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FaLabse_Mizan4

Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1021596
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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FaLabse_Mizan4 / README.md
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1021596
- loss:MultipleNegativesRankingLoss
base_model: codersan/FaLabse
widget:
- source_sentence: Most women can't understand why this happens.
sentences:
- 'بیشتر زنان دلیل این کار را درک نمی‌کنند '
- ' سخت از خود در غضب بود که آن چه را به آسانی و صراحت می‌توانست نزد خود تصمیم بگیرد،
قادر به بیان آن در حضور شاهزاده خانم تورسکی نیست. زیرا این زن در نظر او تجسم همان
نیروی بیدادگری بود که بر زندگی ظاهری او حکومت می‌کرد و مانع ابراز عشق و عفو و
نمایاندن احساساتش بود.'
- 'آقای تالبویز: چه روزهای خوشی، عجب روزهای ‌خوشی!'
- source_sentence: to government offices, to the post office, and to the Governor's.
sentences:
- ناخوشی را تقویت می‌کند.
- به ادارات دولتی و اداره پست و سپس نزد استاندار رفت.
- اما به حال طبیعی نبود و در حالی که بازوی شوهرش را گرفته بود، گفتی که در عالم رؤیا
قدم بر میدارد.
- source_sentence: Even as she did so a sound checked her for an instant ' the unmistakable
bang of a window shutting, somewhere in Mrs Semprill's house.
sentences:
- در همین آن صدائی به گوشش رسید که بدون شک صدای بسته شدن ‌پنجره خانه خانم سمپریل
بود!
- این کارم گذشتن از مرز بود.
- به همین دلیل هیچ کس بهتر از او برای تربیت مردی که حافظ تمامی خصوصیات نیک خانوادگی
باشد، وجود نداشت.
- source_sentence: 'It signifies God: done this day by my hand.'
sentences:
- معنی آن مهر این است که 3 خدا، امروز به دست من انجام شد.
- همه یکدیگر را بوسیدند
- این نشو نه‌ی جادوگرهای تبه کاره
- source_sentence: If this were continued, the barricade was no longer tenable.
sentences:
- اگر این کار مداومت می‌یافت، سنگر قادر به مقاومت نمی‌بود.
- هر دو با هم به زمین می‌غلتیدند.
- خوب، در این لحظه او یک محافظ داشت.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on codersan/FaLabse
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [codersan/FaLabse](https://huggingface.co/codersan/FaLabse). 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:** [codersan/FaLabse](https://huggingface.co/codersan/FaLabse) <!-- at revision 0fe1341c6962d7fe2ea375d90f9f55f34e395bcd -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **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': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): 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("codersan/FaLabse_Mizan4")
# Run inference
sentences = [
'If this were continued, the barricade was no longer tenable.',
