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validadted_faLabse_withCosSimb

Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:172826
loss:CosineSimilarityLoss
text-embeddings-inference
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:172826
- loss:CosineSimilarityLoss
base_model: sentence-transformers/LaBSE
widget:
- source_sentence: How do you make Yahoo your homepage?
sentences:
- چگونه ویکی پدیا بدون تبلیغ در وب سایت خود درآمد کسب می کند؟
- چگونه می توانم برای امتحان INS 21 آماده شوم؟
- How can I make Yahoo my homepage on my browser?
- source_sentence: کدام VPN رایگان در چین کار می کند؟
sentences:
- VPN های رایگان که در چین کار می کنند چیست؟
- How can I stop masturbations?
- آیا مدرسه خلاقیت را می کشد؟
- source_sentence: چند روش خوب برای کاهش وزن چیست؟
sentences:
- چگونه می توانم یک کتاب خوب بنویسم؟
- من اضافه وزن دارمچگونه می توانم وزن کم کنم؟
- آیا می توانید ببینید چه کسی داستانهای اینستاگرام شما را مشاهده می کند؟
- source_sentence: چگونه می توان یک Dell Inspiron 1525 را به تنظیمات کارخانه بازگرداند؟
sentences:
- چگونه می توان یک Dell Inspiron B130 را به تنظیمات کارخانه بازگرداند؟
- مبدل چیست؟
- چگونه زندگی شما بعد از تشخیص HIV مثبت تغییر کرد؟
- source_sentence: داشتن هزاران دنبال کننده در Quora چگونه است؟
sentences:
- چگونه Airprint HP OfficeJet 4620 با HP LaserJet Enterprise M606X مقایسه می شود؟
- چه چیزی است که ده ها هزار دنبال کننده در Quora داشته باشید؟
- اگر هند واردات همه محصولات چینی را ممنوع کند ، چه می شود؟
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/LaBSE
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 836121a0533e5664b21c7aacc5d22951f2b8b25b -->
- **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/validadted_faLabse_withCosSimb")
# Run inference
sentences = [
'داشتن هزاران دنبال کننده در Quora چگونه است؟',
'چه چیزی است که ده ها هزار دنبال کننده در Quora داشته باشید؟',
'چگونه Airprint HP OfficeJet 4620 با HP LaserJet Enterprise M606X مقایسه می شود؟',
]
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: 172,826 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.2 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.47 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 0.76</li><li>mean: 0.95</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------|:---------------------------------------------------------------|:--------------------------------|
| <code>تفاوت بین تحلیلگر تحقیقات بازار و تحلیلگر تجارت چیست؟</code> | <code>تفاوت بین تحقیقات بازاریابی و تحلیلگر تجارت چیست؟</code> | <code>0.982593297958374</code> |
| <code>خوردن چه چیزی باعث دل درد میشود؟</code> | <code>چه چیزی باعث رفع دل درد میشود؟</code> | <code>0.9582258462905884</code> |
