 0.5777     | 27300     | 0.2415        | -        |
| 0.5798     | 27400     | 0.2257        | -        |
| 0.5820     | 27500     | 0.2775        | -        |
| 0.5841     | 27600     | 0.2533        | -        |
| 0.5862     | 27700     | 0.2893        | -        |
| 0.5883     | 27800     | 0.2095        | -        |
| 0.5904     | 27900     | 0.2156        | -        |
| 0.5925     | 28000     | 0.2315        | -        |
| 0.5947     | 28100     | 0.2865        | -        |
| 0.5968     | 28200     | 0.262         | -        |
| 0.5989     | 28300     | 0.2506        | -        |
| 0.6010     | 28400     | 0.2472        | -        |
| 0.6031     | 28500     | 0.2395        | -        |
| 0.6052     | 28600     | 0.2269        | -        |
| 0.6074     | 28700     | 0.2639        | -        |
| 0.6095     | 28800     | 0.2674        | -        |
| 0.6116     | 28900     | 0.2521        | -        |
| 0.6137     | 29000     | 0.2553        | -        |
| 0.6158     | 29100     | 0.2526        | -        |
| 0.6179     | 29200     | 0.231         | -        |
| 0.6201     | 29300     | 0.2622        | -        |
| 0.6222     | 29400     | 0.237         | -        |
| 0.6243     | 29500     | 0.2475        | -        |
| 0.6264     | 29600     | 0.2435        | -        |
| 0.6285     | 29700     | 0.2109        | -        |
| 0.6306     | 29800     | 0.2376        | -        |
| 0.6328     | 29900     | 0.2202        | -        |
| 0.6349     | 30000     | 0.2147        | 0.2370   |
| 0.6370     | 30100     | 0.2306        | -        |
| 0.6391     | 30200     | 0.2249        | -        |
| 0.6412     | 30300     | 0.3027        | -        |
| 0.6433     | 30400     | 0.2115        | -        |
| 0.6454     | 30500     | 0.2597        | -        |
| 0.6476     | 30600     | 0.2483        | -        |
| 0.6497     | 30700     | 0.2719        | -        |
| 0.6518     | 30800     | 0.2162        | -        |
| 0.6539     | 30900     | 0.2947        | -        |
| 0.6560     | 31000     | 0.2144        | -        |
| 0.6581     | 31100     | 0.2391        | -        |
| 0.6603     | 31200     | 0.2572        | -        |
| 0.6624     | 31300     | 0.1977        | -        |
| 0.6645     | 31400     | 0.2678        | -        |
| 0.6666     | 31500     | 0.2353        | -        |
| 0.6687     | 31600     | 0.1911        | -        |
| 0.6708     | 31700     | 0.2844        | -        |
| 0.6730     | 31800     | 0.2689        | -        |
| 0.6751     | 31900     | 0.2491        | -        |
| 0.6772     | 32000     | 0.2259        | -        |
| 0.6793     | 32100     | 0.2248        | -        |
| 0.6814     | 32200     | 0.2462        | -        |
| 0.6835     | 32300     | 0.2135        | -        |
| 0.6857     | 32400     | 0.2085        | -        |
| 0.6878     | 32500     | 0.227         | -        |
| 0.6899     | 32600     | 0.2488        | -        |
| 0.6920     | 32700     | 0.2614        | -        |
| 0.6941     | 32800     | 0.2274        | -        |
| 0.6962     | 32900     | 0.2389        | -        |
| 0.6984     | 33000     | 0.2573        | -        |
| 0.7005     | 33100     | 0.245         | -        |
| 0.7026     | 33200     | 0.21          | -        |
| 0.7047     | 33300     | 0.2196        | -        |
| 0.7068     | 33400     | 0.2218        | -        |
| 0.7089     | 33500     | 0.2092        | -        |
| 0.7111     | 33600     | 0.2526        | -        |
| 0.7132     | 33700     | 0.2275        | -        |
| 0.7153     | 33800     | 0.2622        | -        |
| 0.7174     | 33900     | 0.2469        | -        |
| 0.7195     | 34000     | 0.2157        | -        |
| 0.7216     | 34100     | 0.2326        | -        |