'اگر این کار مداومت می\u200cیافت، سنگر قادر به مقاومت نمی\u200cبود.',
'خوب، در این لحظه او یک محافظ داشت.',
]
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: 1,021,596 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: 3 tokens</li><li>mean: 16.37 tokens</li><li>max: 85 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.63 tokens</li><li>max: 81 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
| <code>They arose to obey.</code> | <code>دختران برای اطاعت امر پدر از جا برخاستند.</code> |
| <code>You'll know it all in time</code> | <code>همه چیز را بم وقع خواهی دانست.</code> |
| <code>She is in hysterics up there, and moans and says that we have been 'shamed and disgraced.</code> | <code>او هر لحظه گرفتار یک‌ وضع است، زارزار گریه می‌کند. می‌گوید به ما توهین کرده‌اند، حیثیتمان را لکه‌دار نمودند.</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
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `push_to_hub`: True
- `hub_model_id`: codersan/FaLabse_Mizan4
- `eval_on_start`: 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`: 32
- `per_device_eval_batch_size`: 8
- `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
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `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`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: codersan/FaLabse_Mizan4
- `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
- `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`: True
- `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 |
|:----------:|:-------:|:-------------:|
| 0 | 0 | - |
| 0.0031 | 100 | 0.1023 |
| 0.0063 | 200 | 0.1162 |
| 0.0094 | 300 | 0.0976 |
| **0.0125** | **400** | **0.088** |
| 0.0157 | 500 | 0.0691 |
| 0.0188 | 600 | 0.0678 |
| 0.0219 | 700 | 0.082 |
| 0.0251 | 800 | 0.08 |
| 0.0282 | 900 | 0.0758 |
| 0.0313 | 1000 | 0.0763 |
| 0.0345 | 1100 | 0.0786 |
| 0.0376 | 1200 | 0.0666 |
| 0.0407 | 1300 | 0.0722 |
| 0.0439 | 1400 | 0.0638 |
| 0.0470 | 1500 | 0.0615 |
| 0.0501 | 1600 | 0.0623 |
| 0.0532 | 1700 | 0.0639 |
| 0.0564 | 1800 | 0.0692 |
| 0.0595 | 1900 | 0.0625 |
| 0.0626 | 2000 | 0.0774 |
| 0.0658 | 2100 | 0.06 |
| 0.0689 | 2200 | 0.0543 |
| 0.0720 | 2300 | 0.0611 |
| 0.0752 | 2400 | 0.0697 |
| 0.0783 | 2500 | 0.0703 |
| 0.0814 | 2600 | 0.058 |
| 0.0846 | 2700 | 0.075 |
| 0.0877 | 2800 | 0.062 |
| 0.0908 | 2900 | 0.0756 |
| 0.0940 | 3000 | 0.0668 |
| 0.0971 | 3100 | 0.054 |
| 0.1002 | 3200 | 0.0626 |
| 0.1034 | 3300 | 0.0645 |
| 0.1065 | 3400 | 0.0714 |
| 0.1096 | 3500 | 0.0644 |
| 0.1128 | 3600 | 0.0693 |
| 0.1159 | 3700 | 0.0734 |
| 0.1190 | 3800 | 0.0622 |
| 0.1222 | 3900 | 0.0741 |
| 0.1253 | 4000 | 0.0761 |