| <code>بهترین نرم افزار ویرایش ویدیویی کدام است؟</code> | <code>بهترین نرم افزار برای ویرایش ویدیو چیست؟</code> | <code>0.9890836477279663</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 12
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `push_to_hub`: True
- `hub_model_id`: codersan/validadted_faLabse_withCosSimb
- `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`: 12
- `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`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `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`: 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}
- `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/validadted_faLabse_withCosSimb
- `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 | - |
| 0.0069 | 100 | 0.0313 |
| 0.0139 | 200 | 0.0264 |
| 0.0208 | 300 | 0.0163 |
| 0.0278 | 400 | 0.0092 |
| 0.0347 | 500 | 0.0044 |
| 0.0417 | 600 | 0.0018 |
| 0.0486 | 700 | 0.0011 |
| 0.0555 | 800 | 0.0007 |
| 0.0625 | 900 | 0.0006 |
| 0.0694 | 1000 | 0.0006 |
| 0.0764 | 1100 | 0.0006 |
| 0.0833 | 1200 | 0.0005 |
| 0.0903 | 1300 | 0.0005 |
| 0.0972 | 1400 | 0.0005 |
| 0.1041 | 1500 | 0.0005 |
| 0.1111 | 1600 | 0.0005 |
| 0.1180 | 1700 | 0.0004 |
| 0.1250 | 1800 | 0.0004 |
| 0.1319 | 1900 | 0.0004 |
| 0.1389 | 2000 | 0.0004 |
| 0.1458 | 2100 | 0.0004 |
| 0.1527 | 2200 | 0.0004 |
| 0.1597 | 2300 | 0.0004 |
| 0.1666 | 2400 | 0.0004 |
| 0.1736 | 2500 | 0.0003 |
| 0.1805 | 2600 | 0.0004 |
| 0.1875 | 2700 | 0.0003 |
| 0.1944 | 2800 | 0.0003 |
| 0.2013 | 2900 | 0.0003 |
| 0.2083 | 3000 | 0.0003 |
| 0.2152 | 3100 | 0.0003 |
| 0.2222 | 3200 | 0.0003 |
| 0.2291 | 3300 | 0.0003 |
| 0.2361 | 3400 | 0.0003 |
| 0.2430 | 3500 | 0.0003 |
| 0.2499 | 3600 | 0.0003 |
| 0.2569 | 3700 | 0.0003 |
| 0.2638 | 3800 | 0.0003 |
| 0.2708 | 3900 | 0.0003 |
| 0.2777 | 4000 | 0.0003 |
| 0.2847 | 4100 | 0.0003 |
| 0.2916 | 4200 | 0.0003 |
| 0.2985 | 4300 | 0.0003 |
| 0.3055 | 4400 | 0.0003 |
| 0.3124 | 4500 | 0.0002 |
| 0.3194 | 4600 | 0.0002 |
| 0.3263 | 4700 | 0.0002 |
| 0.3333 | 4800 | 0.0003 |
| 0.3402 | 4900 | 0.0002 |
| 0.3471 | 5000 | 0.0002 |
| 0.3541 | 5100 | 0.0002 |
| 0.3610 | 5200 | 0.0002 |
| 0.3680 | 5300 | 0.0002 |
| 0.3749 | 5400 | 0.0002 |
| 0.3819 | 5500 | 0.0002 |
| 0.3888 | 5600 | 0.0002 |
| 0.3958 | 5700 | 0.0002 |
| 0.4027 | 5800 | 0.0002 |
| 0.4096 | 5900 | 0.0002 |
| 0.4166 | 6000 | 0.0002 |
| 0.4235 | 6100 | 0.0002 |
| 0.4305 | 6200 | 0.0002 |
| 0.4374 | 6300 | 0.0002 |
| 0.4444 | 6400 | 0.0002 |
| 0.4513 | 6500 | 0.0002 |
| 0.4582 | 6600 | 0.0002 |
| 0.4652 | 6700 | 0.0002 |
| 0.4721 | 6800 | 0.0002 |
| 0.4791 | 6900 | 0.0002 |
| 0.4860 | 7000 | 0.0002 |
| 0.4930 | 7100 | 0.0002 |
