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---
language:
- ar
library_name: sentence-transformers
tags:
- mteb
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: UBC-NLP/MARBERTv2
datasets:
- Omartificial-Intelligence-Space/Arabic-NLi-Triplet
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: Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
results:
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 49.25240527202211
- type: cosine_spearman
value: 51.87708566904703
- type: euclidean_pearson
value: 49.790877425774696
- type: euclidean_spearman
value: 51.725274981021855
- type: main_score
value: 51.87708566904703
- type: manhattan_pearson
value: 52.31560776967401
- type: manhattan_spearman
value: 54.28979124658997
task:
type: STS
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 65.81089479351829
- type: cosine_spearman
value: 65.80163441928238
- type: euclidean_pearson
value: 65.2718874370746
- type: euclidean_spearman
value: 65.92429031695988
- type: main_score
value: 65.80163441928238
- type: manhattan_pearson
value: 65.28701419332383
- type: manhattan_spearman
value: 65.94229793651319
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 65.11346939995998
- type: cosine_spearman
value: 63.00297824477175
- type: euclidean_pearson
value: 63.85320097970942
- type: euclidean_spearman
value: 63.25151047701848
- type: main_score
value: 63.00297824477175
- type: manhattan_pearson
value: 64.40291990853984
- type: manhattan_spearman
value: 63.63497232399945
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 52.2735823521702
- type: cosine_spearman
value: 52.23198766098021
- type: euclidean_pearson
value: 54.12467577456837
- type: euclidean_spearman
value: 52.40014028261351
- type: main_score
value: 52.23198766098021
- type: manhattan_pearson
value: 54.38052509834607
- type: manhattan_spearman
value: 52.70836595958237
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 58.55307076840419
- type: cosine_spearman
value: 59.2261024017655
- type: euclidean_pearson
value: 59.55734715751804
- type: euclidean_spearman
value: 60.135899681574834
- type: main_score
value: 59.2261024017655
- type: manhattan_pearson
value: 59.99274396356966
- type: manhattan_spearman
value: 60.44325356503041
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 68.94418532602707
- type: cosine_spearman
value: 70.01912156519296
- type: euclidean_pearson
value: 71.67028435860581
- type: euclidean_spearman
value: 71.48252471922122
- type: main_score
value: 70.01912156519296
- type: manhattan_pearson
value: 71.9587452337792
- type: manhattan_spearman
value: 71.69160519065173
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 62.81619254162203
- type: cosine_spearman
value: 64.98814526698425
- type: euclidean_pearson
value: 66.43531796610995
- type: euclidean_spearman
value: 66.53768451143964
- type: main_score
value: 64.98814526698425
- type: manhattan_pearson
value: 66.57822125651369
- type: manhattan_spearman
value: 66.71830390508079
task:
type: STS
- dataset:
config: ar-ar
name: MTEB STS17 (ar-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 81.68055610903552
- type: cosine_spearman
value: 82.18125783448961
- type: euclidean_pearson
value: 80.5422740473486
- type: euclidean_spearman
value: 81.79456727036232
- type: main_score
value: 82.18125783448961
- type: manhattan_pearson
value: 80.43564733654793
- type: manhattan_spearman
value: 81.76103816207625
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 51.33460593849487
- type: cosine_spearman
value: 58.07741072443786
- type: euclidean_pearson
value: 54.26430308336828
- type: euclidean_spearman
value: 58.8384539429318
- type: main_score
value: 58.07741072443786
- type: manhattan_pearson
value: 54.41587176266624
- type: manhattan_spearman
value: 58.831993325957086
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 61.11956207522431
- type: cosine_spearman
value: 61.16768766134144
- type: euclidean_pearson
value: 64.44141934993837
- type: euclidean_spearman
value: 63.450379593077066
- type: main_score
value: 61.16768766134144
- type: manhattan_pearson
value: 64.43852352892529
- type: manhattan_spearman
value: 63.57630045107761
task:
type: STS
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_pearson
value: 29.583566160417668
- type: cosine_spearman
value: 29.534419950502212
- type: dot_pearson
value: 28.13970643170574
- type: dot_spearman
value: 28.907762267009073
- type: main_score
value: 29.534419950502212
- type: pearson
value: 29.583566160417668
- type: spearman
value: 29.534419950502212
