--- inference: false language: - ar library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss - mteb base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 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-all-nli-triplet-Matryoshka results: - dataset: config: default name: MTEB BIOSSES (default) revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics: - type: cosine_pearson value: 81.20578037912223 - type: cosine_spearman value: 77.43670420687278 - type: euclidean_pearson value: 74.60444698819703 - type: euclidean_spearman value: 72.25767053642666 - type: main_score value: 77.43670420687278 - type: manhattan_pearson value: 73.86951335383257 - type: manhattan_spearman value: 71.41608509527123 task: type: STS - dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics: - type: cosine_pearson value: 83.11155556919923 - type: cosine_spearman value: 79.39435627520159 - type: euclidean_pearson value: 81.05225024180342 - type: euclidean_spearman value: 79.09926890001618 - type: main_score value: 79.39435627520159 - type: manhattan_pearson value: 80.74351302609706 - type: manhattan_spearman value: 78.826254748334 task: type: STS - dataset: config: default name: MTEB STS12 (default) revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cosine_pearson value: 85.10074960888633 - type: cosine_spearman value: 78.93043293576132 - type: euclidean_pearson value: 84.1168219787408 - type: euclidean_spearman value: 78.44739559202252 - type: main_score value: 78.93043293576132 - type: manhattan_pearson value: 83.79447841594396 - type: manhattan_spearman value: 77.94028171700384 task: type: STS - dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cosine_pearson value: 81.34459901517775 - type: cosine_spearman value: 82.73032633919925 - type: euclidean_pearson value: 82.83546499367434 - type: euclidean_spearman value: 83.29701673615389 - type: main_score value: 82.73032633919925 - type: manhattan_pearson value: 82.63480502797324 - type: manhattan_spearman value: 83.05016589615636 task: type: STS - dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cosine_pearson value: 82.53179983763488 - type: cosine_spearman value: 81.64974497557361 - type: euclidean_pearson value: 83.03981070806898 - type: euclidean_spearman value: 82.65556168300631 - type: main_score value: 81.64974497557361 - type: manhattan_pearson value: 82.83722360191446 - type: manhattan_spearman value: 82.4164264119 task: type: STS - dataset: config: default name: MTEB STS15 (default) revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cosine_pearson value: 86.5684162475647 - type: cosine_spearman value: 87.62163215009723 - type: euclidean_pearson value: 87.3068288651339 - type: euclidean_spearman value: 88.03508640722863 - type: main_score value: 87.62163215009723 - type: manhattan_pearson value: 87.21818681800193 - type: manhattan_spearman value: 87.94690511382603 task: type: STS - dataset: config: default name: MTEB STS16 (default) revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cosine_pearson value: 81.70518105237446 - type: cosine_spearman value: 83.66083698795428 - type: euclidean_pearson value: 82.80400684544435 - type: euclidean_spearman value: 83.39926895275799 - type: main_score value: 83.66083698795428 - type: manhattan_pearson value: 82.44430538731845 - type: manhattan_spearman value: 82.99600783826028 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: 82.23229967696153 - type: cosine_spearman value: 82.40039006538706 - type: euclidean_pearson value: 79.21322872573518 - type: euclidean_spearman value: 79.14230529579783 - type: main_score value: 82.40039006538706 - type: manhattan_pearson value: 79.1476348987964 - type: manhattan_spearman value: 78.82381660638143 task: type: STS - dataset: config: ar name: MTEB STS22 (ar) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 45.95767124518871 - type: cosine_spearman value: 51.37922888872568 - type: euclidean_pearson value: 45.519471121310126 - type: euclidean_spearman value: 51.45605803385654 - type: main_score value: 51.37922888872568 - type: manhattan_pearson value: 45.98761117909666 - type: manhattan_spearman value: 51.48451973989366 task: type: STS - dataset: config: default name: MTEB STSBenchmark (default) revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cosine_pearson value: 85.38916827757183 - type: cosine_spearman value: 86.16303183485594 - type: euclidean_pearson value: 85.16406897245115 - type: euclidean_spearman value: 85.40364087457081 - type: main_score value: 86.16303183485594 - type: manhattan_pearson value: 84.96853193915084 - type: manhattan_spearman value: 85.13238442843544 task: type: STS - dataset: config: default name: MTEB SummEval (default) revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cosine_pearson value: 30.077426987171158 - type: cosine_spearman value: 30.163682020271608 - type: dot_pearson value: 27.31125295906803 - type: dot_spearman value: 29.138235153208193 - type: main_score value: 30.163682020271608 - type: pearson value: 30.077426987171158 - type: spearman value: 30.163682020271608 task: type: Summarization - name: >- SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: 0.8538831619509135 name: Pearson Cosine - type: spearman_cosine value: 0.861625750018802 name: Spearman Cosine - type: pearson_manhattan value: 0.8496745674597512 name: Pearson Manhattan - type: spearman_manhattan value: 0.8513333417508545 name: Spearman Manhattan - type: pearson_euclidean value: 0.8516261261374778 name: Pearson Euclidean - type: spearman_euclidean value: 0.8540549341060195 name: Spearman Euclidean - type: pearson_dot value: 0.7281308266536204 name: Pearson Dot - type: spearman_dot