--- inference: false language: - ar library_name: sentence-transformers tags: - mteb - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/LaBSE datasets: - Omartificial-Intelligence-Space/Arabic-NLi-Triplet metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة sentences: - رجل يقدم عرضاً - هناك رجل بالخارج قرب الشاطئ - رجل يجلس على أريكه - source_sentence: رجل يقفز إلى سريره القذر sentences: - السرير قذر. - رجل يضحك أثناء غسيل الملابس - الرجل على القمر - source_sentence: الفتيات بالخارج sentences: - امرأة تلف الخيط إلى كرات بجانب كومة من الكرات - فتيان يركبان في جولة متعة - >- ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة تتحدث إليهن - source_sentence: الرجل يرتدي قميصاً أزرق. sentences: - >- رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة حمراء مع الماء في الخلفية. - كتاب القصص مفتوح - رجل يرتدي قميص أسود يعزف على الجيتار. - source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة. sentences: - ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه - رجل يستلقي على وجهه على مقعد في الحديقة. - الشاب نائم بينما الأم تقود ابنتها إلى الحديقة pipeline_tag: sentence-similarity model-index: - name: Omartificial-Intelligence-Space/Arabic-labse-Matryoshka results: - dataset: config: default name: MTEB BIOSSES (default) revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics: - type: cosine_pearson value: 76.46793440999714 - type: cosine_spearman value: 76.66439745271298 - type: euclidean_pearson value: 76.52075972347127 - type: euclidean_spearman value: 76.66439745271298 - type: main_score value: 76.66439745271298 - type: manhattan_pearson value: 76.68001857069733 - type: manhattan_spearman value: 76.73066402288269 task: type: STS - dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics: - type: cosine_pearson value: 79.67657890693198 - type: cosine_spearman value: 77.03286420274621 - type: euclidean_pearson value: 78.1960735272073 - type: euclidean_spearman value: 77.032855497919 - type: main_score value: 77.03286420274621 - type: manhattan_pearson value: 78.25627275994229 - type: manhattan_spearman value: 77.00430810589081 task: type: STS - dataset: config: default name: MTEB STS12 (default) revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cosine_pearson value: 83.94288954523996 - type: cosine_spearman value: 79.21432176112556 - type: euclidean_pearson value: 81.21333251943913 - type: euclidean_spearman value: 79.2152067330468 - type: main_score value: 79.21432176112556 - type: manhattan_pearson value: 81.16910737482634 - type: manhattan_spearman value: 79.08756466301445 task: type: STS - dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cosine_pearson value: 77.48393909963059 - type: cosine_spearman value: 79.54963868861196 - type: euclidean_pearson value: 79.28416002197451 - type: euclidean_spearman value: 79.54963861790114 - type: main_score value: 79.54963868861196 - type: manhattan_pearson value: 79.18653917582513 - type: manhattan_spearman value: 79.46713533414295 task: type: STS - dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cosine_pearson value: 78.51596313692846 - type: cosine_spearman value: 78.84601702652395 - type: euclidean_pearson value: 78.55199809961427 - type: euclidean_spearman value: 78.84603362286225 - type: main_score value: 78.84601702652395 - type: manhattan_pearson value: 78.52780170677605 - type: manhattan_spearman value: 78.77744294039178 task: type: STS - dataset: config: default name: MTEB STS15 (default) revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cosine_pearson value: 84.53393478889929 - type: cosine_spearman value: 85.60821849381648 - type: euclidean_pearson value: 85.32813923250558 - type: euclidean_spearman value: 85.6081835456016 - type: main_score value: 85.60821849381648 - type: manhattan_pearson value: 85.32782097916476 - type: manhattan_spearman value: 85.58098670898562 task: type: STS - dataset: config: default name: MTEB STS16 (default) revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cosine_pearson value: 77.00196998325856 - type: cosine_spearman value: 79.930951699069 - type: euclidean_pearson value: 79.43196738390897 - type: euclidean_spearman value: 79.93095112410258 - type: main_score value: 79.930951699069 - type: manhattan_pearson value: 79.33744358111427 - type: manhattan_spearman value: 79.82939266539601 task: type: STS - dataset: config: ar-ar name: MTEB STS17 (ar-ar) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics: - type: cosine_pearson value: 81.60289529424327 - type: cosine_spearman value: 82.46806381979653 - type: euclidean_pearson value: 81.32235058296072 - type: euclidean_spearman value: 82.46676890643914 - type: main_score value: 82.46806381979653 - type: manhattan_pearson value: 81.43885277175312 - type: manhattan_spearman value: 82.38955952718666 task: type: STS - dataset: config: ar name: MTEB STS22 (ar) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 49.58293768761314 - type: cosine_spearman value: 57.261888789832874 - type: euclidean_pearson value: 53.36549109538782 - type: euclidean_spearman value: 57.261888789832874 - type: main_score value: 57.261888789832874 - type: manhattan_pearson value: 53.06640323833928 - type: manhattan_spearman value: 57.05837935512948 task: type: STS - dataset: config: default name: MTEB STSBenchmark (default) revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cosine_pearson value: 81.43997935928729 - type: cosine_spearman value: 82.04996129795596 - type: euclidean_pearson value: 82.01917866996972 - type: euclidean_spearman value: 82.04996129795596 - type: main_score value: 82.04996129795596 - type: manhattan_pearson value: 82.03487112040936 - type: manhattan_spearman value: 82.03774605775651 task: type: STS - dataset: config: default name: MTEB SummEval (default) revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cosine_pearson value: 32.113475997147674 - type: cosine_spearman value: 32.17194233764879 - type: dot_pearson value: 32.113469728827255 - type: dot_spearman value: 32.174771315355386 - type: main_score value: 32.17194233764879 - type: pearson value: 32.113475997147674 - type: spearman value: 32.17194233764879 task: type: Summarization - name: SentenceTransformer based on sentence-transformers/LaBSE results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: 0.7269177710249681 name: Pearson Cosine - type: spearman_cosine value: 0.7225258779395222 name: Spearman Cosine - type: pearson_manhattan value: 0.7259261785622463 name: Pearson Manhattan - type: spearman_manhattan value: 0.7210463582530393 name: Spearman Manhattan - type: pearson_euclidean value: 0.7259567884235211 name: Pearson Euclidean - type: spearman_euclidean value: 0.722525823788783 name: Spearman Euclidean - type: pearson_dot value: 0.7269177712136122 name: Pearson Dot - type: spearman_dot value: 0.7225258771129475 name: Spearman Dot - type: pearson_max value: 0.7269177712136122 name: Pearson Max - type: spearman_max value: 0.7225258779395222 name: Spearman Max - type: pearson_cosine value: 0.8143867576376295 name: Pearson Cosine - type: spearman_cosine value: 0.8205044914629483 name: Spearman Cosine - type: pearson_manhattan value: 0.8203365887013151 name: Pearson Manhattan - type: spearman_manhattan value: 0.8203816698535976 name: Spearman Manhattan - type: pearson_euclidean value: 0.8201809453496319 