task:
type: Summarization
- name: SentenceTransformer based on UBC-NLP/MARBERTv2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 768
type: sts-test-768
metrics:
- type: pearson_cosine
value: 0.611168498883907
name: Pearson Cosine
- type: spearman_cosine
value: 0.6116733587939157
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6443687886661206
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6358107360369792
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.644404066642609
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6345893921062774
name: Spearman Euclidean
- type: pearson_dot
value: 0.4723643245352202
name: Pearson Dot
- type: spearman_dot
value: 0.44844519905410135
name: Spearman Dot
- type: pearson_max
value: 0.644404066642609
name: Pearson Max
- type: spearman_max
value: 0.6358107360369792
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 512
type: sts-test-512
metrics:
- type: pearson_cosine
value: 0.6664570291720014
name: Pearson Cosine
- type: spearman_cosine
value: 0.6647687532159875
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6429976947418544
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6334753432753939
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6466249455585532
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6373181315122213
name: Spearman Euclidean
- type: pearson_dot
value: 0.5370129457359227
name: Pearson Dot
- type: spearman_dot
value: 0.5241649973373772
name: Spearman Dot
- type: pearson_max
value: 0.6664570291720014
name: Pearson Max
- type: spearman_max
value: 0.6647687532159875
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.6601248277308522
name: Pearson Cosine
- type: spearman_cosine
value: 0.6592739654246011
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6361644543165994
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6250621947417249
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6408426652431157
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6300109524350457
name: Spearman Euclidean
- type: pearson_dot
value: 0.5250513197384045
name: Pearson Dot
- type: spearman_dot
value: 0.5154779060125071
name: Spearman Dot
- type: pearson_max
value: 0.6601248277308522
name: Pearson Max
- type: spearman_max
value: 0.6592739654246011
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.6549481034721005
name: Pearson Cosine
- type: spearman_cosine
value: 0.6523201621940143
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6342700090917214
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6226791710099966
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6397224689512541
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6280973341704362
name: Spearman Euclidean
- type: pearson_dot
value: 0.47240889358810917
name: Pearson Dot
- type: spearman_dot
value: 0.4633669926372942
name: Spearman Dot
- type: pearson_max
value: 0.6549481034721005
name: Pearson Max
- type: spearman_max
value: 0.6523201621940143
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.6367217585211098
name: Pearson Cosine
- type: spearman_cosine
value: 0.6370191671711296
name: Spearman Cosine
- type: pearson_manhattan
value: 0.6263730801254332
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.6118927366012856
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.6327699647617465
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6180184829867724
name: Spearman Euclidean
- type: pearson_dot
value: 0.41169381399943167
name: Pearson Dot
- type: spearman_dot
value: 0.40444222536491986
name: Spearman Dot
- type: pearson_max
value: 0.6367217585211098
name: Pearson Max
- type: spearman_max
value: 0.6370191671711296
name: Spearman Max
license: apache-2.0
---
# SentenceTransformer based on UBC-NLP/MARBERTv2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) 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:** [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) <!-- at revision fe88db9db8ccdb0c4e1627495f405c44a5f89066 -->
- **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: BertModel
(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/Marbert-all-nli-triplet")
# 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 | 0.6112 |
| **spearman_cosine** | **0.6117** |
| pearson_manhattan | 0.6444 |
| spearman_manhattan | 0.6358 |
| pearson_euclidean | 0.6444 |
| spearman_euclidean | 0.6346 |
| pearson_dot | 0.4724 |
| spearman_dot | 0.4484 |
| pearson_max | 0.6444 |
| spearman_max | 0.6358 |
#### 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 | 0.6665 |
| **spearman_cosine** | **0.6648** |
