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---
base_model: ahdsoft/persian-sentence-transformer-news-wiki-pairs-v3
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:804708
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: کیف رودوشی نشنال جئوگرافیک مدل NG A4569
sentences:
- خودرو و موتورسیکلت
- لوازم جانبی کالای دیجیتال
- ورزش و سفر
- source_sentence: پازل 35 تکه مدل کیتی کد 48
sentences:
- اسباب بازی، کودک و نوزاد
- لوازم جانبی کالای دیجیتال
- اسباب بازی، کودک و نوزاد
- source_sentence: ادو تویلت مردانه مون بلان مدل Legend حجم 200 میلی لیتر
sentences:
- زیبایی و سلامت
- کتاب، لوازم تحریر و هنر
- زیبایی و سلامت
- source_sentence: تاپ ورزشی مردانه مدل REM116
sentences:
- کتاب، لوازم تحریر و هنر
- لوازم جانبی کالای دیجیتال
- مد و پوشاک
- source_sentence: بازی آموزشی مدل جورچین ایران کد K-5
sentences:
- زیبایی و سلامت
- اسباب بازی، کودک و نوزاد
- کالاهای سوپرمارکتی
model-index:
- name: SentenceTransformer based on ahdsoft/persian-sentence-transformer-news-wiki-pairs-v3
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: embedding similarity eval
type: embedding-similarity-eval
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: .nan
name: Pearson Manhattan
- type: spearman_manhattan
value: .nan
name: Spearman Manhattan
- type: pearson_euclidean
value: .nan
name: Pearson Euclidean
- type: spearman_euclidean
value: .nan
name: Spearman Euclidean
- type: pearson_dot
value: .nan
name: Pearson Dot
- type: spearman_dot
value: .nan
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
---
# SentenceTransformer based on ahdsoft/persian-sentence-transformer-news-wiki-pairs-v3
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ahdsoft/persian-sentence-transformer-news-wiki-pairs-v3](https://huggingface.co/ahdsoft/persian-sentence-transformer-news-wiki-pairs-v3). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [ahdsoft/persian-sentence-transformer-news-wiki-pairs-v3](https://huggingface.co/ahdsoft/persian-sentence-transformer-news-wiki-pairs-v3) <!-- at revision 7eee26ceead63a452608ae0d47de2d50397c91eb -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("aidal/persian-sentence-transformer-product-classification")
# Run inference
sentences = [
'بازی آموزشی مدل جورچین ایران کد K-5',
'اسباب بازی، کودک و نوزاد',
'زیبایی و سلامت',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `embedding-similarity-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:--------|
| pearson_cosine | nan |
| spearman_cosine | nan |
| pearson_manhattan | nan |
| spearman_manhattan | nan |
| pearson_euclidean | nan |
| spearman_euclidean | nan |
| pearson_dot | nan |
| spearman_dot | nan |
| pearson_max | nan |
| **spearman_max** | **nan** |
<!--