| 0.7237     | 34200     | 0.268         | -        |
| 0.7259     | 34300     | 0.2628        | -        |
| 0.7280     | 34400     | 0.2503        | -        |
| 0.7301     | 34500     | 0.2101        | -        |
| 0.7322     | 34600     | 0.237         | -        |
| 0.7343     | 34700     | 0.233         | -        |
| 0.7364     | 34800     | 0.2077        | -        |
| 0.7386     | 34900     | 0.259         | -        |
| 0.7407     | 35000     | 0.2312        | 0.2284   |
| 0.7428     | 35100     | 0.287         | -        |
| 0.7449     | 35200     | 0.2278        | -        |
| 0.7470     | 35300     | 0.2618        | -        |
| 0.7491     | 35400     | 0.2298        | -        |
| 0.7513     | 35500     | 0.195         | -        |
| 0.7534     | 35600     | 0.2248        | -        |
| 0.7555     | 35700     | 0.2234        | -        |
| 0.7576     | 35800     | 0.2218        | -        |
| 0.7597     | 35900     | 0.2002        | -        |
| 0.7618     | 36000     | 0.2158        | -        |
| 0.7640     | 36100     | 0.1919        | -        |
| 0.7661     | 36200     | 0.2972        | -        |
| 0.7682     | 36300     | 0.2665        | -        |
| 0.7703     | 36400     | 0.2114        | -        |
| 0.7724     | 36500     | 0.1879        | -        |
| 0.7745     | 36600     | 0.2137        | -        |
| 0.7767     | 36700     | 0.2847        | -        |
| 0.7788     | 36800     | 0.2372        | -        |
| 0.7809     | 36900     | 0.2058        | -        |
| 0.7830     | 37000     | 0.2205        | -        |
| 0.7851     | 37100     | 0.2012        | -        |
| 0.7872     | 37200     | 0.2057        | -        |
| 0.7894     | 37300     | 0.1932        | -        |
| 0.7915     | 37400     | 0.2261        | -        |
| 0.7936     | 37500     | 0.2633        | -        |
| 0.7957     | 37600     | 0.1558        | -        |
| 0.7978     | 37700     | 0.2064        | -        |
| 0.7999     | 37800     | 0.2166        | -        |
| 0.8020     | 37900     | 0.2249        | -        |
| 0.8042     | 38000     | 0.2626        | -        |
| 0.8063     | 38100     | 0.1945        | -        |
| 0.8084     | 38200     | 0.2611        | -        |
| 0.8105     | 38300     | 0.199         | -        |
| 0.8126     | 38400     | 0.2004        | -        |
| 0.8147     | 38500     | 0.2506        | -        |
| 0.8169     | 38600     | 0.1722        | -        |
| 0.8190     | 38700     | 0.1959        | -        |
| 0.8211     | 38800     | 0.2505        | -        |
| 0.8232     | 38900     | 0.2343        | -        |
| 0.8253     | 39000     | 0.2353        | -        |
| 0.8274     | 39100     | 0.22          | -        |
| 0.8296     | 39200     | 0.2089        | -        |
| 0.8317     | 39300     | 0.2416        | -        |
| 0.8338     | 39400     | 0.1916        | -        |
| 0.8359     | 39500     | 0.2387        | -        |
| 0.8380     | 39600     | 0.2475        | -        |
| 0.8401     | 39700     | 0.2189        | -        |
| 0.8423     | 39800     | 0.2141        | -        |
| 0.8444     | 39900     | 0.2008        | -        |
| 0.8465     | 40000     | 0.2489        | 0.2253   |
| 0.8486     | 40100     | 0.2258        | -        |
| 0.8507     | 40200     | 0.2341        | -        |
| 0.8528     | 40300     | 0.2377        | -        |
| 0.8550     | 40400     | 0.194         | -        |
| 0.8571     | 40500     | 0.2144        | -        |
| 0.8592     | 40600     | 0.2605        | -        |
| 0.8613     | 40700     | 0.2517        | -        |
| 0.8634     | 40800     | 0.2044        | -        |
| 0.8655     | 40900     | 0.2259        | -        |