| pearson_manhattan | 0.643 |
| spearman_manhattan | 0.6335 |
| pearson_euclidean | 0.6466 |
| spearman_euclidean | 0.6373 |
| pearson_dot | 0.537 |
| spearman_dot | 0.5242 |
| pearson_max | 0.6665 |
| spearman_max | 0.6648 |
#### 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 | 0.6601 |
| **spearman_cosine** | **0.6593** |
| pearson_manhattan | 0.6362 |
| spearman_manhattan | 0.6251 |
| pearson_euclidean | 0.6408 |
| spearman_euclidean | 0.63 |
| pearson_dot | 0.5251 |
| spearman_dot | 0.5155 |
| pearson_max | 0.6601 |
| spearman_max | 0.6593 |
#### 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 | 0.6549 |
| **spearman_cosine** | **0.6523** |
| pearson_manhattan | 0.6343 |
| spearman_manhattan | 0.6227 |
| pearson_euclidean | 0.6397 |
| spearman_euclidean | 0.6281 |
| pearson_dot | 0.4724 |
| spearman_dot | 0.4634 |
| pearson_max | 0.6549 |
| spearman_max | 0.6523 |
#### 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 | 0.6367 |
| **spearman_cosine** | **0.637** |
| pearson_manhattan | 0.6264 |
| spearman_manhattan | 0.6119 |
| pearson_euclidean | 0.6328 |
| spearman_euclidean | 0.618 |
| pearson_dot | 0.4117 |
| spearman_dot | 0.4044 |
| pearson_max | 0.6367 |
| spearman_max | 0.637 |
<!--
## 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.*
-->
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### 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: 4 tokens</li><li>mean: 7.68 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.66 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.47 tokens</li><li>max: 40 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: 14.78 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.41 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.95 tokens</li><li>max: 21 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 |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0229 | 200 | 25.0771 | - | - | - | - | - |
| 0.0459 | 400 | 9.1435 | - | - | - | - | - |
| 0.0688 | 600 | 8.0492 | - | - | - | - | - |
| 0.0918 | 800 | 7.1378 | - | - | - | - | - |
| 0.1147 | 1000 | 7.6249 | - | - | - | - | - |
| 0.1377 | 1200 | 7.3604 | - | - | - | - | - |
| 0.1606 | 1400 | 6.5783 | - | - | - | - | - |
| 0.1835 | 1600 | 6.4145 | - | - | - | - | - |
| 0.2065 | 1800 | 6.1781 | - | - | - | - | - |
| 0.2294 | 2000 | 6.2375 | - | - | - | - | - |
| 0.2524 | 2200 | 6.2587 | - | - | - | - | - |
| 0.2753 | 2400 | 6.0826 | - | - | - | - | - |
| 0.2983 | 2600 | 6.1514 | - | - | - | - | - |
| 0.3212 | 2800 | 5.6949 | - | - | - | - | - |
| 0.3442 | 3000 | 6.0062 | - | - | - | - | - |
| 0.3671 | 3200 | 5.7551 | - | - | - | - | - |
| 0.3900 | 3400 | 5.658 | - | - | - | - | - |
| 0.4130 | 3600 | 5.7135 | - | - | - | - | - |
| 0.4359 | 3800 | 5.3909 | - | - | - | - | - |
| 0.4589 | 4000 | 5.5068 | - | - | - | - | - |
| 0.4818 | 4200 | 5.2261 | - | - | - | - | - |
| 0.5048 | 4400 | 5.1674 | - | - | - | - | - |
| 0.5277 | 4600 | 5.0427 | - | - | - | - | - |
| 0.5506 | 4800 | 5.3824 | - | - | - | - | - |
| 0.5736 | 5000 | 5.3063 | - | - | - | - | - |
| 0.5965 | 5200 | 5.2174 | - | - | - | - | - |
| 0.6195 | 5400 | 5.2116 | - | - | - | - | - |
| 0.6424 | 5600 | 5.2226 | - | - | - | - | - |
| 0.6654 | 5800 | 5.2051 | - | - | - | - | - |
| 0.6883 | 6000 | 5.204 | - | - | - | - | - |
| 0.7113 | 6200 | 5.154 | - | - | - | - | - |
| 0.7342 | 6400 | 5.0236 | - | - | - | - | - |
| 0.7571 | 6600 | 4.9476 | - | - | - | - | - |
| 0.7801 | 6800 | 4.0164 | - | - | - | - | - |
| 0.8030 | 7000 | 3.5707 | - | - | - | - | - |
| 0.8260 | 7200 | 3.3586 | - | - | - | - | - |
| 0.8489 | 7400 | 3.2376 | - | - | - | - | - |
| 0.8719 | 7600 | 3.0282 | - | - | - | - | - |
| 0.8948 | 7800 | 2.901 | - | - | - | - | - |
| 0.9177 | 8000 | 2.9371 | - | - | - | - | - |
| 0.9407 | 8200 | 2.8362 | - | - | - | - | - |
| 0.9636 | 8400 | 2.8121 | - | - | - | - | - |
| 0.9866 | 8600 | 2.7105 | - | - | - | - | - |
| 1.0 | 8717 | - | 0.6523 | 0.6593 | 0.6648 | 0.6370 | 0.6117 |
### 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}
}
```
## <span style="color:blue">Acknowledgments</span>
The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models.
```markdown
## Citation
If you use the Arabic Matryoshka Embeddings Model, please cite it as follows:
```bibtex
@software{nacar2024,
author = {Omer Nacar},
title = {Arabic Matryoshka Embeddings Model - Marbert All Nli Triplet Matryoshka},
year = 2024,
url = {https://huggingface.co/Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka},
version = {1.0.0},
} |