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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: tomaarsen/mpnet-base-all-nli-triplet
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط
النظيفة
sentences:
- رجل يقدم عرضاً
- هناك رجل بالخارج قرب الشاطئ
- رجل يجلس على أريكه
- source_sentence: رجل يقفز إلى سريره القذر
sentences:
- السرير قذر.
- رجل يضحك أثناء غسيل الملابس
- الرجل على القمر
- source_sentence: الفتيات بالخارج
sentences:
- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
- فتيان يركبان في جولة متعة
- ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة تتحدث
إليهن
- source_sentence: الرجل يرتدي قميصاً أزرق.
sentences:
- رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة حمراء
مع الماء في الخلفية.
- كتاب القصص مفتوح
- رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة
شابة.
sentences:
- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
- رجل يستلقي على وجهه على مقعد في الحديقة.
- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.66986244175229
name: Pearson Cosine
- type: spearman_cosine
value: 0.675651628513557
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6943200977280434
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6839707658313092
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6973190148612566
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6872926092972673
name: Spearman Euclidean
- type: pearson_dot
value: 0.5534197296097646
name: Pearson Dot
- type: spearman_dot
value: 0.5421965591416092
name: Spearman Dot
- type: pearson_max
value: 0.6973190148612566
name: Pearson Max
- type: spearman_max
value: 0.6872926092972673
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.6628171358537143
name: Pearson Cosine
- type: spearman_cosine
value: 0.670314701212355
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6916567677127377
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6815748132707206
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6948756461188812
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.685329042213794
name: Spearman Euclidean
- type: pearson_dot
value: 0.5229142840207227
name: Pearson Dot
- type: spearman_dot
value: 0.5113740757424073
name: Spearman Dot
- type: pearson_max
value: 0.6948756461188812
name: Pearson Max
- type: spearman_max
value: 0.685329042213794
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.6368313837029833
name: Pearson Cosine
- type: spearman_cosine
value: 0.6512526280069127
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6832129716443456
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.674638334774044
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6843664039671002
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6760040651639672
name: Spearman Euclidean
- type: pearson_dot
value: 0.4266095536126992
name: Pearson Dot
- type: spearman_dot
value: 0.4179376458107888
name: Spearman Dot
- type: pearson_max
value: 0.6843664039671002
name: Pearson Max
- type: spearman_max
value: 0.6760040651639672
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.6147896744901056
name: Pearson Cosine
- type: spearman_cosine
value: 0.6354730852658397
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6730782159165468
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6652649799789521
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.676407799774529
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6691409653459247
name: Spearman Euclidean
- type: pearson_dot
value: 0.35130869784942953
name: Pearson Dot
- type: spearman_dot
value: 0.3445374275232203
name: Spearman Dot
- type: pearson_max
value: 0.676407799774529
name: Pearson Max
- type: spearman_max
value: 0.6691409653459247
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.5789158725954748
name: Pearson Cosine
- type: spearman_cosine
