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Model: Omartificial-Intelligence-Space/Arabic-base-all-nli-stsb-quora
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{
"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
}

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---
language:
- ar
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2772052
- loss:MultipleNegativesRankingLoss
- loss:SoftmaxLoss
- loss:CoSENTLoss
base_model: google-bert/bert-base-multilingual-cased
datasets:
- Omartificial-Intelligence-Space/Arabic-stsb
- Omartificial-Intelligence-Space/Arabic-Quora-Duplicates
widget:
- source_sentence: امرأة تكتب شيئاً
sentences:
- قد يكون من الممكن أن يوجد نظام شمسي مثل نظامنا خارج المجرة
- امرأة تقطع البصل الأخضر.
- مراهق يتحدث إلى فتاة عبر كاميرا الإنترنت
- source_sentence: لاعب التزلج على الجليد يقفز فوق برميل
sentences:
- الرجل كان يمشي
- رجل عجوز يجلس في غرفة الانتظار بالمستشفى.
- متزلج على الجليد يقفز
- source_sentence: العديد من النساء يرتدين ملابس الشرق الأوسط من الذهب والأزرق والأصفر
والأحمر ويؤدون رقصة.
sentences:
- الناس توقفوا على جانب الطريق
- هناك على الأقل إمرأتين
- المرأة وحدها نائمة في قاربها على القمر
- 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.
- الرجل يجلس بجانب لوحة لنفسه
- رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: ما هي الدروس التي يمكن أن نتعلمها من أدولف هتلر؟
sentences:
- ما هي الدروس التي يمكن أن نتعلمها من أدولف هتلر؟
- ما مدى قربنا من الحرب العالمية؟
- هل حرق وقود الطائرات يذوب أعمدة الصلب؟
pipeline_tag: sentence-similarity
---
# SentenceTransformer based on google-bert/bert-base-multilingual-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the all-nli-pair, all-nli-pair-class, all-nli-pair-score, all-nli-triplet, [stsb](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb) and [quora](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-quora-duplicates) datasets. 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:** [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) <!-- at revision 3f076fdb1ab68d5b2880cb87a0886f315b8146f8 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- all-nli-pair
- all-nli-pair-class
- all-nli-pair-score
- all-nli-triplet
- [stsb](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb)
- [quora](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-quora-duplicates)
- **Language:** ar
<!-- - **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/Arabic-base-all-nli-stsb-quora")
# Run inference
sentences = [
'ما هي الدروس التي يمكن أن نتعلمها من أدولف هتلر؟',
'ما هي الدروس التي يمكن أن نتعلمها من أدولف هتلر؟',
'ما مدى قربنا من الحرب العالمية؟',
]
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 Datasets
#### all-nli-pair
* Dataset: all-nli-pair
* Size: 314,315 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 24.43 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.73 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------------------------|:--------------------------------------------|
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> |
| <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> |
| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### all-nli-pair-class
* Dataset: all-nli-pair-class
* Size: 942,069 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 8 tokens</li><li>mean: 24.78 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.55 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:-----------------------------------------------|:--------------------------------------------|:---------------|
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص يقوم بتدريب حصانه للمنافسة</code> | <code>1</code> |
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في مطعم، يطلب عجة.</code> | <code>2</code> |
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
#### all-nli-pair-score
* Dataset: all-nli-pair-score
* Size: 942,069 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: 8 tokens</li><li>mean: 24.78 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.55 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------|:--------------------------------------------|:-----------------|
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص يقوم بتدريب حصانه للمنافسة</code> | <code>0.5</code> |
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في مطعم، يطلب عجة.</code> | <code>0.0</code> |
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
#### all-nli-triplet
* Dataset: all-nli-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: 6 tokens</li><li>mean: 12.54 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.06 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.13 tokens</li><li>max: 70 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### stsb
* Dataset: [stsb](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb) at [7c6c4bd](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb/tree/7c6c4bd31a465a0f3ed1a3704a31f2682a0f65be)
* Size: 5,749 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: 11.68 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.44 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------|:--------------------------------------------------------|:------------------|
| <code>طائرة ستقلع</code> | <code>طائرة جوية ستقلع</code> | <code>1.0</code> |
| <code>رجل يعزف على ناي كبير</code> | <code>رجل يعزف على الناي.</code> | <code>0.76</code> |
| <code>رجل ينشر الجبن الممزق على البيتزا</code> | <code>رجل ينشر الجبن الممزق على بيتزا غير مطبوخة</code> | <code>0.76</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
#### quora
* Dataset: [quora](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-quora-duplicates) at [7d49308](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-quora-duplicates/tree/7d49308a21bbad3a2762d11f2e8c0cbcc86510fe)
