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---
language:
- zh
license: mit
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:225000
- loss:MultipleNegativesRankingLoss
base_model: richinfoai/ritrieve_zh_v1
widget:
- source_sentence: 下班后和同事直奔常去的那家火锅店，热热闹闹地涮了一晚上。
sentences:
- 联延掩四远，赫弈成洪炉。
- 把酒仰问天，古今谁不死。
- 骑出平阳里，筵开卫尉家。
- source_sentence: 站在山顶看日出时，突然觉得世俗烦恼都不重要了。
sentences:
- 郁没二悲魂，萧条犹在否。
- 封疆亲日月，邑里出王公。
- 心朝玉皇帝，貌似紫阳人。
- source_sentence: 隔壁老张家两个儿子都被征走了，现在天天以泪洗面。
sentences:
- 若教为女嫁东风，除却黄莺难匹配。
- 山东今岁点行频，几处冤魂哭虏尘。
- 远图尝画地，超拜乃登坛。
- source_sentence: 边境小镇常年没人驻守，只有老李一个人在山脚下种地。
sentences:
- 海徼长无戍，湘山独种畬。
- 高名宋玉遗闲丽，作赋兰成绝盛才。
- 九衢南面色，苍翠绝纤尘。
- source_sentence: 微信列表翻到底，能说真心话的居然只剩快递群。
sentences:
- 黛消波月空蟾影，歌息梁尘有梵声。
- 代情难重论，人事好乖移。
- 时应记得长安事，曾向文场属思劳。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# RETRIEVE ZH 微调：古诗 ↔ 现代语
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [richinfoai/ritrieve_zh_v1](https://huggingface.co/richinfoai/ritrieve_zh_v1) on the json dataset. It maps sentences & paragraphs to a 1792-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:** [richinfoai/ritrieve_zh_v1](https://huggingface.co/richinfoai/ritrieve_zh_v1) <!-- at revision f8d5a707656c55705027678e311f9202c8ced12c -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1792 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** zh
- **License:** mit
### 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': 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): Dense({'in_features': 1024, 'out_features': 1792, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## 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 = [
'微信列表翻到底，能说真心话的居然只剩快递群。',
'代情难重论，人事好乖移。',
'时应记得长安事，曾向文场属思劳。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1792]
# 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.*
-->
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 225,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 14 tokens</li><li>mean: 26.51 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 15.23 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 15.34 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------|:------------------------------|:------------------------------|
| <code>整个人蜷在阳光里，连毛衣都晒出一股蓬松的香味。</code> | <code>箕踞拥裘坐，半身在日旸。</code> | <code>洛阳女儿对门居，才可容颜十五馀。</code> |
| <code>好像所有的好事都约好了一样，今天一起找上门来。</code> | <code>临终极乐宝华迎，观音势至俱来至。</code> | <code>身没南朝宅已荒，邑人犹赏旧风光。</code> |
| <code>大家都觉得她太娇气，只有你一直小心照顾着她。</code> | <code>弱质人皆弃，唯君手自栽。</code> | <code>秦筑长城城已摧，汉武北上单于台。</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
#### json
* Dataset: json