| 0.1284 | 4100 | 0.0582 |
| 0.1316 | 4200 | 0.0804 |
| 0.1347 | 4300 | 0.0708 |
| 0.1378 | 4400 | 0.0734 |
| 0.1410 | 4500 | 0.0709 |
| 0.1441 | 4600 | 0.0759 |
| 0.1472 | 4700 | 0.085 |
| 0.1504 | 4800 | 0.0573 |
| 0.1535 | 4900 | 0.056 |
| 0.1566 | 5000 | 0.0601 |
| 0.1597 | 5100 | 0.0596 |
| 0.1629 | 5200 | 0.079 |
| 0.1660 | 5300 | 0.0679 |
| 0.1691 | 5400 | 0.0553 |
| 0.1723 | 5500 | 0.0677 |
| 0.1754 | 5600 | 0.0795 |
| 0.1785 | 5700 | 0.0779 |
| 0.1817 | 5800 | 0.0599 |
| 0.1848 | 5900 | 0.0667 |
| 0.1879 | 6000 | 0.064 |
| 0.1911 | 6100 | 0.0637 |
| 0.1942 | 6200 | 0.0747 |
| 0.1973 | 6300 | 0.0829 |
| 0.2005 | 6400 | 0.0589 |
| 0.2036 | 6500 | 0.0623 |
| 0.2067 | 6600 | 0.0589 |
| 0.2099 | 6700 | 0.0648 |
| 0.2130 | 6800 | 0.0527 |
| 0.2161 | 6900 | 0.0519 |
| 0.2193 | 7000 | 0.0668 |
| 0.2224 | 7100 | 0.0729 |
| 0.2255 | 7200 | 0.0627 |
| 0.2287 | 7300 | 0.0539 |
| 0.2318 | 7400 | 0.055 |
| 0.2349 | 7500 | 0.0663 |
| 0.2381 | 7600 | 0.0589 |
| 0.2412 | 7700 | 0.0555 |
| 0.2443 | 7800 | 0.0875 |
| 0.2475 | 7900 | 0.055 |
| 0.2506 | 8000 | 0.0584 |
| 0.2537 | 8100 | 0.0607 |
| 0.2569 | 8200 | 0.0551 |
| 0.2600 | 8300 | 0.0527 |
| 0.2631 | 8400 | 0.0773 |
| 0.2662 | 8500 | 0.0696 |
| 0.2694 | 8600 | 0.062 |
| 0.2725 | 8700 | 0.0716 |
| 0.2756 | 8800 | 0.06 |
| 0.2788 | 8900 | 0.0536 |
| 0.2819 | 9000 | 0.0604 |
| 0.2850 | 9100 | 0.0563 |
| 0.2882 | 9200 | 0.0734 |
| 0.2913 | 9300 | 0.0714 |
| 0.2944 | 9400 | 0.0658 |
| 0.2976 | 9500 | 0.0623 |
| 0.3007 | 9600 | 0.0713 |
| 0.3038 | 9700 | 0.0674 |
| 0.3070 | 9800 | 0.0708 |
| 0.3101 | 9900 | 0.0579 |
| 0.3132 | 10000 | 0.0616 |
| 0.3164 | 10100 | 0.0653 |
| 0.3195 | 10200 | 0.0614 |
| 0.3226 | 10300 | 0.0626 |
| 0.3258 | 10400 | 0.0611 |
| 0.3289 | 10500 | 0.0521 |
| 0.3320 | 10600 | 0.056 |
| 0.3352 | 10700 | 0.0761 |
| 0.3383 | 10800 | 0.0629 |
| 0.3414 | 10900 | 0.0658 |
| 0.3446 | 11000 | 0.0576 |
| 0.3477 | 11100 | 0.0483 |
| 0.3508 | 11200 | 0.0654 |
| 0.3540 | 11300 | 0.0602 |
| 0.3571 | 11400 | 0.065 |
| 0.3602 | 11500 | 0.0787 |
| 0.3634 | 11600 | 0.0634 |
| 0.3665 | 11700 | 0.0678 |
| 0.3696 | 11800 | 0.0758 |
| 0.3727 | 11900 | 0.0637 |
| 0.3759 | 12000 | 0.0577 |
| 0.3790 | 12100 | 0.0572 |
| 0.3821 | 12200 | 0.0614 |
| 0.3853 | 12300 | 0.0685 |
| 0.3884 | 12400 | 0.0641 |
| 0.3915 | 12500 | 0.0583 |
| 0.3947 | 12600 | 0.0502 |
| 0.3978 | 12700 | 0.0481 |
| 0.4009 | 12800 | 0.0546 |
| 0.4041 | 12900 | 0.0664 |
| 0.4072 | 13000 | 0.0699 |
| 0.4103 | 13100 | 0.0513 |
| 0.4135 | 13200 | 0.0423 |
| 0.4166 | 13300 | 0.0554 |
| 0.4197 | 13400 | 0.0592 |
| 0.4229 | 13500 | 0.0457 |
| 0.4260 | 13600 | 0.0612 |