| 0.4999 | 7200 | 0.0002 |
| 0.5068 | 7300 | 0.0002 |
| 0.5138 | 7400 | 0.0002 |
| 0.5207 | 7500 | 0.0002 |
| 0.5277 | 7600 | 0.0002 |
| 0.5346 | 7700 | 0.0002 |
| 0.5416 | 7800 | 0.0002 |
| 0.5485 | 7900 | 0.0002 |
| 0.5554 | 8000 | 0.0002 |
| 0.5624 | 8100 | 0.0002 |
| 0.5693 | 8200 | 0.0002 |
| 0.5763 | 8300 | 0.0002 |
| 0.5832 | 8400 | 0.0002 |
| 0.5902 | 8500 | 0.0002 |
| 0.5971 | 8600 | 0.0002 |
| 0.6040 | 8700 | 0.0002 |
| 0.6110 | 8800 | 0.0002 |
| 0.6179 | 8900 | 0.0002 |
| 0.6249 | 9000 | 0.0002 |
| 0.6318 | 9100 | 0.0002 |
| 0.6388 | 9200 | 0.0002 |
| 0.6457 | 9300 | 0.0002 |
| 0.6526 | 9400 | 0.0002 |
| 0.6596 | 9500 | 0.0002 |
| 0.6665 | 9600 | 0.0002 |
| 0.6735 | 9700 | 0.0002 |
| 0.6804 | 9800 | 0.0002 |
| 0.6874 | 9900 | 0.0002 |
| 0.6943 | 10000 | 0.0002 |
| 0.7012 | 10100 | 0.0002 |
| 0.7082 | 10200 | 0.0002 |
| 0.7151 | 10300 | 0.0002 |
| 0.7221 | 10400 | 0.0002 |
| 0.7290 | 10500 | 0.0002 |
| 0.7360 | 10600 | 0.0002 |
| 0.7429 | 10700 | 0.0002 |
| 0.7498 | 10800 | 0.0002 |
| 0.7568 | 10900 | 0.0002 |
| 0.7637 | 11000 | 0.0002 |
| 0.7707 | 11100 | 0.0002 |
| 0.7776 | 11200 | 0.0002 |
| 0.7846 | 11300 | 0.0002 |
| 0.7915 | 11400 | 0.0002 |
| 0.7984 | 11500 | 0.0002 |
| 0.8054 | 11600 | 0.0002 |
| 0.8123 | 11700 | 0.0002 |
| 0.8193 | 11800 | 0.0002 |
| 0.8262 | 11900 | 0.0002 |
| 0.8332 | 12000 | 0.0002 |
| 0.8401 | 12100 | 0.0002 |
| 0.8470 | 12200 | 0.0002 |
| 0.8540 | 12300 | 0.0002 |
| 0.8609 | 12400 | 0.0002 |
| 0.8679 | 12500 | 0.0002 |
| 0.8748 | 12600 | 0.0002 |
| 0.8818 | 12700 | 0.0001 |
| 0.8887 | 12800 | 0.0002 |
| 0.8956 | 12900 | 0.0002 |
| 0.9026 | 13000 | 0.0002 |
| 0.9095 | 13100 | 0.0001 |
| 0.9165 | 13200 | 0.0002 |
| 0.9234 | 13300 | 0.0002 |
| 0.9304 | 13400 | 0.0002 |
| 0.9373 | 13500 | 0.0001 |
| 0.9442 | 13600 | 0.0002 |
| 0.9512 | 13700 | 0.0002 |
| 0.9581 | 13800 | 0.0001 |
| 0.9651 | 13900 | 0.0001 |
| 0.9720 | 14000 | 0.0002 |
| 0.9790 | 14100 | 0.0002 |
| 0.9859 | 14200 | 0.0001 |
| 0.9928 | 14300 | 0.0001 |
| 0.9998 | 14400 | 0.0001 |
| 1.0067 | 14500 | 0.0001 |
| 1.0137 | 14600 | 0.0001 |
| 1.0206 | 14700 | 0.0001 |
| 1.0276 | 14800 | 0.0002 |
| 1.0345 | 14900 | 0.0001 |
| 1.0414 | 15000 | 0.0002 |
| 1.0484 | 15100 | 0.0002 |
| 1.0553 | 15200 | 0.0001 |
| 1.0623 | 15300 | 0.0002 |
| 1.0692 | 15400 | 0.0001 |
| 1.0762 | 15500 | 0.0001 |
| 1.0831 | 15600 | 0.0001 |
| 1.0901 | 15700 | 0.0001 |
| 1.0970 | 15800 | 0.0001 |
| 1.1039 | 15900 | 0.0001 |
| 1.1109 | 16000 | 0.0001 |
| 1.1178 | 16100 | 0.0002 |
| 1.1248 | 16200 | 0.0001 |
| 1.1317 | 16300 | 0.0002 |
| 1.1387 | 16400 | 0.0001 |
| 1.1456 | 16500 | 0.0001 |
| 1.1525 | 16600 | 0.0001 |
| 1.1595 | 16700 | 0.0001 |
| 1.1664 | 16800 | 0.0001 |