## 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: 804,708 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: 6 tokens</li><li>mean: 16.15 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.42 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------|:-------------------------------------|
| <code>مربا زرشک مارجان - 270 گرم</code> | <code>محصولات بومی و محلی</code> |
| <code>دفتر یادداشت بادکنک آبی طرح انیمه مدل Attack on titan مجموعه 2 عددی</code> | <code>کتاب، لوازم تحریر و هنر</code> |
| <code>چای ساز کاراجا مدل Cay Sever</code> | <code>لوازم خانگی برقی</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: 89,413 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: 6 tokens</li><li>mean: 16.02 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.38 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------|:--------------------------------------|
| <code>لامپ ال ای دی 6 وات لداستار مدل شعله ای پایه E27 بسته 3 عددی</code> | <code>خانه و آشپزخانه</code> |
| <code>زیرانداز تعویض نوزاد مدل هپی ویکند</code> | <code>اسباب بازی، کودک و نوزاد</code> |
| <code>تابلو نوری کاکتی مدل عاشقانه طرح اسم شهسوار کد TA14352</code> | <code>خانه و آشپزخانه</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`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `log_level`: debug
- `fp16`: True
- `load_best_model_at_end`: True
#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: debug
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | embedding-similarity-eval_spearman_max |
|:----------:|:---------:|:-------------:|:----------:|:--------------------------------------:|
| 0.0040 | 100 | 2.7527 | - | - |
| 0.0080 | 200 | 2.0773 | - | - |
| 0.0119 | 300 | 1.764 | - | - |
| 0.0159 | 400 | 1.5861 | - | - |
| 0.0199 | 500 | 1.5138 | - | - |
| 0.0239 | 600 | 1.4307 | - | - |
| 0.0278 | 700 | 1.3923 | - | - |
| 0.0318 | 800 | 1.3251 | - | - |
| 0.0358 | 900 | 1.3023 | - | - |
| 0.0398 | 1000 | 1.2929 | - | - |
| 0.0437 | 1100 | 1.2764 | - | - |
| 0.0477 | 1200 | 1.2728 | - | - |
| 0.0517 | 1300 | 1.2262 | - | - |
| 0.0557 | 1400 | 1.2456 | - | - |
| 0.0596 | 1500 | 1.2052 | - | - |
| 0.0636 | 1600 | 1.1912 | - | - |
| 0.0676 | 1700 | 1.2077 | - | - |
| 0.0716 | 1800 | 1.2196 | - | - |
| 0.0756 | 1900 | 1.1603 | - | - |
| 0.0795 | 2000 | 1.1706 | - | - |
| 0.0835 | 2100 | 1.2001 | - | - |
| 0.0875 | 2200 | 1.1822 | - | - |
| 0.0915 | 2300 | 1.1703 | - | - |
| 0.0954 | 2400 | 1.204 | - | - |
| 0.0994 | 2500 | 1.1863 | 1.1333 | nan |
| 0.1034 | 2600 | 1.1567 | - | - |
| 0.1074 | 2700 | 1.1876 | - | - |
| 0.1113 | 2800 | 1.1553 | - | - |
| 0.1153 | 2900 | 1.1332 | - | - |
| 0.1193 | 3000 | 1.1426 | - | - |
| 0.1233 | 3100 | 1.1476 | - | - |