* Size: 25,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 26.86 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 15.31 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 15.3 tokens</li><li>max: 26 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------|:--------------------------|:------------------------------|
| <code>看着街边那些孤零零的老人，真怕自己以后也变成那样。</code> | <code>垂白乱南翁，委身希北叟。</code> | <code>熏香荀令偏怜少，傅粉何郎不解愁。</code> |
| <code>关了灯，屋里黑漆漆的，就听见外面秋虫和落叶在说话。</code> | <code>秋虫与秋叶，一夜隔窗闻。</code> | <code>未能穷意义，岂敢求瑕痕。</code> |
| <code>虽然爷爷不在了，但他教我做人的道理永远记在心里。</code> | <code>惟孝虽遥，灵规不朽。</code> | <code>巧类鸳机织，光攒麝月团。</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`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 6
- `warmup_ratio`: 0.1
- `fp16`: 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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 2e-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`: 6
- `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`: 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}
- `tp_size`: 0
- `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`: None
- `hub_always_push`: False
- `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`: 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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0284 | 50 | 4.4241 | - |
| 0.0569 | 100 | 3.4415 | - |
| 0.0853 | 150 | 2.6725 | - |
| 0.1138 | 200 | 2.4137 | 2.2686 |
| 0.1422 | 250 | 2.2701 | - |
| 0.1706 | 300 | 2.1523 | - |
| 0.1991 | 350 | 2.0805 | - |
| 0.2275 | 400 | 2.0513 | 1.9506 |
| 0.2560 | 450 | 2.0048 | - |
| 0.2844 | 500 | 1.9552 | - |
| 0.3129 | 550 | 1.8778 | - |
| 0.3413 | 600 | 1.8549 | 1.7630 |
| 0.3697 | 650 | 1.822 | - |
| 0.3982 | 700 | 1.8128 | - |
| 0.4266 | 750 | 1.7742 | - |
| 0.4551 | 800 | 1.7076 | 1.6331 |
| 0.4835 | 850 | 1.6919 | - |
| 0.5119 | 900 | 1.64 | - |
| 0.5404 | 950 | 1.6291 | - |
| 0.5688 | 1000 | 1.5881 | 1.5368 |
| 0.5973 | 1050 | 1.6018 | - |
| 0.6257 | 1100 | 1.5664 | - |
| 0.6542 | 1150 | 1.5545 | - |
| 0.6826 | 1200 | 1.5292 | 1.4532 |
| 0.7110 | 1250 | 1.5166 | - |
| 0.7395 | 1300 | 1.517 | - |
| 0.7679 | 1350 | 1.4639 | - |
| 0.7964 | 1400 | 1.4729 | 1.3687 |
| 0.8248 | 1450 | 1.4501 | - |
| 0.8532 | 1500 | 1.3932 | - |
| 0.8817 | 1550 | 1.4063 | - |
| 0.9101 | 1600 | 1.3825 | 1.3003 |
| 0.9386 | 1650 | 1.3647 | - |
| 0.9670 | 1700 | 1.3431 | - |
| 0.9954 | 1750 | 1.3417 | - |
| 1.0239 | 1800 | 1.0839 | 1.2431 |
| 1.0523 | 1850 | 1.0801 | - |
| 1.0808 | 1900 | 1.0577 | - |
| 1.1092 | 1950 | 1.0159 | - |
| 1.1377 | 2000 | 1.0239 | 1.2132 |
| 1.1661 | 2050 | 1.0335 | - |
| 1.1945 | 2100 | 1.0117 | - |
| 1.2230 | 2150 | 1.0343 | - |
| 1.2514 | 2200 | 1.0193 | 1.1808 |
| 1.2799 | 2250 | 1.0235 | - |
| 1.3083 | 2300 | 0.9949 | - |
| 1.3367 | 2350 | 1.0058 | - |
| 1.3652 | 2400 | 1.0039 | 1.1428 |