value: 0.7230282720855726 name: Spearman Dot - type: pearson_max value: 0.8538831619509135 name: Pearson Max - type: spearman_max value: 0.861625750018802 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.8542379189261009 name: Pearson Cosine - type: spearman_cosine value: 0.8609329396560859 name: Spearman Cosine - type: pearson_manhattan value: 0.8486657899695456 name: Pearson Manhattan - type: spearman_manhattan value: 0.8512120732504748 name: Spearman Manhattan - type: pearson_euclidean value: 0.8505249483849495 name: Pearson Euclidean - type: spearman_euclidean value: 0.8538738365440234 name: Spearman Euclidean - type: pearson_dot value: 0.7075618032859148 name: Pearson Dot - type: spearman_dot value: 0.7028728329509918 name: Spearman Dot - type: pearson_max value: 0.8542379189261009 name: Pearson Max - type: spearman_max value: 0.8609329396560859 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.8486308733045101 name: Pearson Cosine - type: spearman_cosine value: 0.8578681811996274 name: Spearman Cosine - type: pearson_manhattan value: 0.8404506123980291 name: Pearson Manhattan - type: spearman_manhattan value: 0.845565163232125 name: Spearman Manhattan - type: pearson_euclidean value: 0.8414758099131773 name: Pearson Euclidean - type: spearman_euclidean value: 0.8471566121478254 name: Spearman Euclidean - type: pearson_dot value: 0.6668664182302968 name: Pearson Dot - type: spearman_dot value: 0.6651222481800894 name: Spearman Dot - type: pearson_max value: 0.8486308733045101 name: Pearson Max - type: spearman_max value: 0.8578681811996274 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.8389761445410956 name: Pearson Cosine - type: spearman_cosine value: 0.8499312736457453 name: Spearman Cosine - type: pearson_manhattan value: 0.8287388421834582 name: Pearson Manhattan - type: spearman_manhattan value: 0.8353046807483782 name: Spearman Manhattan - type: pearson_euclidean value: 0.8297699263897746 name: Pearson Euclidean - type: spearman_euclidean value: 0.8371843253238523 name: Spearman Euclidean - type: pearson_dot value: 0.5855876200722326 name: Pearson Dot - type: spearman_dot value: 0.5834920267418124 name: Spearman Dot - type: pearson_max value: 0.8389761445410956 name: Pearson Max - type: spearman_max value: 0.8499312736457453 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.8290685425698586 name: Pearson Cosine - type: spearman_cosine value: 0.8429054799136109 name: Spearman Cosine - type: pearson_manhattan value: 0.8100968316314205 name: Pearson Manhattan - type: spearman_manhattan value: 0.8221121550434057 name: Spearman Manhattan - type: pearson_euclidean value: 0.8129044863346081 name: Pearson Euclidean - type: spearman_euclidean value: 0.8255133471709527 name: Spearman Euclidean - type: pearson_dot value: 0.5067257944655903 name: Pearson Dot - type: spearman_dot value: 0.5109761436588146 name: Spearman Dot - type: pearson_max value: 0.8290685425698586 name: Pearson Max - type: spearman_max value: 0.8429054799136109 name: Spearman Max license: apache-2.0 --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) 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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) - **Maximum Sequence Length:** 128 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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-Nli-Matryoshka") # 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 | 0.8539 | | **spearman_cosine** | **0.8616** | | pearson_manhattan | 0.8497 | | spearman_manhattan | 0.8513 | | pearson_euclidean | 0.8516 | | spearman_euclidean | 0.8541 | | pearson_dot | 0.7281 | | spearman_dot | 0.723 | | pearson_max | 0.8539 | | spearman_max | 0.8616 | #### 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 | 0.8542 | | **spearman_cosine** | **0.8609** | | pearson_manhattan | 0.8487 | | spearman_manhattan | 0.8512 | | pearson_euclidean | 0.8505 | | spearman_euclidean | 0.8539 | | pearson_dot | 0.7076 | | spearman_dot | 0.7029 | | pearson_max | 0.8542 | | spearman_max | 0.8609 | #### 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 | 0.8486 | | **spearman_cosine** | **0.8579** | | pearson_manhattan | 0.8405 | | spearman_manhattan | 0.8456 | | pearson_euclidean | 0.8415 | | spearman_euclidean | 0.8472 | | pearson_dot | 0.6669 | | spearman_dot | 0.6651 | | pearson_max | 0.8486 | | spearman_max | 0.8579 | #### 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 | 0.839 | | **spearman_cosine** | **0.8499** | | pearson_manhattan | 0.8287 | | spearman_manhattan | 0.8353 | | pearson_euclidean | 0.8298 | | spearman_euclidean | 0.8372 | | pearson_dot | 0.5856 | | spearman_dot | 0.5835 | | pearson_max | 0.839 | | spearman_max | 0.8499 | #### 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 | 0.8291 | | **spearman_cosine** | **0.8429** | | pearson_manhattan | 0.8101 | | spearman_manhattan | 0.8221 | | pearson_euclidean | 0.8129 | | spearman_euclidean | 0.8255 | | pearson_dot | 0.5067 | | spearman_dot | 0.511 | | pearson_max | 0.8291 | | spearman_max | 0.8429 | ## 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`: 256 - `per_device_eval_batch_size`: 256 - `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`: 256 - `per_device_eval_batch_size`: 256 - `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 | |:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | 0.2294 | 500 | 10.1279 | - | - | - | - | - | | 0.4587 | 1000 | 8.0384 | - | - | - | - | - | | 0.6881 | 1500 | 7.3484 | - | - | - | - | - | | 0.9174 | 2000 | 4.2216 | - | - | - | - | - | | 1.0 | 2180 | - | 0.8499 | 0.8579 | 0.8609 | 0.8429 | 0.8616 | ### 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}, }