Add new SentenceTransformer model.

1_Pooling/config.json

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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
+}

README.md

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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},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "google-bert/bert-base-multilingual-cased",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.40.0",
+    "pytorch": "2.2.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b50aacb357c86dfd2fcc749c93554d9de01774f84a1f2ef1c638f3b6a8e7403f
+size 711436136

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}