* Size: 10,000 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 19.69 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 20.15 tokens</li><li>max: 73 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------|:------------------------------------------------------------------------------------------|
| <code>علم التنجيم: أنا برج الجدي الشمس القمر والقبعة الشمسية...</code> | <code>أنا برج الجدي الثلاثي (الشمس والقمر والصعود في برج الجدي) ماذا يقول هذا عني؟</code> |
| <code>كيف أكون جيولوجياً جيداً؟</code> | <code>ماذا علي أن أفعل لأكون جيولوجياً عظيماً؟</code> |
| <code>كيف أقرأ وأجد تعليقاتي على يوتيوب؟</code> | <code>كيف يمكنني رؤية كل تعليقاتي على اليوتيوب؟</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Datasets
#### all-nli-triplet
* Dataset: all-nli-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: 5 tokens</li><li>mean: 25.81 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.09 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.35 tokens</li><li>max: 42 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>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### stsb
* Dataset: [stsb](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb) at [7c6c4bd](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-stsb/tree/7c6c4bd31a465a0f3ed1a3704a31f2682a0f65be)
* Size: 1,500 evaluation 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: 20.19 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.09 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------|:---------------------------------------|:------------------|
| <code>رجل يرتدي قبعة صلبة يرقص</code> | <code>رجل يرتدي قبعة صلبة يرقص.</code> | <code>1.0</code> |
| <code>طفل صغير يركب حصاناً.</code> | <code>طفل يركب حصاناً.</code> | <code>0.95</code> |
| <code>رجل يطعم فأراً لأفعى</code> | <code>الرجل يطعم الفأر للثعبان.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
#### quora
* Dataset: [quora](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-quora-duplicates) at [7d49308](https://huggingface.co/datasets/Omartificial-Intelligence-Space/arabic-quora-duplicates/tree/7d49308a21bbad3a2762d11f2e8c0cbcc86510fe)
* Size: 1,000 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 19.66 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.17 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------|:---------------------------------------------------------------------------|
| <code>ما هو قرارك في السنة الجديدة؟</code> | <code>ما الذي يمكن أن يكون قراري للعام الجديد لعام 2017؟</code> |
| <code>هل يجب أن أشتري هاتف آيفون 6 أو سامسونج غالاكسي إس 7؟</code> | <code>أيهما أفضل: الـ iPhone 6S Plus أو الـ Samsung Galaxy S7 Edge؟</code> |
| <code>ما هي الاختلافات بين التجاوز والتراجع؟</code> | <code>ما الفرق بين التجاوز والتراجع؟</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `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
- `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`: 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, '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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0231 | 500 | 5.0061 |
| 0.0462 | 1000 | 4.7876 |
| 0.0693 | 1500 | 4.6618 |
| 0.0923 | 2000 | 4.7337 |
| 0.1154 | 2500 | 4.5945 |
| 0.1385 | 3000 | 4.7536 |
| 0.1616 | 3500 | 4.619 |
| 0.1847 | 4000 | 4.4761 |
| 0.2078 | 4500 | 4.4454 |
| 0.2309 | 5000 | 4.6376 |
| 0.2539 | 5500 | 4.5513 |
| 0.2770 | 6000 | 4.5619 |
| 0.3001 | 6500 | 4.3416 |
| 0.3232 | 7000 | 4.7372 |
| 0.3463 | 7500 | 4.5906 |
| 0.3694 | 8000 | 4.6546 |
| 0.3924 | 8500 | 4.2452 |
| 0.4155 | 9000 | 4.684 |
| 0.4386 | 9500 | 4.426 |
| 0.4617 | 10000 | 4.2539 |
| 0.4848 | 10500 | 4.3224 |
| 0.5079 | 11000 | 4.4046 |
| 0.5310 | 11500 | 4.4644 |
| 0.5540 | 12000 | 4.4542 |
| 0.5771 | 12500 | 4.6026 |
| 0.6002 | 13000 | 4.3519 |
| 0.6233 | 13500 | 4.5135 |
| 0.6464 | 14000 | 4.3318 |
| 0.6695 | 14500 | 4.4465 |
| 0.6926 | 15000 | 3.9692 |
| 0.7156 | 15500 | 4.2084 |
| 0.7387 | 16000 | 4.2217 |
| 0.7618 | 16500 | 4.2791 |
| 0.7849 | 17000 | 4.5962 |
| 0.8080 | 17500 | 4.5871 |
| 0.8311 | 18000 | 4.3271 |
| 0.8541 | 18500 | 4.1688 |
| 0.8772 | 19000 | 4.2081 |
| 0.9003 | 19500 | 4.2867 |
| 0.9234 | 20000 | 4.5474 |
| 0.9465 | 20500 | 4.5257 |
| 0.9696 | 21000 | 3.8461 |
| 0.9927 | 21500 | 4.1254 |
### 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 and SoftmaxLoss
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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config.json Normal file
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{
"_name_or_path": "google-bert/bert-base-multilingual-cased",
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"pooler_type": "first_token_transform",
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"torch_dtype": "float32",
"transformers_version": "4.40.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 119547
}

10
config_sentence_transformers.json Normal file
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{
"__version__": {
"sentence_transformers": "3.0.1",
"transformers": "4.40.0",
"pytorch": "2.2.2+cu121"
},
"prompts": {},
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"similarity_fn_name": null
}

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model.safetensors Normal file
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modules.json Normal file
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[
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"type": "sentence_transformers.models.Pooling"
}
]

4
sentence_bert_config.json Normal file
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{
"max_seq_length": 512,
"do_lower_case": false
}

7
special_tokens_map.json Normal file
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{
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"sep_token": "[SEP]",
"unk_token": "[UNK]"
}

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tokenizer_config.json Normal file
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{
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