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--- |
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language: |
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- ar |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:557850 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة |
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sentences: |
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- رجل يقدم عرضاً |
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- هناك رجل بالخارج قرب الشاطئ |
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- رجل يجلس على أريكه |
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- source_sentence: رجل يقفز إلى سريره القذر |
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sentences: |
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- السرير قذر. |
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- رجل يضحك أثناء غسيل الملابس |
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- الرجل على القمر |
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- source_sentence: الفتيات بالخارج |
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sentences: |
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- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات |
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- فتيان يركبان في جولة متعة |
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- >- |
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ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة |
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تتحدث إليهن |
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- source_sentence: الرجل يرتدي قميصاً أزرق. |
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sentences: |
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- >- |
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رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة |
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حمراء مع الماء في الخلفية. |
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- كتاب القصص مفتوح |
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- رجل يرتدي قميص أسود يعزف على الجيتار. |
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- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة. |
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sentences: |
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- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه |
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- رجل يستلقي على وجهه على مقعد في الحديقة. |
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- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: >- |
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SentenceTransformer based on |
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sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test 256 |
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type: sts-test-256 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8264447022356382 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8386403752382455 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.8219134931449013 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.825509659109493 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.8223094468630248 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.8260503151751462 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.6375226884845725 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.6287228614640888 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.8264447022356382 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.8386403752382455 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test 128 |
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type: sts-test-128 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8209661910768973 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8347149482673766 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.8082811559854036 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.8148314269262763 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.8093138512113149 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.8156468458613929 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.5795109620454884 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.5760223026552876 |
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name: Spearman Dot |
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- type: pearson_max |
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value: 0.8209661910768973 |
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name: Pearson Max |
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- type: spearman_max |
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value: 0.8347149482673766 |
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name: Spearman Max |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test 64 |
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type: sts-test-64 |
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metrics: |
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- type: pearson_cosine |
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value: 0.808708530451336 |
|
name: Pearson Cosine |
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- type: spearman_cosine |
|
value: 0.8217532539767914 |
|
name: Spearman Cosine |
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- type: pearson_manhattan |
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value: 0.7876121380998453 |
|
name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: 0.7969092304137347 |
|
name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: 0.7902997966909958 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: 0.7987635968785215 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: 0.495047136234386 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: 0.49287000679901516 |
|
name: Spearman Dot |
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- type: pearson_max |
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value: 0.808708530451336 |
|
name: Pearson Max |
|
- type: spearman_max |
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value: 0.8217532539767914 |
|
name: Spearman Max |
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--- |
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|
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# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- Omartificial-Intelligence-Space/arabic-n_li-triplet |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 384, '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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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Omartificial-Intelligence-Space/MiniLM-L12-v2-all-nli-triplet") |
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# Run inference |
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sentences = [ |
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'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.', |
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'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه', |
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'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Semantic Similarity |
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* Dataset: `sts-test-256` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.8264 | |
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| **spearman_cosine** | **0.8386** | |