| 0.1272 | 3200 | 1.1482 | - | - |
| 0.1312 | 3300 | 1.1343 | - | - |
| 0.1352 | 3400 | 1.1572 | - | - |
| 0.1392 | 3500 | 1.1018 | - | - |
| 0.1432 | 3600 | 1.1175 | - | - |
| 0.1471 | 3700 | 1.1024 | - | - |
| 0.1511 | 3800 | 1.1308 | - | - |
| 0.1551 | 3900 | 1.1386 | - | - |
| 0.1591 | 4000 | 1.1103 | - | - |
| 0.1630 | 4100 | 1.1472 | - | - |
| 0.1670 | 4200 | 1.1079 | - | - |
| 0.1710 | 4300 | 1.1199 | - | - |
| 0.1750 | 4400 | 1.1306 | - | - |
| 0.1789 | 4500 | 1.0975 | - | - |
| 0.1829 | 4600 | 1.1285 | - | - |
| 0.1869 | 4700 | 1.121 | - | - |
| 0.1909 | 4800 | 1.1099 | - | - |
| 0.1948 | 4900 | 1.0913 | - | - |
| 0.1988 | 5000 | 1.0631 | 1.0980 | nan |
| 0.2028 | 5100 | 1.1336 | - | - |
| 0.2068 | 5200 | 1.1055 | - | - |
| 0.2108 | 5300 | 1.0987 | - | - |
| 0.2147 | 5400 | 1.1078 | - | - |
| 0.2187 | 5500 | 1.0749 | - | - |
| 0.2227 | 5600 | 1.1016 | - | - |
| 0.2267 | 5700 | 1.0768 | - | - |
| 0.2306 | 5800 | 1.0954 | - | - |
| 0.2346 | 5900 | 1.0975 | - | - |
| 0.2386 | 6000 | 1.0638 | - | - |
| 0.2426 | 6100 | 1.0751 | - | - |
| 0.2465 | 6200 | 1.0675 | - | - |
| 0.2505 | 6300 | 1.0513 | - | - |
| 0.2545 | 6400 | 1.0808 | - | - |
| 0.2585 | 6500 | 1.0863 | - | - |
| 0.2624 | 6600 | 1.0681 | - | - |
| 0.2664 | 6700 | 1.0813 | - | - |
| 0.2704 | 6800 | 1.077 | - | - |
| 0.2744 | 6900 | 1.0811 | - | - |
| 0.2784 | 7000 | 1.0543 | - | - |
| 0.2823 | 7100 | 1.0677 | - | - |
| 0.2863 | 7200 | 1.0691 | - | - |
| 0.2903 | 7300 | 1.0597 | - | - |
| 0.2943 | 7400 | 1.0538 | - | - |
| 0.2982 | 7500 | 1.0853 | 1.0658 | nan |
| 0.3022 | 7600 | 1.0831 | - | - |
| 0.3062 | 7700 | 1.0565 | - | - |
| 0.3102 | 7800 | 1.0667 | - | - |
| 0.3141 | 7900 | 1.0839 | - | - |
| 0.3181 | 8000 | 1.0742 | - | - |
| 0.3221 | 8100 | 1.0543 | - | - |
| 0.3261 | 8200 | 1.0539 | - | - |
| 0.3300 | 8300 | 1.07 | - | - |
| 0.3340 | 8400 | 1.0556 | - | - |
| 0.3380 | 8500 | 1.0715 | - | - |
| 0.3420 | 8600 | 1.0468 | - | - |
| 0.3460 | 8700 | 1.0477 | - | - |
| 0.3499 | 8800 | 1.0401 | - | - |
| 0.3539 | 8900 | 1.1047 | - | - |
| 0.3579 | 9000 | 1.0345 | - | - |
| 0.3619 | 9100 | 1.0677 | - | - |
| 0.3658 | 9200 | 1.0705 | - | - |
| 0.3698 | 9300 | 1.0624 | - | - |
| 0.3738 | 9400 | 1.0528 | - | - |
| 0.3778 | 9500 | 1.0455 | - | - |
| 0.3817 | 9600 | 1.0555 | - | - |
| 0.3857 | 9700 | 1.0338 | - | - |
| 0.3897 | 9800 | 1.0624 | - | - |
| 0.3937 | 9900 | 1.0645 | - | - |
| 0.3976 | 10000 | 1.0622 | 1.0430 | nan |
| 0.4016 | 10100 | 1.0523 | - | - |
| 0.4056 | 10200 | 1.0697 | - | - |
| 0.4096 | 10300 | 1.0733 | - | - |
| 0.4136 | 10400 | 1.0415 | - | - |
| 0.4175 | 10500 | 1.0644 | - | - |
| 0.4215 | 10600 | 1.0404 | - | - |
| 0.4255 | 10700 | 1.026 | - | - |
| 0.4295 | 10800 | 1.0408 | - | - |