| 1.1734 | 16900 | 0.0001 |
| 1.1803 | 17000 | 0.0002 |
| 1.1873 | 17100 | 0.0001 |
| 1.1942 | 17200 | 0.0001 |
| 1.2011 | 17300 | 0.0001 |
| 1.2081 | 17400 | 0.0001 |
| 1.2150 | 17500 | 0.0001 |
| 1.2220 | 17600 | 0.0001 |
| 1.2289 | 17700 | 0.0001 |
| 1.2359 | 17800 | 0.0001 |
| 1.2428 | 17900 | 0.0001 |
| 1.2497 | 18000 | 0.0001 |
| 1.2567 | 18100 | 0.0001 |
| 1.2636 | 18200 | 0.0001 |
| 1.2706 | 18300 | 0.0001 |
| 1.2775 | 18400 | 0.0001 |
| 1.2845 | 18500 | 0.0001 |
| 1.2914 | 18600 | 0.0001 |
| 1.2983 | 18700 | 0.0001 |
| 1.3053 | 18800 | 0.0001 |
| 1.3122 | 18900 | 0.0001 |
| 1.3192 | 19000 | 0.0001 |
| 1.3261 | 19100 | 0.0001 |
| 1.3331 | 19200 | 0.0001 |
| 1.3400 | 19300 | 0.0001 |
| 1.3469 | 19400 | 0.0001 |
| 1.3539 | 19500 | 0.0001 |
| 1.3608 | 19600 | 0.0001 |
| 1.3678 | 19700 | 0.0001 |
| 1.3747 | 19800 | 0.0001 |
| 1.3817 | 19900 | 0.0001 |
| 1.3886 | 20000 | 0.0001 |
| 1.3955 | 20100 | 0.0001 |
| 1.4025 | 20200 | 0.0001 |
| 1.4094 | 20300 | 0.0001 |
| 1.4164 | 20400 | 0.0001 |
| 1.4233 | 20500 | 0.0001 |
| 1.4303 | 20600 | 0.0001 |
| 1.4372 | 20700 | 0.0001 |
| 1.4441 | 20800 | 0.0001 |
| 1.4511 | 20900 | 0.0001 |
| 1.4580 | 21000 | 0.0001 |
| 1.4650 | 21100 | 0.0001 |
| 1.4719 | 21200 | 0.0001 |
| 1.4789 | 21300 | 0.0001 |
| 1.4858 | 21400 | 0.0001 |
| 1.4927 | 21500 | 0.0001 |
| 1.4997 | 21600 | 0.0001 |
| 1.5066 | 21700 | 0.0001 |
| 1.5136 | 21800 | 0.0001 |
| 1.5205 | 21900 | 0.0001 |
| 1.5275 | 22000 | 0.0001 |
| 1.5344 | 22100 | 0.0001 |
| 1.5413 | 22200 | 0.0001 |
| 1.5483 | 22300 | 0.0001 |
| 1.5552 | 22400 | 0.0001 |
| 1.5622 | 22500 | 0.0001 |
| 1.5691 | 22600 | 0.0001 |
| 1.5761 | 22700 | 0.0001 |
| 1.5830 | 22800 | 0.0001 |
| 1.5899 | 22900 | 0.0001 |
| 1.5969 | 23000 | 0.0001 |
| 1.6038 | 23100 | 0.0001 |
| 1.6108 | 23200 | 0.0001 |
| 1.6177 | 23300 | 0.0001 |
| 1.6247 | 23400 | 0.0001 |
| 1.6316 | 23500 | 0.0001 |
| 1.6385 | 23600 | 0.0001 |
| 1.6455 | 23700 | 0.0001 |
| 1.6524 | 23800 | 0.0001 |
| 1.6594 | 23900 | 0.0001 |
| 1.6663 | 24000 | 0.0001 |
| 1.6733 | 24100 | 0.0001 |
| 1.6802 | 24200 | 0.0001 |
| 1.6871 | 24300 | 0.0001 |
| 1.6941 | 24400 | 0.0001 |
| 1.7010 | 24500 | 0.0001 |
| 1.7080 | 24600 | 0.0001 |
| 1.7149 | 24700 | 0.0001 |
| 1.7219 | 24800 | 0.0001 |
| 1.7288 | 24900 | 0.0001 |
| 1.7357 | 25000 | 0.0001 |
| 1.7427 | 25100 | 0.0001 |
| 1.7496 | 25200 | 0.0001 |
| 1.7566 | 25300 | 0.0001 |
| 1.7635 | 25400 | 0.0001 |
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| 4.9851 | 71800 | 0.0 |
| 4.9920 | 71900 | 0.0001 |
| 4.9990 | 72000 | 0.0 |
</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",
}
```
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