| 1.3936 | 2450 | 1.0164 | - |
| 1.4221 | 2500 | 0.9934 | - |
| 1.4505 | 2550 | 0.9777 | - |
| 1.4790 | 2600 | 0.9753 | 1.1101 |
| 1.5074 | 2650 | 0.9621 | - |
| 1.5358 | 2700 | 0.9756 | - |
| 1.5643 | 2750 | 0.9725 | - |
| 1.5927 | 2800 | 0.9649 | 1.0813 |
| 1.6212 | 2850 | 0.9652 | - |
| 1.6496 | 2900 | 0.9861 | - |
| 1.6780 | 2950 | 0.916 | - |
| 1.7065 | 3000 | 0.9417 | 1.0523 |
| 1.7349 | 3050 | 0.9599 | - |
| 1.7634 | 3100 | 0.9275 | - |
| 1.7918 | 3150 | 0.9247 | - |
| 1.8203 | 3200 | 0.9417 | 1.0306 |
| 1.8487 | 3250 | 0.9275 | - |
| 1.8771 | 3300 | 0.9431 | - |
| 1.9056 | 3350 | 0.9147 | - |
| 1.9340 | 3400 | 0.8957 | 1.0051 |
| 1.9625 | 3450 | 0.9169 | - |
| 1.9909 | 3500 | 0.9079 | - |
| 2.0193 | 3550 | 0.7057 | - |
| 2.0478 | 3600 | 0.6037 | 0.9944 |
| 2.0762 | 3650 | 0.5888 | - |
| 2.1047 | 3700 | 0.6134 | - |
| 2.1331 | 3750 | 0.6209 | - |
| 2.1615 | 3800 | 0.6163 | 0.9836 |
| 2.1900 | 3850 | 0.6271 | - |
| 2.2184 | 3900 | 0.629 | - |
| 2.2469 | 3950 | 0.6041 | - |
| 2.2753 | 4000 | 0.622 | 0.9792 |
| 2.3038 | 4050 | 0.6175 | - |
| 2.3322 | 4100 | 0.627 | - |
| 2.3606 | 4150 | 0.6339 | - |
| 2.3891 | 4200 | 0.6325 | 0.9643 |
| 2.4175 | 4250 | 0.6044 | - |
| 2.4460 | 4300 | 0.6124 | - |
| 2.4744 | 4350 | 0.6326 | - |
| 2.5028 | 4400 | 0.6349 | 0.9462 |
| 2.5313 | 4450 | 0.6286 | - |
| 2.5597 | 4500 | 0.6325 | - |
| 2.5882 | 4550 | 0.6399 | - |
| 2.6166 | 4600 | 0.6184 | 0.9317 |
| 2.6451 | 4650 | 0.6292 | - |
| 2.6735 | 4700 | 0.6017 | - |
| 2.7019 | 4750 | 0.6305 | - |
| 2.7304 | 4800 | 0.6152 | 0.9213 |
| 2.7588 | 4850 | 0.5972 | - |
| 2.7873 | 4900 | 0.6048 | - |
| 2.8157 | 4950 | 0.6096 | - |
| 2.8441 | 5000 | 0.6156 | 0.9073 |
| 2.8726 | 5050 | 0.5942 | - |
| 2.9010 | 5100 | 0.592 | - |
| 2.9295 | 5150 | 0.6088 | - |
| 2.9579 | 5200 | 0.5941 | 0.8950 |
| 2.9863 | 5250 | 0.6161 | - |
| 3.0148 | 5300 | 0.5021 | - |
| 3.0432 | 5350 | 0.4116 | - |
| 3.0717 | 5400 | 0.3936 | 0.9009 |
| 3.1001 | 5450 | 0.4193 | - |
| 3.1286 | 5500 | 0.422 | - |
| 3.1570 | 5550 | 0.432 | - |
| 3.1854 | 5600 | 0.4281 | 0.8985 |
| 3.2139 | 5650 | 0.4091 | - |
| 3.2423 | 5700 | 0.4305 | - |
| 3.2708 | 5750 | 0.4203 | - |
| 3.2992 | 5800 | 0.4193 | 0.8869 |
| 3.3276 | 5850 | 0.4238 | - |
| 3.3561 | 5900 | 0.4274 | - |
| 3.3845 | 5950 | 0.4124 | - |
| 3.4130 | 6000 | 0.4241 | 0.8842 |
| 3.4414 | 6050 | 0.427 | - |
| 3.4699 | 6100 | 0.4275 | - |
| 3.4983 | 6150 | 0.4152 | - |
| 3.5267 | 6200 | 0.4247 | 0.8733 |
| 3.5552 | 6250 | 0.4111 | - |
| 3.5836 | 6300 | 0.4396 | - |
| 3.6121 | 6350 | 0.4122 | - |
| 3.6405 | 6400 | 0.4252 | 0.8657 |
| 3.6689 | 6450 | 0.4167 | - |
| 3.6974 | 6500 | 0.4282 | - |
| 3.7258 | 6550 | 0.411 | - |
| 3.7543 | 6600 | 0.4273 | 0.8540 |
