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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: sentence-transformers/LaBSE
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 sentence-transformers/LaBSE
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.7269177710249681
name: Pearson Cosine
- type: spearman_cosine
value: 0.7225258779395222
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7259261785622463
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7210463582530393
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7259567884235211
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.722525823788783
name: Spearman Euclidean
- type: pearson_dot
value: 0.7269177712136122
name: Pearson Dot
- type: spearman_dot
value: 0.7225258771129475
name: Spearman Dot
- type: pearson_max
value: 0.7269177712136122
name: Pearson Max
- type: spearman_max
value: 0.7225258779395222
name: Spearman Max
- type: pearson_cosine
value: 0.8143867576376295
name: Pearson Cosine
- type: spearman_cosine
value: 0.8205044914629483
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8203365887013151
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8203816698535976
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8201809453496319
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8205044914629483
name: Spearman Euclidean
- type: pearson_dot
value: 0.8143867541070537
name: Pearson Dot
- type: spearman_dot
value: 0.8205044914629483
name: Spearman Dot
- type: pearson_max
value: 0.8203365887013151
name: Pearson Max
- type: spearman_max
value: 0.8205044914629483
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.7268389724271859
name: Pearson Cosine
- type: spearman_cosine
value: 0.7224359411000278
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7241418669615103
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7195408311833029
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7248184919191593
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7212936866178097
name: Spearman Euclidean
- type: pearson_dot
value: 0.7252522928016701
name: Pearson Dot
- type: spearman_dot
value: 0.7205040482865328
name: Spearman Dot
- type: pearson_max
value: 0.7268389724271859
name: Pearson Max
- type: spearman_max
value: 0.7224359411000278
name: Spearman Max
- type: pearson_cosine
value: 0.8143448965624136
name: Pearson Cosine
- type: spearman_cosine
value: 0.8211700903453509
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8217448619823571
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8216016599665544
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8216413349390971
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.82188122418776
name: Spearman Euclidean
- type: pearson_dot
value: 0.8097020064483653
name: Pearson Dot
- type: spearman_dot
value: 0.8147306090545295
name: Spearman Dot
- type: pearson_max
value: 0.8217448619823571
name: Pearson Max
- type: spearman_max
value: 0.82188122418776
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.7283468617741852
name: Pearson Cosine
- type: spearman_cosine
value: 0.7264294106954872
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7227711798003426
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.718067982079232
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7251492361775083
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7215068115809131
name: Spearman Euclidean
- type: pearson_dot
value: 0.7243396991648858
name: Pearson Dot
- type: spearman_dot
value: 0.7221390873398206
name: Spearman Dot
- type: pearson_max
value: 0.7283468617741852
name: Pearson Max
- type: spearman_max
value: 0.7264294106954872
name: Spearman Max
- type: pearson_cosine
value: 0.8075613785257986
name: Pearson Cosine
- type: spearman_cosine
value: 0.8159258089804861
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8208711370091426
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8196747601014518
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8210210137439432
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8203004500356083
name: Spearman Euclidean
- type: pearson_dot
value: 0.7870611647231145
name: Pearson Dot
- type: spearman_dot
value: 0.7874848213991118
name: Spearman Dot
- type: pearson_max
value: 0.8210210137439432
name: Pearson Max
- type: spearman_max
value: 0.8203004500356083
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.7102082520621849
name: Pearson Cosine
- type: spearman_cosine
value: 0.7103917869311991
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7134729607181519
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.708895102058259