value: 0.6081197115891086
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6578631744829946
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6518503436513217
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6629734628760299
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6570510967281272
name: Spearman Euclidean
- type: pearson_dot
value: 0.24034366392620327
name: Pearson Dot
- type: spearman_dot
value: 0.2331392769925126
name: Spearman Dot
- type: pearson_max
value: 0.6629734628760299
name: Pearson Max
- type: spearman_max
value: 0.6570510967281272
name: Spearman Max
---
# SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. 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:** [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) <!-- at revision e88732e5620f3592bf6566604be9a6a5cad814ec -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- Omartificial-Intelligence-Space/arabic-n_li-triplet
<!-- - **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: 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})
)
```
## 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("Omartificial-Intelligence-Space/mpnet-base-all-nli-triplet-Arabic-mpnet_base")
# Run inference
sentences = [
'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
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.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6699 |
| **spearman_cosine** | **0.6757** |
| pearson_manhattan | 0.6943 |
| spearman_manhattan | 0.684 |
| pearson_euclidean | 0.6973 |
| spearman_euclidean | 0.6873 |
| pearson_dot | 0.5534 |
| spearman_dot | 0.5422 |
| pearson_max | 0.6973 |
| spearman_max | 0.6873 |
#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6628 |
| **spearman_cosine** | **0.6703** |
| pearson_manhattan | 0.6917 |
| spearman_manhattan | 0.6816 |
| pearson_euclidean | 0.6949 |
| spearman_euclidean | 0.6853 |
| pearson_dot | 0.5229 |
| spearman_dot | 0.5114 |
| pearson_max | 0.6949 |
| spearman_max | 0.6853 |
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6368 |
| **spearman_cosine** | **0.6513** |
| pearson_manhattan | 0.6832 |
| spearman_manhattan | 0.6746 |
| pearson_euclidean | 0.6844 |
| spearman_euclidean | 0.676 |
| pearson_dot | 0.4266 |
| spearman_dot | 0.4179 |
| pearson_max | 0.6844 |
| spearman_max | 0.676 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.6148 |
| **spearman_cosine** | **0.6355** |
| pearson_manhattan | 0.6731 |
| spearman_manhattan | 0.6653 |
| pearson_euclidean | 0.6764 |
| spearman_euclidean | 0.6691 |
| pearson_dot | 0.3513 |
| spearman_dot | 0.3445 |
| pearson_max | 0.6764 |
| spearman_max | 0.6691 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5789 |
| **spearman_cosine** | **0.6081** |
| pearson_manhattan | 0.6579 |
| spearman_manhattan | 0.6519 |
| pearson_euclidean | 0.663 |
| spearman_euclidean | 0.6571 |
| pearson_dot | 0.2403 |
| spearman_dot | 0.2331 |
| pearson_max | 0.663 |
| spearman_max | 0.6571 |
<!--
## 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.*
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## Training Details
### Training Dataset
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 557,850 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: 12 tokens</li><li>mean: 23.93 tokens</li><li>max: 155 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 29.62 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.95 tokens</li><li>max: 149 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------|:--------------------------------------------|:------------------------------------|
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> |
| <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> | <code>الاطفال يتجهمون</code> |
| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> | <code>الصبي يتزلج على الرصيف</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 6,584 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: 49.5 tokens</li><li>max: 246 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 23.66 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 25.33 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------|
| <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>إمرأتان يحملان حزمة</code> | <code>الرجال يتشاجرون خارج مطعم</code> |
| <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> | <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> | <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> |
| <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code> | <code>رجل يبيع الدونات لعميل</code> | <code>امرأة تشرب قهوتها في مقهى صغير</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: 3
- `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
- `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}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, '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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 200 | 21.5318 | - | - | - | - | - |
| 0.0459 | 400 | 17.2344 | - | - | - | - | - |
| 0.0688 | 600 | 15.393 | - | - | - | - | - |
| 0.0918 | 800 | 13.7897 | - | - | - | - | - |
| 0.1147 | 1000 | 13.534 | - | - | - | - | - |
| 0.1377 | 1200 | 12.2683 | - | - | - | - | - |
| 0.1606 | 1400 | 10.9271 | - | - | - | - | - |
| 0.1835 | 1600 | 11.071 | - | - | - | - | - |
| 0.2065 | 1800 | 10.0153 | - | - | - | - | - |
| 0.2294 | 2000 | 9.8463 | - | - | - | - | - |
| 0.2524 | 2200 | 10.0194 | - | - | - | - | - |
| 0.2753 | 2400 | 9.8371 | - | - | - | - | - |
| 0.2983 | 2600 | 9.6315 | - | - | - | - | - |
| 0.3212 | 2800 | 8.9858 | - | - | - | - | - |
| 0.3442 | 3000 | 9.1876 | - | - | - | - | - |
| 0.3671 | 3200 | 8.8028 | - | - | - | - | - |
| 0.3900 | 3400 | 8.6075 | - | - | - | - | - |
| 0.4130 | 3600 | 8.4285 | - | - | - | - | - |
| 0.4359 | 3800 | 8.1258 | - | - | - | - | - |
| 0.4589 | 4000 | 8.2508 | - | - | - | - | - |
| 0.4818 | 4200 | 7.8037 | - | - | - | - | - |
| 0.5048 | 4400 | 7.7133 | - | - | - | - | - |
| 0.5277 | 4600 | 7.5006 | - | - | - | - | - |
| 0.5506 | 4800 | 7.7025 | - | - | - | - | - |
| 0.5736 | 5000 | 7.7593 | - | - | - | - | - |
| 0.5965 | 5200 | 7.6305 | - | - | - | - | - |
| 0.6195 | 5400 | 7.7502 | - | - | - | - | - |
| 0.6424 | 5600 | 7.5624 | - | - | - | - | - |
| 0.6654 | 5800 | 7.5287 | - | - | - | - | - |
| 0.6883 | 6000 | 7.4261 | - | - | - | - | - |
| 0.7113 | 6200 | 7.239 | - | - | - | - | - |
| 0.7342 | 6400 | 7.1631 | - | - | - | - | - |
| 0.7571 | 6600 | 7.6865 | - | - | - | - | - |
| 0.7801 | 6800 | 7.6124 | - | - | - | - | - |
| 0.8030 | 7000 | 6.9936 | - | - | - | - | - |
| 0.8260 | 7200 | 6.7331 | - | - | - | - | - |
| 0.8489 | 7400 | 6.4542 | - | - | - | - | - |
| 0.8719 | 7600 | 6.1994 | - | - | - | - | - |
| 0.8948 | 7800 | 5.9798 | - | - | - | - | - |
| 0.9177 | 8000 | 5.7808 | - | - | - | - | - |
| 0.9407 | 8200 | 5.6952 | - | - | - | - | - |
| 0.9636 | 8400 | 5.5082 | - | - | - | - | - |
| 0.9866 | 8600 | 5.4421 | - | - | - | - | - |
| 1.0095 | 8800 | 3.0309 | - | - | - | - | - |
| 1.0026 | 9000 | 1.1835 | - | - | - | - | - |
| 1.0256 | 9200 | 8.1196 | - | - | - | - | - |
| 1.0485 | 9400 | 8.0326 | - | - | - | - | - |
| 1.0715 | 9600 | 8.5028 | - | - | - | - | - |
| 1.0944 | 9800 | 7.6923 | - | - | - | - | - |
| 1.1174 | 10000 | 8.029 | - | - | - | - | - |
| 1.1403 | 10200 | 7.5052 | - | - | - | - | - |
| 1.1632 | 10400 | 7.1177 | - | - | - | - | - |
| 1.1862 | 10600 | 6.9594 | - | - | - | - | - |
| 1.2091 | 10800 | 6.6662 | - | - | - | - | - |
| 1.2321 | 11000 | 6.6903 | - | - | - | - | - |
| 1.2550 | 11200 | 6.9523 | - | - | - | - | - |
| 1.2780 | 11400 | 6.676 | - | - | - | - | - |
| 1.3009 | 11600 | 6.7141 | - | - | - | - | - |
