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---
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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