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7171545288118942
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7130380237150746
name: Spearman Euclidean
- type: pearson_dot
value: 0.6777774738547628
name: Pearson Dot
- type: spearman_dot
value: 0.6746474823963989
name: Spearman Dot
- type: pearson_max
value: 0.7171545288118942
name: Pearson Max
- type: spearman_max
value: 0.7130380237150746
name: Spearman Max
- type: pearson_cosine
value: 0.8024378358145556
name: Pearson Cosine
- type: spearman_cosine
value: 0.8117561815472325
name: Spearman Cosine
- type: pearson_manhattan
value: 0.818920309459774
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8180515365910205
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8198346073356603
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8185162896024369
name: Spearman Euclidean
- type: pearson_dot
value: 0.7513270537478935
name: Pearson Dot
- type: spearman_dot
value: 0.7427542871546953
name: Spearman Dot
- type: pearson_max
value: 0.8198346073356603
name: Pearson Max
- type: spearman_max
value: 0.8185162896024369
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.6930745722517785
name: Pearson Cosine
- type: spearman_cosine
value: 0.6982194042238953
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6971382079778946
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6942362764367931
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7012627015062325
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6986972295835788
name: Spearman Euclidean
- type: pearson_dot
value: 0.6376735798940838
name: Pearson Dot
- type: spearman_dot
value: 0.6344835722310429
name: Spearman Dot
- type: pearson_max
value: 0.7012627015062325
name: Pearson Max
- type: spearman_max
value: 0.6986972295835788
name: Spearman Max
- type: pearson_cosine
value: 0.7855080652087961
name: Pearson Cosine
- type: spearman_cosine
value: 0.7948979371698327
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8060407473462375
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8041199691999044
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8088262858195556
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8060483394849104
name: Spearman Euclidean
- type: pearson_dot
value: 0.677754045289596
name: Pearson Dot
- type: spearman_dot
value: 0.6616232873061395
name: Spearman Dot
- type: pearson_max
value: 0.8088262858195556
name: Pearson Max
- type: spearman_max
value: 0.8060483394849104
name: Spearman Max
---
# 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) 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision e34fab64a3011d2176c99545a93d5cbddc9a91b7 -->
- **Maximum Sequence Length:** 256 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': 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("Omartificial-Intelligence-Space/Arabic-labse")
# 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 | 0.7269 |
| **spearman_cosine** | **0.7225** |
| pearson_manhattan | 0.7259 |
| spearman_manhattan | 0.721 |
| pearson_euclidean | 0.726 |
| spearman_euclidean | 0.7225 |
| pearson_dot | 0.7269 |
| spearman_dot | 0.7225 |
| pearson_max | 0.7269 |
| spearman_max | 0.7225 |
#### 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 | 0.7268 |
| **spearman_cosine** | **0.7224** |
| pearson_manhattan | 0.7241 |
| spearman_manhattan | 0.7195 |
| pearson_euclidean | 0.7248 |
| spearman_euclidean | 0.7213 |
| pearson_dot | 0.7253 |
| spearman_dot | 0.7205 |
| pearson_max | 0.7268 |
| spearman_max | 0.7224 |
#### 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 | 0.7283 |
| **spearman_cosine** | **0.7264** |
| pearson_manhattan | 0.7228 |
| spearman_manhattan | 0.7181 |
| pearson_euclidean | 0.7251 |
| spearman_euclidean | 0.7215 |
| pearson_dot | 0.7243 |
| spearman_dot | 0.7221 |
| pearson_max | 0.7283 |
| spearman_max | 0.7264 |
#### 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 | 0.7102 |
| **spearman_cosine** | **0.7104** |
| pearson_manhattan | 0.7135 |
| spearman_manhattan | 0.7089 |
| pearson_euclidean | 0.7172 |
| spearman_euclidean | 0.713 |
| pearson_dot | 0.6778 |
| spearman_dot | 0.6746 |
| pearson_max | 0.7172 |
| spearman_max | 0.713 |
#### 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 | 0.6931 |
| **spearman_cosine** | **0.6982** |
| pearson_manhattan | 0.6971 |
| spearman_manhattan | 0.6942 |
| pearson_euclidean | 0.7013 |
| spearman_euclidean | 0.6987 |
| pearson_dot | 0.6377 |
| spearman_dot | 0.6345 |
| pearson_max | 0.7013 |
| spearman_max | 0.6987 |
#### 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 | 0.8144 |
| **spearman_cosine** | **0.8205** |
| pearson_manhattan | 0.8203 |
| spearman_manhattan | 0.8204 |