| 0.8677     | 41000     | 0.2141        | -        |
| 0.8698     | 41100     | 0.1895        | -        |
| 0.8719     | 41200     | 0.2361        | -        |
| 0.8740     | 41300     | 0.1978        | -        |
| 0.8761     | 41400     | 0.2089        | -        |
| 0.8782     | 41500     | 0.2258        | -        |
| 0.8803     | 41600     | 0.2368        | -        |
| 0.8825     | 41700     | 0.2473        | -        |
| 0.8846     | 41800     | 0.2185        | -        |
| 0.8867     | 41900     | 0.212         | -        |
| 0.8888     | 42000     | 0.2469        | -        |
| 0.8909     | 42100     | 0.1817        | -        |
| 0.8930     | 42200     | 0.1884        | -        |
| 0.8952     | 42300     | 0.207         | -        |
| 0.8973     | 42400     | 0.2422        | -        |
| 0.8994     | 42500     | 0.2606        | -        |
| 0.9015     | 42600     | 0.2266        | -        |
| 0.9036     | 42700     | 0.2103        | -        |
| 0.9057     | 42800     | 0.2712        | -        |
| 0.9079     | 42900     | 0.1944        | -        |
| 0.9100     | 43000     | 0.2003        | -        |
| 0.9121     | 43100     | 0.1991        | -        |
| 0.9142     | 43200     | 0.2129        | -        |
| 0.9163     | 43300     | 0.2465        | -        |
| 0.9184     | 43400     | 0.1764        | -        |
| 0.9206     | 43500     | 0.2365        | -        |
| 0.9227     | 43600     | 0.2054        | -        |
| 0.9248     | 43700     | 0.2551        | -        |
| 0.9269     | 43800     | 0.2322        | -        |
| 0.9290     | 43900     | 0.2213        | -        |
| 0.9311     | 44000     | 0.1962        | -        |
| 0.9333     | 44100     | 0.1988        | -        |
| 0.9354     | 44200     | 0.1982        | -        |
| 0.9375     | 44300     | 0.2193        | -        |
| 0.9396     | 44400     | 0.2378        | -        |
| 0.9417     | 44500     | 0.2244        | -        |
| 0.9438     | 44600     | 0.2296        | -        |
| 0.9460     | 44700     | 0.2446        | -        |
| 0.9481     | 44800     | 0.2206        | -        |
| 0.9502     | 44900     | 0.1815        | -        |
| **0.9523** | **45000** | **0.2385**    | **0.22** |
| 0.9544     | 45100     | 0.2106        | -        |
| 0.9565     | 45200     | 0.1929        | -        |
| 0.9586     | 45300     | 0.181         | -        |
| 0.9608     | 45400     | 0.1908        | -        |
| 0.9629     | 45500     | 0.1926        | -        |
| 0.9650     | 45600     | 0.1922        | -        |
| 0.9671     | 45700     | 0.2003        | -        |
| 0.9692     | 45800     | 0.2377        | -        |
| 0.9713     | 45900     | 0.2069        | -        |
| 0.9735     | 46000     | 0.2024        | -        |
| 0.9756     | 46100     | 0.1795        | -        |
| 0.9777     | 46200     | 0.2372        | -        |
| 0.9798     | 46300     | 0.2135        | -        |
| 0.9819     | 46400     | 0.2396        | -        |
| 0.9840     | 46500     | 0.2295        | -        |
| 0.9862     | 46600     | 0.2235        | -        |
| 0.9883     | 46700     | 0.2427        | -        |
| 0.9904     | 46800     | 0.2145        | -        |
| 0.9925     | 46900     | 0.2231        | -        |
| 0.9946     | 47000     | 0.2401        | -        |
| 0.9967     | 47100     | 0.1764        | -        |
| 0.9989     | 47200     | 0.1943        | -        |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 3.1.0.dev0
- Transformers: 4.42.4
- PyTorch: 2.3.1+cpu
- Accelerate: 0.32.1
- Datasets: 2.20.0
- 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",
}
```

#### 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}
}
```

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