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| pearson_manhattan | 0.8219 | |
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| spearman_manhattan | 0.8255 | |
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| pearson_euclidean | 0.8223 | |
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| spearman_euclidean | 0.8261 | |
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| pearson_dot | 0.6375 | |
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| spearman_dot | 0.6287 | |
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| pearson_max | 0.8264 | |
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| spearman_max | 0.8386 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-test-128` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.821 | |
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| **spearman_cosine** | **0.8347** | |
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| pearson_manhattan | 0.8083 | |
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| spearman_manhattan | 0.8148 | |
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| pearson_euclidean | 0.8093 | |
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| spearman_euclidean | 0.8156 | |
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| pearson_dot | 0.5795 | |
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| spearman_dot | 0.576 | |
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| pearson_max | 0.821 | |
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| spearman_max | 0.8347 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-test-64` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | 0.8087 | |
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| **spearman_cosine** | **0.8218** | |
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| pearson_manhattan | 0.7876 | |
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| spearman_manhattan | 0.7969 | |
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| pearson_euclidean | 0.7903 | |
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| spearman_euclidean | 0.7988 | |
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| pearson_dot | 0.495 | |
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| spearman_dot | 0.4929 | |
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| pearson_max | 0.8087 | |
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| spearman_max | 0.8218 | |
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|
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<!-- |
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## Bias, Risks and Limitations |
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*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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
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### Training Dataset |
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#### Omartificial-Intelligence-Space/arabic-n_li-triplet |
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* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet |
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* Size: 557,850 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 10.33 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.21 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.32 tokens</li><li>max: 53 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:------------------------------------------------------------|:--------------------------------------------|:------------------------------------| |
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| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> | |
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| <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> | <code>الاطفال يتجهمون</code> | |
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| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> | <code>الصبي يتزلج على الرصيف</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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|
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### Evaluation Dataset |
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|
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#### Omartificial-Intelligence-Space/arabic-n_li-triplet |
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|
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* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet |
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* Size: 6,584 evaluation samples |
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* 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: 21.86 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.22 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.2 tokens</li><li>max: 33 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------| |
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| <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>إمرأتان يحملان حزمة</code> | <code>الرجال يتشاجرون خارج مطعم</code> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
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- `label_names`: None |
|
- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `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-64_spearman_cosine | |
|
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:---------------------------:| |
|
| 0.0229 | 200 | 6.2204 | - | - | - | |
|
| 0.0459 | 400 | 4.9559 | - | - | - | |
|
| 0.0688 | 600 | 4.7835 | - | - | - | |
|
| 0.0918 | 800 | 4.2725 | - | - | - | |
|
| 0.1147 | 1000 | 4.291 | - | - | - | |
|
| 0.1377 | 1200 | 4.0704 | - | - | - | |
|
| 0.1606 | 1400 | 3.7962 | - | - | - | |
|
| 0.1835 | 1600 | 3.7447 | - | - | - | |
|
| 0.2065 | 1800 | 3.569 | - | - | - | |
|
| 0.2294 | 2000 | 3.5373 | - | - | - | |
|
| 0.2524 | 2200 | 3.608 | - | - | - | |
|
| 0.2753 | 2400 | 3.5609 | - | - | - | |
|
| 0.2983 | 2600 | 3.5231 | - | - | - | |
|
| 0.3212 | 2800 | 3.3312 | - | - | - | |
|
| 0.3442 | 3000 | 3.4803 | - | - | - | |
|
| 0.3671 | 3200 | 3.3552 | - | - | - | |
|
| 0.3900 | 3400 | 3.3024 | - | - | - | |
|
| 0.4130 | 3600 | 3.2559 | - | - | - | |
|
| 0.4359 | 3800 | 3.1882 | - | - | - | |
|
| 0.4589 | 4000 | 3.227 | - | - | - | |
|
| 0.4818 | 4200 | 3.0889 | - | - | - | |
|
| 0.5048 | 4400 | 3.0861 | - | - | - | |
|
| 0.5277 | 4600 | 3.0178 | - | - | - | |
|
| 0.5506 | 4800 | 3.231 | - | - | - | |
|
| 0.5736 | 5000 | 3.1593 | - | - | - | |
|
| 0.5965 | 5200 | 3.1101 | - | - | - | |
|
| 0.6195 | 5400 | 3.1307 | - | - | - | |
|
| 0.6424 | 5600 | 3.1265 | - | - | - | |
|
| 0.6654 | 5800 | 3.1116 | - | - | - | |
|
| 0.6883 | 6000 | 3.1417 | - | - | - | |
|
| 0.7113 | 6200 | 3.0862 | - | - | - | |
|
| 0.7342 | 6400 | 2.9652 | - | - | - | |
|
| 0.7571 | 6600 | 2.8466 | - | - | - | |
|
| 0.7801 | 6800 | 2.271 | - | - | - | |
|
| 0.8030 | 7000 | 2.046 | - | - | - | |
|
| 0.8260 | 7200 | 1.9634 | - | - | - | |
|
| 0.8489 | 7400 | 1.8875 | - | - | - | |
|
| 0.8719 | 7600 | 1.7655 | - | - | - | |
|
| 0.8948 | 7800 | 1.6874 | - | - | - | |
|
| 0.9177 | 8000 | 1.7315 | - | - | - | |
|
| 0.9407 | 8200 | 1.6674 | - | - | - | |
|
| 0.9636 | 8400 | 1.6574 | - | - | - | |
|
| 0.9866 | 8600 | 1.6142 | - | - | - | |
|
| 1.0 | 8717 | - | 0.8347 | 0.8386 | 0.8218 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.9.18 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.40.0 |
|
- PyTorch: 2.2.2+cu121 |
|
- Accelerate: 0.26.1 |
|
- Datasets: 2.19.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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