name: Pearson Euclidean - type: spearman_euclidean value: 0.8205044914629483 name: Spearman Euclidean - type: pearson_dot value: 0.8143867541070537 name: Pearson Dot - type: spearman_dot value: 0.8205044914629483 name: Spearman Dot - type: pearson_max value: 0.8203365887013151 name: Pearson Max - type: spearman_max value: 0.8205044914629483 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.7268389724271859 name: Pearson Cosine - type: spearman_cosine value: 0.7224359411000278 name: Spearman Cosine - type: pearson_manhattan value: 0.7241418669615103 name: Pearson Manhattan - type: spearman_manhattan value: 0.7195408311833029 name: Spearman Manhattan - type: pearson_euclidean value: 0.7248184919191593 name: Pearson Euclidean - type: spearman_euclidean value: 0.7212936866178097 name: Spearman Euclidean - type: pearson_dot value: 0.7252522928016701 name: Pearson Dot - type: spearman_dot value: 0.7205040482865328 name: Spearman Dot - type: pearson_max value: 0.7268389724271859 name: Pearson Max - type: spearman_max value: 0.7224359411000278 name: Spearman Max - type: pearson_cosine value: 0.8143448965624136 name: Pearson Cosine - type: spearman_cosine value: 0.8211700903453509 name: Spearman Cosine - type: pearson_manhattan value: 0.8217448619823571 name: Pearson Manhattan - type: spearman_manhattan value: 0.8216016599665544 name: Spearman Manhattan - type: pearson_euclidean value: 0.8216413349390971 name: Pearson Euclidean - type: spearman_euclidean value: 0.82188122418776 name: Spearman Euclidean - type: pearson_dot value: 0.8097020064483653 name: Pearson Dot - type: spearman_dot value: 0.8147306090545295 name: Spearman Dot - type: pearson_max value: 0.8217448619823571 name: Pearson Max - type: spearman_max value: 0.82188122418776 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.7283468617741852 name: Pearson Cosine - type: spearman_cosine value: 0.7264294106954872 name: Spearman Cosine - type: pearson_manhattan value: 0.7227711798003426 name: Pearson Manhattan - type: spearman_manhattan value: 0.718067982079232 name: Spearman Manhattan - type: pearson_euclidean value: 0.7251492361775083 name: Pearson Euclidean - type: spearman_euclidean value: 0.7215068115809131 name: Spearman Euclidean - type: pearson_dot value: 0.7243396991648858 name: Pearson Dot - type: spearman_dot value: 0.7221390873398206 name: Spearman Dot - type: pearson_max value: 0.7283468617741852 name: Pearson Max - type: spearman_max value: 0.7264294106954872 name: Spearman Max - type: pearson_cosine value: 0.8075613785257986 name: Pearson Cosine - type: spearman_cosine value: 0.8159258089804861 name: Spearman Cosine - type: pearson_manhattan value: 0.8208711370091426 name: Pearson Manhattan - type: spearman_manhattan value: 0.8196747601014518 name: Spearman Manhattan - type: pearson_euclidean value: 0.8210210137439432 name: Pearson Euclidean - type: spearman_euclidean value: 0.8203004500356083 name: Spearman Euclidean - type: pearson_dot value: 0.7870611647231145 name: Pearson Dot - type: spearman_dot value: 0.7874848213991118 name: Spearman Dot - type: pearson_max value: 0.8210210137439432 name: Pearson Max - type: spearman_max value: 0.8203004500356083 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.7102082520621849 name: Pearson Cosine - type: spearman_cosine value: 0.7103917869311991 name: Spearman Cosine - type: pearson_manhattan value: 0.7134729607181519 name: Pearson Manhattan - type: spearman_manhattan value: 0.708895102058259 name: Spearman Manhattan - type: pearson_euclidean value: 0.7171545288118942 name: Pearson Euclidean - type: spearman_euclidean value: 0.7130380237150746 name: Spearman Euclidean - type: pearson_dot value: 0.6777774738547628 name: Pearson Dot - type: spearman_dot value: 0.6746474823963989 name: Spearman Dot - type: pearson_max value: 0.7171545288118942 name: Pearson Max - type: spearman_max value: 0.7130380237150746 name: Spearman Max - type: pearson_cosine value: 0.8024378358145556 name: Pearson Cosine - type: spearman_cosine value: 0.8117561815472325 name: Spearman Cosine - type: pearson_manhattan value: 0.818920309459774 name: Pearson Manhattan - type: spearman_manhattan value: 0.8180515365910205 name: Spearman Manhattan - type: pearson_euclidean value: 0.8198346073356603 name: Pearson Euclidean - type: spearman_euclidean value: 0.8185162896024369 name: Spearman Euclidean - type: pearson_dot value: 0.7513270537478935 name: Pearson Dot - type: spearman_dot value: 0.7427542871546953 name: Spearman Dot - type: pearson_max value: 0.8198346073356603 name: Pearson Max - type: spearman_max value: 0.8185162896024369 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.6930745722517785 name: Pearson Cosine - type: spearman_cosine value: 0.6982194042238953 name: Spearman Cosine - type: pearson_manhattan value: 0.6971382079778946 name: Pearson Manhattan - type: spearman_manhattan value: 0.6942362764367931 name: Spearman Manhattan - type: pearson_euclidean value: 0.7012627015062325 name: Pearson Euclidean - type: spearman_euclidean value: 0.6986972295835788 name: Spearman Euclidean - type: pearson_dot value: 0.6376735798940838 name: Pearson Dot - type: spearman_dot value: 0.6344835722310429 name: Spearman Dot - type: pearson_max value: 0.7012627015062325 name: Pearson Max - type: spearman_max value: 0.6986972295835788 name: Spearman Max - type: pearson_cosine value: 0.7855080652087961 name: Pearson Cosine - type: spearman_cosine value: 0.7948979371698327 name: Spearman Cosine - type: pearson_manhattan value: 0.8060407473462375 name: Pearson Manhattan - type: spearman_manhattan value: 0.8041199691999044 name: Spearman Manhattan - type: pearson_euclidean value: 0.8088262858195556 name: Pearson Euclidean - type: spearman_euclidean value: 0.8060483394849104 name: Spearman Euclidean - type: pearson_dot value: 0.677754045289596 name: Pearson Dot - type: spearman_dot value: 0.6616232873061395 name: Spearman Dot - type: pearson_max value: 0.8088262858195556 name: Pearson Max - type: spearman_max value: 0.8060483394849104 name: Spearman Max license: apache-2.0 --- # SentenceTransformer based on sentence-transformers/LaBSE This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - Omartificial-Intelligence-Space/arabic-n_li-triplet ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) (3): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Omartificial-Intelligence-Space/Arabic-labse") # Run inference sentences = [ 'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.', 'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه', 'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test-768` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7269 | | **spearman_cosine** | **0.7225** | | pearson_manhattan | 0.7259 | | spearman_manhattan | 0.721 | | pearson_euclidean | 0.726 | | spearman_euclidean | 0.7225 | | pearson_dot | 0.7269 | | spearman_dot | 0.7225 | | pearson_max | 0.7269 | | spearman_max | 0.7225 | #### Semantic Similarity * Dataset: `sts-test-512` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7268 | | **spearman_cosine** | **0.7224** | | pearson_manhattan | 0.7241 | | spearman_manhattan | 0.7195 | | pearson_euclidean | 0.7248 | | spearman_euclidean | 0.7213 | | pearson_dot | 0.7253 | | spearman_dot | 0.7205 | | pearson_max | 0.7268 | | spearman_max | 0.7224 | #### Semantic Similarity * Dataset: `sts-test-256` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7283 | | **spearman_cosine** | **0.7264** | | pearson_manhattan | 0.7228 | | spearman_manhattan | 0.7181 | | pearson_euclidean | 0.7251 | | spearman_euclidean | 0.7215 | | pearson_dot | 0.7243 | | spearman_dot | 0.7221 | | pearson_max | 0.7283 | | spearman_max | 0.7264 | #### Semantic Similarity * Dataset: `sts-test-128` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7102 | | **spearman_cosine** | **0.7104** | | pearson_manhattan | 0.7135 | | spearman_manhattan | 0.7089 | | pearson_euclidean | 0.7172 | | spearman_euclidean | 0.713 | | pearson_dot | 0.6778 | | spearman_dot | 0.6746 | | pearson_max | 0.7172 | | spearman_max | 0.713 | #### Semantic Similarity * Dataset: `sts-test-64` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.6931 | | **spearman_cosine** | **0.6982** | | pearson_manhattan | 0.6971 | | spearman_manhattan | 0.6942 | | pearson_euclidean | 0.7013 | | spearman_euclidean | 0.6987 | | pearson_dot | 0.6377 | | spearman_dot | 0.6345 | | pearson_max | 0.7013 | | spearman_max | 0.6987 | #### Semantic Similarity * Dataset: `sts-test-768` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8144 | | **spearman_cosine** | **0.8205** | | pearson_manhattan | 0.8203 | | spearman_manhattan | 0.8204 | | pearson_euclidean | 0.8202 | | spearman_euclidean | 0.8205 | | pearson_dot | 0.8144 | | spearman_dot | 0.8205 | | pearson_max | 0.8203 | | spearman_max | 0.8205 | #### Semantic Similarity * Dataset: `sts-test-512` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8143 | | **spearman_cosine** | **0.8212** | | pearson_manhattan | 0.8217 | | spearman_manhattan | 0.8216 | | pearson_euclidean | 0.8216 | | spearman_euclidean | 0.8219 | | pearson_dot | 0.8097 | | spearman_dot | 0.8147 | | pearson_max | 0.8217 | | spearman_max | 0.8219 | #### Semantic Similarity * Dataset: `sts-test-256` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8076 | | **spearman_cosine** | **0.8159** | | pearson_manhattan | 0.8209 | | spearman_manhattan | 0.8197 | | pearson_euclidean | 0.821 | | spearman_euclidean | 0.8203 | | pearson_dot | 0.7871 | | spearman_dot | 0.7875 | | pearson_max | 0.821 | | spearman_max | 0.8203 | #### Semantic Similarity * Dataset: `sts-test-128` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8024 | | **spearman_cosine** | **0.8118** | | pearson_manhattan | 0.8189 | | spearman_manhattan | 0.8181 | | pearson_euclidean | 0.8198 | | spearman_euclidean | 0.8185 | | pearson_dot | 0.7513 | | spearman_dot | 0.7428 | | pearson_max | 0.8198 | | spearman_max | 0.8185 | #### Semantic Similarity * Dataset: `sts-test-64` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7855 | | **spearman_cosine** | **0.7949** | | pearson_manhattan | 0.806 | | spearman_manhattan | 0.8041 | | pearson_euclidean | 0.8088 | | spearman_euclidean | 0.806 | | pearson_dot | 0.6778 | | spearman_dot | 0.6616 | | pearson_max | 0.8088 | | spearman_max | 0.806 | ## Training Details ### Training Dataset #### Omartificial-Intelligence-Space/arabic-n_li-triplet * Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet * Size: 557,850 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:------------------------------------------------------------|:--------------------------------------------|:------------------------------------| | شخص على حصان يقفز فوق طائرة معطلة | شخص في الهواء الطلق، على حصان. | شخص في مطعم، يطلب عجة. | | أطفال يبتسمون و يلوحون للكاميرا | هناك أطفال حاضرون | الاطفال يتجهمون | | صبي يقفز على لوح التزلج في منتصف الجسر الأحمر. | الفتى يقوم بخدعة التزلج | الصبي يتزلج