| 0.4334 | 10900 | 1.0602 | - | - |
| 0.4374 | 11000 | 1.0538 | - | - |
| 0.4414 | 11100 | 1.0396 | - | - |
| 0.4454 | 11200 | 1.0852 | - | - |
| 0.4493 | 11300 | 1.0412 | - | - |
| 0.4533 | 11400 | 1.0249 | - | - |
| 0.4573 | 11500 | 1.024 | - | - |
| 0.4613 | 11600 | 1.0494 | - | - |
| 0.4652 | 11700 | 1.0461 | - | - |
| 0.4692 | 11800 | 1.027 | - | - |
| 0.4732 | 11900 | 1.0802 | - | - |
| 0.4772 | 12000 | 1.0402 | - | - |
| 0.4812 | 12100 | 1.026 | - | - |
| 0.4851 | 12200 | 1.0565 | - | - |
| 0.4891 | 12300 | 1.0416 | - | - |
| 0.4931 | 12400 | 1.0452 | - | - |
| 0.4971 | 12500 | 1.0425 | 1.0376 | nan |
| 0.5010 | 12600 | 1.0319 | - | - |
| 0.5050 | 12700 | 1.0422 | - | - |
| 0.5090 | 12800 | 1.0261 | - | - |
| 0.5130 | 12900 | 1.0498 | - | - |
| 0.5169 | 13000 | 1.0189 | - | - |
| 0.5209 | 13100 | 1.0309 | - | - |
| 0.5249 | 13200 | 1.0509 | - | - |
| 0.5289 | 13300 | 1.0524 | - | - |
| 0.5328 | 13400 | 1.0516 | - | - |
| 0.5368 | 13500 | 1.0104 | - | - |
| 0.5408 | 13600 | 1.0394 | - | - |
| 0.5448 | 13700 | 1.0473 | - | - |
| 0.5488 | 13800 | 1.0151 | - | - |
| 0.5527 | 13900 | 1.0379 | - | - |
| 0.5567 | 14000 | 1.0556 | - | - |
| 0.5607 | 14100 | 1.0465 | - | - |
| 0.5647 | 14200 | 1.046 | - | - |
| 0.5686 | 14300 | 1.0211 | - | - |
| 0.5726 | 14400 | 1.0234 | - | - |
| 0.5766 | 14500 | 1.0215 | - | - |
| 0.5806 | 14600 | 1.0445 | - | - |
| 0.5845 | 14700 | 1.0229 | - | - |
| 0.5885 | 14800 | 1.0383 | - | - |
| 0.5925 | 14900 | 1.0491 | - | - |
| 0.5965 | 15000 | 1.0425 | 1.0303 | nan |
| 0.6004 | 15100 | 1.052 | - | - |
| 0.6044 | 15200 | 1.0281 | - | - |
| 0.6084 | 15300 | 1.0288 | - | - |
| 0.6124 | 15400 | 1.0096 | - | - |
| 0.6164 | 15500 | 1.0447 | - | - |
| 0.6203 | 15600 | 1.038 | - | - |
| 0.6243 | 15700 | 1.0061 | - | - |
| 0.6283 | 15800 | 1.0255 | - | - |
| 0.6323 | 15900 | 1.0246 | - | - |
| 0.6362 | 16000 | 1.0255 | - | - |
| 0.6402 | 16100 | 1.0271 | - | - |
| 0.6442 | 16200 | 1.0163 | - | - |
| 0.6482 | 16300 | 1.0381 | - | - |
| 0.6521 | 16400 | 1.0333 | - | - |
| 0.6561 | 16500 | 1.0161 | - | - |
| 0.6601 | 16600 | 1.03 | - | - |
| 0.6641 | 16700 | 1.0299 | - | - |
| 0.6680 | 16800 | 1.0191 | - | - |
| 0.6720 | 16900 | 1.0268 | - | - |
| 0.6760 | 17000 | 1.0177 | - | - |
| 0.6800 | 17100 | 1.0157 | - | - |
| 0.6840 | 17200 | 1.0382 | - | - |
| 0.6879 | 17300 | 1.0306 | - | - |
| 0.6919 | 17400 | 1.0231 | - | - |
| 0.6959 | 17500 | 1.0456 | 1.0231 | nan |
| 0.6999 | 17600 | 0.9993 | - | - |
| 0.7038 | 17700 | 1.0212 | - | - |
| 0.7078 | 17800 | 1.0114 | - | - |
| 0.7118 | 17900 | 1.0169 | - | - |
| 0.7158 | 18000 | 1.0115 | - | - |
| 0.7197 | 18100 | 1.019 | - | - |
| 0.7237 | 18200 | 1.016 | - | - |