| 1.3238 | 11800 | 6.568 | - | - | - | - | - |
| 1.3468 | 12000 | 6.8938 | - | - | - | - | - |
| 1.3697 | 12200 | 6.3745 | - | - | - | - | - |
| 1.3927 | 12400 | 6.2513 | - | - | - | - | - |
| 1.4156 | 12600 | 6.2589 | - | - | - | - | - |
| 1.4386 | 12800 | 6.1388 | - | - | - | - | - |
| 1.4615 | 13000 | 6.1835 | - | - | - | - | - |
| 1.4845 | 13200 | 5.9004 | - | - | - | - | - |
| 1.5074 | 13400 | 5.7891 | - | - | - | - | - |
| 1.5303 | 13600 | 5.6184 | - | - | - | - | - |
| 1.5533 | 13800 | 5.9762 | - | - | - | - | - |
| 1.5762 | 14000 | 5.9737 | - | - | - | - | - |
| 1.5992 | 14200 | 5.8563 | - | - | - | - | - |
| 1.6221 | 14400 | 5.8904 | - | - | - | - | - |
| 1.6451 | 14600 | 5.8484 | - | - | - | - | - |
| 1.6680 | 14800 | 5.8906 | - | - | - | - | - |
| 1.6909 | 15000 | 5.7613 | - | - | - | - | - |
| 1.7139 | 15200 | 5.5744 | - | - | - | - | - |
| 1.7368 | 15400 | 5.6569 | - | - | - | - | - |
| 1.7598 | 15600 | 5.7439 | - | - | - | - | - |
| 1.7827 | 15800 | 5.5593 | - | - | - | - | - |
| 1.8057 | 16000 | 5.2935 | - | - | - | - | - |
| 1.8286 | 16200 | 5.088 | - | - | - | - | - |
| 1.8516 | 16400 | 5.0167 | - | - | - | - | - |
| 1.8745 | 16600 | 4.84 | - | - | - | - | - |
| 1.8974 | 16800 | 4.6731 | - | - | - | - | - |
| 1.9204 | 17000 | 4.6404 | - | - | - | - | - |
| 1.9433 | 17200 | 4.6413 | - | - | - | - | - |
| 1.9663 | 17400 | 4.4495 | - | - | - | - | - |
| 1.9892 | 17600 | 4.4262 | - | - | - | - | - |
| 2.0122 | 17800 | 2.01 | - | - | - | - | - |
| 2.0053 | 18000 | 1.8418 | - | - | - | - | - |
| 2.0282 | 18200 | 6.2714 | - | - | - | - | - |
| 2.0512 | 18400 | 6.1742 | - | - | - | - | - |
| 2.0741 | 18600 | 6.5996 | - | - | - | - | - |
| 2.0971 | 18800 | 6.0907 | - | - | - | - | - |
| 2.1200 | 19000 | 6.2418 | - | - | - | - | - |
| 2.1429 | 19200 | 5.7817 | - | - | - | - | - |
| 2.1659 | 19400 | 5.7073 | - | - | - | - | - |
| 2.1888 | 19600 | 5.2645 | - | - | - | - | - |
| 2.2118 | 19800 | 5.3451 | - | - | - | - | - |
| 2.2347 | 20000 | 5.2453 | - | - | - | - | - |
| 2.2577 | 20200 | 5.6161 | - | - | - | - | - |
| 2.2806 | 20400 | 5.2289 | - | - | - | - | - |
| 2.3035 | 20600 | 5.3888 | - | - | - | - | - |
| 2.3265 | 20800 | 5.2483 | - | - | - | - | - |
| 2.3494 | 21000 | 5.5791 | - | - | - | - | - |
| 2.3724 | 21200 | 5.1643 | - | - | - | - | - |
| 2.3953 | 21400 | 5.1231 | - | - | - | - | - |
| 2.4183 | 21600 | 5.1055 | - | - | - | - | - |
| 2.4412 | 21800 | 5.1778 | - | - | - | - | - |
| 2.4642 | 22000 | 5.0466 | - | - | - | - | - |
| 2.4871 | 22200 | 4.8321 | - | - | - | - | - |
| 2.5100 | 22400 | 4.7056 | - | - | - | - | - |
| 2.5330 | 22600 | 4.6858 | - | - | - | - | - |
| 2.5559 | 22800 | 4.9189 | - | - | - | - | - |
| 2.5789 | 23000 | 4.912 | - | - | - | - | - |
| 2.6018 | 23200 | 4.8289 | - | - | - | - | - |
| 2.6248 | 23400 | 4.8959 | - | - | - | - | - |
| 2.6477 | 23600 | 4.9441 | - | - | - | - | - |
| 2.6706 | 23800 | 4.9334 | - | - | - | - | - |
| 2.6936 | 24000 | 4.8328 | - | - | - | - | - |
| 2.7165 | 24200 | 4.601 | - | - | - | - | - |
| 2.7395 | 24400 | 4.834 | - | - | - | - | - |
| 2.7624 | 24600 | 5.152 | - | - | - | - | - |
| 2.7854 | 24800 | 4.9232 | - | - | - | - | - |
| 2.8083 | 25000 | 4.6556 | - | - | - | - | - |
| 2.8312 | 25200 | 4.6229 | - | - | - | - | - |
| 2.8542 | 25400 | 4.5768 | - | - | - | - | - |
| 2.8771 | 25600 | 4.3619 | - | - | - | - | - |
| 2.9001 | 25800 | 4.3608 | - | - | - | - | - |
| 2.9230 | 26000 | 4.2834 | - | - | - | - | - |
| 2.9403 | 26151 | - | 0.6355 | 0.6513 | 0.6703 | 0.6081 | 0.6757 |
</details>
### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.40.0
- PyTorch: 2.2.2+cu121
- Accelerate: 0.26.1
- Datasets: 2.19.0
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### 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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