على الرصيف | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### Omartificial-Intelligence-Space/arabic-n_li-triplet * Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet * Size: 6,584 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------| | امرأتان يتعانقان بينما يحملان حزمة | إمرأتان يحملان حزمة | الرجال يتشاجرون خارج مطعم | | طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة. | طفلين يرتديان قميصاً مرقماً يغسلون أيديهم | طفلين يرتديان سترة يذهبان إلى المدرسة | | رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس | رجل يبيع الدونات لعميل | امرأة تشرب قهوتها في مقهى صغير | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine | |:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | None | 0 | - | 0.7104 | 0.7264 | 0.7224 | 0.6982 | 0.7225 | | 0.0229 | 200 | 13.1738 | - | - | - | - | - | | 0.0459 | 400 | 8.8127 | - | - | - | - | - | | 0.0688 | 600 | 8.0984 | - | - | - | - | - | | 0.0918 | 800 | 7.2984 | - | - | - | - | - | | 0.1147 | 1000 | 7.5749 | - | - | - | - | - | | 0.1377 | 1200 | 7.1292 | - | - | - | - | - | | 0.1606 | 1400 | 6.6146 | - | - | - | - | - | | 0.1835 | 1600 | 6.6523 | - | - | - | - | - | | 0.2065 | 1800 | 6.1095 | - | - | - | - | - | | 0.2294 | 2000 | 6.0841 | - | - | - | - | - | | 0.2524 | 2200 | 6.3024 | - | - | - | - | - | | 0.2753 | 2400 | 6.1941 | - | - | - | - | - | | 0.2983 | 2600 | 6.1686 | - | - | - | - | - | | 0.3212 | 2800 | 5.8317 | - | - | - | - | - | | 0.3442 | 3000 | 6.0597 | - | - | - | - | - | | 0.3671 | 3200 | 5.7832 | - | - | - | - | - | | 0.3900 | 3400 | 5.7088 | - | - | - | - | - | | 0.4130 | 3600 | 5.6988 | - | - | - | - | - | | 0.4359 | 3800 | 5.5268 | - | - | - | - | - | | 0.4589 | 4000 | 5.5543 | - | - | - | - | - | | 0.4818 | 4200 | 5.3152 | - | - | - | - | - | | 0.5048 | 4400 | 5.2894 | - | - | - | - | - | | 0.5277 | 4600 | 5.1805 | - | - | - | - | - | | 0.5506 | 4800 | 5.4559 | - | - | - | - | - | | 0.5736 | 5000 | 5.3836 | - | - | - | - | - | | 0.5965 | 5200 | 5.2626 | - | - | - | - | - | | 0.6195 | 5400 | 5.2511 | - | - | - | - | - | | 0.6424 | 5600 | 5.3308 | - | - | - | - | - | | 0.6654 | 5800 | 5.2264 | - | - | - | - | - | | 0.6883 | 6000 | 5.2881 | - | - | - | - | - | | 0.7113 | 6200 | 5.1349 | - | - | - | - | - | | 0.7342 | 6400 | 5.0872 | - | - | - | - | - | | 0.7571 | 6600 | 4.5515 | - | - | - | - | - | | 0.7801 | 6800 | 3.4312 | - | - | - | - | - | | 0.8030 | 7000 | 3.1008 | - | - | - | - | - | | 0.8260 | 7200 | 2.9582 | - | - | - | - | - | | 0.8489 | 7400 | 2.8153 | - | - | - | - | - | | 0.8719 | 7600 | 2.7214 | - | - | - | - | - | | 0.8948 | 7800 | 2.5392 | - | - | - | - | - | | 0.9177 | 8000 | 2.584 | - | - | - | - | - | | 0.9407 | 8200 | 2.5384 | - | - | - | - | - | | 0.9636 | 8400 | 2.4937 | - | - | - | - | - | | 0.9866 | 8600 | 2.4155 | - | - | - | - | - | | 1.0 | 8717 | - | 0.8118 | 0.8159 | 0.8212 | 0.7949 | 0.8205 | ### Framework Versions - Python: 3.9.18 - Sentence Transformers: 3.0.1 - Transformers: 4.40.0 - PyTorch: 2.2.2+cu121 - Accelerate: 0.26.1 - Datasets: 2.19.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgments The author would like to thank Prince Sultan University for their invaluable support in this project. Their contributions and resources have been instrumental in the development and fine-tuning of these models. ```markdown ## Citation If you use the Arabic Matryoshka Embeddings Model, please cite it as follows: @misc{nacar2024enhancingsemanticsimilarityunderstanding, title={Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning}, author={Omer Nacar and Anis Koubaa}, year={2024}, eprint={2407.21139}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.21139}, }