| 3.7827 | 6650 | 0.4327 | - |
| 3.8111 | 6700 | 0.431 | - |
| 3.8396 | 6750 | 0.4347 | - |
| 3.8680 | 6800 | 0.4264 | 0.8523 |
| 3.8965 | 6850 | 0.4213 | - |
| 3.9249 | 6900 | 0.4285 | - |
| 3.9534 | 6950 | 0.4138 | - |
| 3.9818 | 7000 | 0.4051 | 0.8407 |
| 4.0102 | 7050 | 0.3779 | - |
| 4.0387 | 7100 | 0.2957 | - |
| 4.0671 | 7150 | 0.2939 | - |
| 4.0956 | 7200 | 0.3065 | 0.8590 |
| 4.1240 | 7250 | 0.3081 | - |
| 4.1524 | 7300 | 0.3043 | - |
| 4.1809 | 7350 | 0.3176 | - |
| 4.2093 | 7400 | 0.3067 | 0.8487 |
| 4.2378 | 7450 | 0.299 | - |
| 4.2662 | 7500 | 0.3106 | - |
| 4.2947 | 7550 | 0.3062 | - |
| 4.3231 | 7600 | 0.3153 | 0.8498 |
| 4.3515 | 7650 | 0.3206 | - |
| 4.3800 | 7700 | 0.3202 | - |
| 4.4084 | 7750 | 0.3167 | - |
| 4.4369 | 7800 | 0.3044 | 0.8426 |
| 4.4653 | 7850 | 0.3015 | - |
| 4.4937 | 7900 | 0.3157 | - |
| 4.5222 | 7950 | 0.3109 | - |
| 4.5506 | 8000 | 0.3164 | 0.8385 |
| 4.5791 | 8050 | 0.2996 | - |
| 4.6075 | 8100 | 0.3247 | - |
| 4.6359 | 8150 | 0.3093 | - |
| 4.6644 | 8200 | 0.3017 | 0.8294 |
| 4.6928 | 8250 | 0.3075 | - |
| 4.7213 | 8300 | 0.3006 | - |
| 4.7497 | 8350 | 0.3134 | - |
| 4.7782 | 8400 | 0.3111 | 0.8249 |
| 4.8066 | 8450 | 0.3165 | - |
| 4.8350 | 8500 | 0.3071 | - |
| 4.8635 | 8550 | 0.3017 | - |
| 4.8919 | 8600 | 0.3092 | 0.8225 |
| 4.9204 | 8650 | 0.3 | - |
| 4.9488 | 8700 | 0.2999 | - |
| 4.9772 | 8750 | 0.3116 | - |
| 5.0057 | 8800 | 0.3046 | 0.8173 |
| 5.0341 | 8850 | 0.2501 | - |
| 5.0626 | 8900 | 0.2443 | - |
| 5.0910 | 8950 | 0.2338 | - |
| 5.1195 | 9000 | 0.2382 | 0.8248 |
| 5.1479 | 9050 | 0.2524 | - |
| 5.1763 | 9100 | 0.2427 | - |
| 5.2048 | 9150 | 0.2512 | - |
| 5.2332 | 9200 | 0.2377 | 0.8218 |
| 5.2617 | 9250 | 0.2458 | - |
| 5.2901 | 9300 | 0.2515 | - |
| 5.3185 | 9350 | 0.2453 | - |
| 5.3470 | 9400 | 0.244 | 0.8226 |
| 5.3754 | 9450 | 0.2389 | - |
| 5.4039 | 9500 | 0.253 | - |
| 5.4323 | 9550 | 0.2509 | - |
| 5.4608 | 9600 | 0.2492 | 0.8198 |
| 5.4892 | 9650 | 0.2379 | - |
| 5.5176 | 9700 | 0.247 | - |
| 5.5461 | 9750 | 0.2419 | - |
| 5.5745 | 9800 | 0.244 | 0.8150 |
| 5.6030 | 9850 | 0.2498 | - |
| 5.6314 | 9900 | 0.2381 | - |
| 5.6598 | 9950 | 0.2425 | - |
| 5.6883 | 10000 | 0.2451 | 0.8148 |
| 5.7167 | 10050 | 0.2468 | - |
| 5.7452 | 10100 | 0.2404 | - |
| 5.7736 | 10150 | 0.2397 | - |
| 5.8020 | 10200 | 0.2417 | 0.8124 |
| 5.8305 | 10250 | 0.2446 | - |
| 5.8589 | 10300 | 0.2443 | - |
| 5.8874 | 10350 | 0.2465 | - |
| 5.9158 | 10400 | 0.2472 | 0.8121 |
</details>
### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu126
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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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