| 0.4291 | 13700 | 0.0507 |
| 0.4323 | 13800 | 0.0592 |
| 0.4354 | 13900 | 0.0566 |
| 0.4385 | 14000 | 0.0806 |
| 0.4417 | 14100 | 0.0648 |
| 0.4448 | 14200 | 0.0535 |
| 0.4479 | 14300 | 0.0748 |
| 0.4511 | 14400 | 0.0488 |
| 0.4542 | 14500 | 0.0539 |
| 0.4573 | 14600 | 0.0597 |
| 0.4605 | 14700 | 0.065 |
| 0.4636 | 14800 | 0.0594 |
| 0.4667 | 14900 | 0.05 |
| 0.4699 | 15000 | 0.0488 |
| 0.4730 | 15100 | 0.0537 |
| 0.4761 | 15200 | 0.0396 |
| 0.4792 | 15300 | 0.0616 |
| 0.4824 | 15400 | 0.0605 |
| 0.4855 | 15500 | 0.0599 |
| 0.4886 | 15600 | 0.0616 |
| 0.4918 | 15700 | 0.0731 |
| 0.4949 | 15800 | 0.0654 |
| 0.4980 | 15900 | 0.0463 |
| 0.5012 | 16000 | 0.0463 |
| 0.5043 | 16100 | 0.0594 |
| 0.5074 | 16200 | 0.0575 |
| 0.5106 | 16300 | 0.056 |
| 0.5137 | 16400 | 0.0542 |
| 0.5168 | 16500 | 0.052 |
| 0.5200 | 16600 | 0.0438 |
| 0.5231 | 16700 | 0.0675 |
| 0.5262 | 16800 | 0.0619 |
| 0.5294 | 16900 | 0.0515 |
| 0.5325 | 17000 | 0.0575 |
| 0.5356 | 17100 | 0.0568 |
| 0.5388 | 17200 | 0.0508 |
| 0.5419 | 17300 | 0.059 |
| 0.5450 | 17400 | 0.0505 |
| 0.5482 | 17500 | 0.0582 |
| 0.5513 | 17600 | 0.0574 |
| 0.5544 | 17700 | 0.0613 |
| 0.5576 | 17800 | 0.048 |
| 0.5607 | 17900 | 0.0553 |
| 0.5638 | 18000 | 0.0571 |
| 0.5670 | 18100 | 0.0543 |
| 0.5701 | 18200 | 0.0484 |
| 0.5732 | 18300 | 0.0763 |
| 0.5764 | 18400 | 0.056 |
| 0.5795 | 18500 | 0.0533 |
| 0.5826 | 18600 | 0.044 |
| 0.5857 | 18700 | 0.0515 |
| 0.5889 | 18800 | 0.0516 |
| 0.5920 | 18900 | 0.0586 |
| 0.5951 | 19000 | 0.0523 |
| 0.5983 | 19100 | 0.0733 |
| 0.6014 | 19200 | 0.0453 |
| 0.6045 | 19300 | 0.0663 |
| 0.6077 | 19400 | 0.0381 |
| 0.6108 | 19500 | 0.0568 |
| 0.6139 | 19600 | 0.0492 |
| 0.6171 | 19700 | 0.0489 |
| 0.6202 | 19800 | 0.0575 |
| 0.6233 | 19900 | 0.0642 |
| 0.6265 | 20000 | 0.0535 |
| 0.6296 | 20100 | 0.0598 |
| 0.6327 | 20200 | 0.0569 |
| 0.6359 | 20300 | 0.0513 |
| 0.6390 | 20400 | 0.0515 |
| 0.6421 | 20500 | 0.053 |
| 0.6453 | 20600 | 0.0569 |
| 0.6484 | 20700 | 0.0372 |
| 0.6515 | 20800 | 0.0464 |
| 0.6547 | 20900 | 0.0522 |
| 0.6578 | 21000 | 0.0427 |
| 0.6609 | 21100 | 0.0584 |
| 0.6641 | 21200 | 0.0616 |
| 0.6672 | 21300 | 0.0552 |
| 0.6703 | 21400 | 0.0509 |
| 0.6735 | 21500 | 0.0439 |
| 0.6766 | 21600 | 0.0762 |
| 0.6797 | 21700 | 0.0539 |
| 0.6829 | 21800 | 0.0475 |
| 0.6860 | 21900 | 0.0557 |
| 0.6891 | 22000 | 0.0421 |
| 0.6922 | 22100 | 0.0471 |
| 0.6954 | 22200 | 0.0398 |
| 0.6985 | 22300 | 0.0521 |
| 0.7016 | 22400 | 0.0472 |
| 0.7048 | 22500 | 0.0579 |
| 0.7079 | 22600 | 0.0539 |
| 0.7110 | 22700 | 0.0527 |
| 0.7142 | 22800 | 0.0677 |
| 0.7173 | 22900 | 0.0509 |
| 0.7204 | 23000 | 0.0478 |