| pearson_euclidean | 0.8202 |
| spearman_euclidean | 0.8205 |
| pearson_dot | 0.8144 |
| spearman_dot | 0.8205 |
| pearson_max | 0.8203 |
| spearman_max | 0.8205 |
#### 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 | 0.8143 |
| **spearman_cosine** | **0.8212** |
| pearson_manhattan | 0.8217 |
| spearman_manhattan | 0.8216 |
| pearson_euclidean | 0.8216 |
| spearman_euclidean | 0.8219 |
| pearson_dot | 0.8097 |
| spearman_dot | 0.8147 |
| pearson_max | 0.8217 |
| spearman_max | 0.8219 |
#### 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 | 0.8076 |
| **spearman_cosine** | **0.8159** |
| pearson_manhattan | 0.8209 |
| spearman_manhattan | 0.8197 |
| pearson_euclidean | 0.821 |
| spearman_euclidean | 0.8203 |
| pearson_dot | 0.7871 |
| spearman_dot | 0.7875 |
| pearson_max | 0.821 |
| spearman_max | 0.8203 |
#### 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 | 0.8024 |
| **spearman_cosine** | **0.8118** |
| pearson_manhattan | 0.8189 |
| spearman_manhattan | 0.8181 |
| pearson_euclidean | 0.8198 |
| spearman_euclidean | 0.8185 |
| pearson_dot | 0.7513 |
| spearman_dot | 0.7428 |
| pearson_max | 0.8198 |
| spearman_max | 0.8185 |
#### 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 | 0.7855 |
| **spearman_cosine** | **0.7949** |
| pearson_manhattan | 0.806 |
| spearman_manhattan | 0.8041 |
| pearson_euclidean | 0.8088 |
| spearman_euclidean | 0.806 |
| pearson_dot | 0.6778 |
| spearman_dot | 0.6616 |
| pearson_max | 0.8088 |
| spearman_max | 0.806 |
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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: 4 tokens</li><li>mean: 9.99 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.44 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.82 tokens</li><li>max: 49 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: 4 tokens</li><li>mean: 19.71 tokens</li><li>max: 100 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.37 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.49 tokens</li><li>max: 34 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
- `num_train_epochs`: 1
- `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`: 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
- `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
| 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 |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| None | 0 | - | 0.7104 | 0.7264 | 0.7224 | 0.6982 | 0.7225 |
| 0.0229 | 200 | 13.1738 | - | - | - | - | - |
| 0.0459 | 400 | 8.8127 | - | - | - | - | - |
| 0.0688 | 600 | 8.0984 | - | - | - | - | - |
| 0.0918 | 800 | 7.2984 | - | - | - | - | - |
| 0.1147 | 1000 | 7.5749 | - | - | - | - | - |
| 0.1377 | 1200 | 7.1292 | - | - | - | - | - |
| 0.1606 | 1400 | 6.6146 | - | - | - | - | - |
| 0.1835 | 1600 | 6.6523 | - | - | - | - | - |
| 0.2065 | 1800 | 6.1095 | - | - | - | - | - |
| 0.2294 | 2000 | 6.0841 | - | - | - | - | - |
| 0.2524 | 2200 | 6.3024 | - | - | - | - | - |
| 0.2753 | 2400 | 6.1941 | - | - | - | - | - |
| 0.2983 | 2600 | 6.1686 | - | - | - | - | - |
| 0.3212 | 2800 | 5.8317 | - | - | - | - | - |
| 0.3442 | 3000 | 6.0597 | - | - | - | - | - |
| 0.3671 | 3200 | 5.7832 | - | - | - | - | - |
| 0.3900 | 3400 | 5.7088 | - | - | - | - | - |
| 0.4130 | 3600 | 5.6988 | - | - | - | - | - |
| 0.4359 | 3800 | 5.5268 | - | - | - | - | - |
| 0.4589 | 4000 | 5.5543 | - | - | - | - | - |
| 0.4818 | 4200 | 5.3152 | - | - | - | - | - |
| 0.5048 | 4400 | 5.2894 | - | - | - | - | - |
| 0.5277 | 4600 | 5.1805 | - | - | - | - | - |
| 0.5506 | 4800 | 5.4559 | - | - | - | - | - |
| 0.5736 | 5000 | 5.3836 | - | - | - | - | - |
| 0.5965 | 5200 | 5.2626 | - | - | - | - | - |
| 0.6195 | 5400 | 5.2511 | - | - | - | - | - |
| 0.6424 | 5600 | 5.3308 | - | - | - | - | - |
| 0.6654 | 5800 | 5.2264 | - | - | - | - | - |
| 0.6883 | 6000 | 5.2881 | - | - | - | - | - |
| 0.7113 | 6200 | 5.1349 | - | - | - | - | - |
| 0.7342 | 6400 | 5.0872 | - | - | - | - | - |
| 0.7571 | 6600 | 4.5515 | - | - | - | - | - |
| 0.7801 | 6800 | 3.4312 | - | - | - | - | - |
| 0.8030 | 7000 | 3.1008 | - | - | - | - | - |
| 0.8260 | 7200 | 2.9582 | - | - | - | - | - |
| 0.8489 | 7400 | 2.8153 | - | - | - | - | - |
| 0.8719 | 7600 | 2.7214 | - | - | - | - | - |
| 0.8948 | 7800 | 2.5392 | - | - | - | - | - |
| 0.9177 | 8000 | 2.584 | - | - | - | - | - |
| 0.9407 | 8200 | 2.5384 | - | - | - | - | - |
| 0.9636 | 8400 | 2.4937 | - | - | - | - | - |
| 0.9866 | 8600 | 2.4155 | - | - | - | - | - |
| 1.0 | 8717 | - | 0.8118 | 0.8159 | 0.8212 | 0.7949 | 0.8205 |
### 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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