| 0.7277 | 18300 | 1.0252 | - | - |
| 0.7317 | 18400 | 1.0374 | - | - |
| 0.7356 | 18500 | 1.0147 | - | - |
| 0.7396 | 18600 | 1.0302 | - | - |
| 0.7436 | 18700 | 1.0203 | - | - |
| 0.7476 | 18800 | 1.0395 | - | - |
| 0.7516 | 18900 | 1.0486 | - | - |
| 0.7555 | 19000 | 1.0321 | - | - |
| 0.7595 | 19100 | 1.0463 | - | - |
| 0.7635 | 19200 | 1.0124 | - | - |
| 0.7675 | 19300 | 1.0026 | - | - |
| 0.7714 | 19400 | 1.0474 | - | - |
| 0.7754 | 19500 | 1.0314 | - | - |
| 0.7794 | 19600 | 1.0183 | - | - |
| 0.7834 | 19700 | 1.0067 | - | - |
| 0.7873 | 19800 | 1.0179 | - | - |
| 0.7913 | 19900 | 1.0388 | - | - |
| 0.7953 | 20000 | 1.0063 | 1.0157 | nan |
| 0.7993 | 20100 | 1.0175 | - | - |
| 0.8032 | 20200 | 1.0349 | - | - |
| 0.8072 | 20300 | 1.0125 | - | - |
| 0.8112 | 20400 | 0.9982 | - | - |
| 0.8152 | 20500 | 1.0428 | - | - |
| 0.8192 | 20600 | 1.0526 | - | - |
| 0.8231 | 20700 | 1.0424 | - | - |
| 0.8271 | 20800 | 1.008 | - | - |
| 0.8311 | 20900 | 1.0186 | - | - |
| 0.8351 | 21000 | 1.0256 | - | - |
| 0.8390 | 21100 | 1.0125 | - | - |
| 0.8430 | 21200 | 1.0286 | - | - |
| 0.8470 | 21300 | 1.0358 | - | - |
| 0.8510 | 21400 | 1.0189 | - | - |
| 0.8549 | 21500 | 0.9861 | - | - |
| 0.8589 | 21600 | 0.9934 | - | - |
| 0.8629 | 21700 | 1.0211 | - | - |
| 0.8669 | 21800 | 1.0221 | - | - |
| 0.8708 | 21900 | 1.0302 | - | - |
| 0.8748 | 22000 | 1.0145 | - | - |
| 0.8788 | 22100 | 1.0027 | - | - |
| 0.8828 | 22200 | 1.0084 | - | - |
| 0.8868 | 22300 | 1.0334 | - | - |
| 0.8907 | 22400 | 1.0025 | - | - |
| 0.8947 | 22500 | 1.0175 | 1.0102 | nan |
| 0.8987 | 22600 | 1.0 | - | - |
| 0.9027 | 22700 | 1.0268 | - | - |
| 0.9066 | 22800 | 0.9795 | - | - |
| 0.9106 | 22900 | 1.0071 | - | - |
| 0.9146 | 23000 | 1.0141 | - | - |
| 0.9186 | 23100 | 1.006 | - | - |
| 0.9225 | 23200 | 1.0327 | - | - |
| 0.9265 | 23300 | 1.0016 | - | - |
| 0.9305 | 23400 | 1.0313 | - | - |
| 0.9345 | 23500 | 1.021 | - | - |
| 0.9384 | 23600 | 1.0217 | - | - |
| 0.9424 | 23700 | 1.0191 | - | - |
| 0.9464 | 23800 | 1.0238 | - | - |
| 0.9504 | 23900 | 1.0469 | - | - |
| 0.9544 | 24000 | 1.0338 | - | - |
| 0.9583 | 24100 | 1.0043 | - | - |
| 0.9623 | 24200 | 1.0054 | - | - |
| 0.9663 | 24300 | 1.0264 | - | - |
| 0.9703 | 24400 | 1.024 | - | - |
| 0.9742 | 24500 | 1.0172 | - | - |
| 0.9782 | 24600 | 1.0127 | - | - |
| 0.9822 | 24700 | 1.013 | - | - |
| 0.9862 | 24800 | 1.0135 | - | - |
| 0.9901 | 24900 | 1.0145 | - | - |
| **0.9941** | **25000** | **1.0184** | **1.0082** | **nan** |
| 0.9981 | 25100 | 1.0305 | - | - |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.43.3
- PyTorch: 2.2.2+cu121
- 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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