| 0.7236 | 23100 | 0.0593 |
| 0.7267 | 23200 | 0.0419 |
| 0.7298 | 23300 | 0.0576 |
| 0.7330 | 23400 | 0.0485 |
| 0.7361 | 23500 | 0.0544 |
| 0.7392 | 23600 | 0.0537 |
| 0.7424 | 23700 | 0.0481 |
| 0.7455 | 23800 | 0.0597 |
| 0.7486 | 23900 | 0.0464 |
| 0.7518 | 24000 | 0.0537 |
| 0.7549 | 24100 | 0.0508 |
| 0.7580 | 24200 | 0.045 |
| 0.7612 | 24300 | 0.0337 |
| 0.7643 | 24400 | 0.0478 |
| 0.7674 | 24500 | 0.0495 |
| 0.7706 | 24600 | 0.0427 |
| 0.7737 | 24700 | 0.0596 |
| 0.7768 | 24800 | 0.0468 |
| 0.7800 | 24900 | 0.0404 |
| 0.7831 | 25000 | 0.0467 |
| 0.7862 | 25100 | 0.0514 |
| 0.7894 | 25200 | 0.0462 |
| 0.7925 | 25300 | 0.0401 |
| 0.7956 | 25400 | 0.0539 |
| 0.7987 | 25500 | 0.0541 |
| 0.8019 | 25600 | 0.0639 |
| 0.8050 | 25700 | 0.0392 |
| 0.8081 | 25800 | 0.0466 |
| 0.8113 | 25900 | 0.0543 |
| 0.8144 | 26000 | 0.0507 |
| 0.8175 | 26100 | 0.0465 |
| 0.8207 | 26200 | 0.0386 |
| 0.8238 | 26300 | 0.0606 |
| 0.8269 | 26400 | 0.0558 |
| 0.8301 | 26500 | 0.0488 |
| 0.8332 | 26600 | 0.0556 |
| 0.8363 | 26700 | 0.047 |
| 0.8395 | 26800 | 0.0548 |
| 0.8426 | 26900 | 0.0423 |
| 0.8457 | 27000 | 0.0529 |
| 0.8489 | 27100 | 0.0513 |
| 0.8520 | 27200 | 0.0432 |
| 0.8551 | 27300 | 0.0605 |
| 0.8583 | 27400 | 0.0448 |
| 0.8614 | 27500 | 0.0508 |
| 0.8645 | 27600 | 0.0578 |
| 0.8677 | 27700 | 0.0409 |
| 0.8708 | 27800 | 0.0487 |
| 0.8739 | 27900 | 0.058 |
| 0.8771 | 28000 | 0.0461 |
| 0.8802 | 28100 | 0.0389 |
| 0.8833 | 28200 | 0.0427 |
| 0.8865 | 28300 | 0.0473 |
| 0.8896 | 28400 | 0.061 |
| 0.8927 | 28500 | 0.0423 |
| 0.8958 | 28600 | 0.0435 |
| 0.8990 | 28700 | 0.0389 |
| 0.9021 | 28800 | 0.0466 |
| 0.9052 | 28900 | 0.042 |
| 0.9084 | 29000 | 0.0466 |
| 0.9115 | 29100 | 0.0412 |
| 0.9146 | 29200 | 0.0444 |
| 0.9178 | 29300 | 0.059 |
| 0.9209 | 29400 | 0.0466 |
| 0.9240 | 29500 | 0.0381 |
| 0.9272 | 29600 | 0.0408 |
| 0.9303 | 29700 | 0.0557 |
| 0.9334 | 29800 | 0.0567 |
| 0.9366 | 29900 | 0.0537 |
| 0.9397 | 30000 | 0.041 |
| 0.9428 | 30100 | 0.0383 |
| 0.9460 | 30200 | 0.0412 |
| 0.9491 | 30300 | 0.0489 |
| 0.9522 | 30400 | 0.046 |
| 0.9554 | 30500 | 0.0525 |
| 0.9585 | 30600 | 0.0493 |
| 0.9616 | 30700 | 0.0485 |
| 0.9648 | 30800 | 0.0532 |
| 0.9679 | 30900 | 0.0446 |
| 0.9710 | 31000 | 0.0372 |
| 0.9742 | 31100 | 0.0472 |
| 0.9773 | 31200 | 0.0399 |
| 0.9804 | 31300 | 0.0402 |
| 0.9836 | 31400 | 0.0372 |
| 0.9867 | 31500 | 0.0497 |
| 0.9898 | 31600 | 0.0432 |
| 0.9930 | 31700 | 0.0382 |
| 0.9961 | 31800 | 0.0475 |
| 0.9992 | 31900 | 0.0367 |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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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