--- 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/paraphrase-multilingual-MiniLM-L12-v2 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-MiniLM-L12-v2-all-nli-triplet results: - dataset: config: default name: MTEB BIOSSES (default) revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics: - type: cosine_pearson value: 72.5081840952171 - type: cosine_spearman value: 69.41362982941537 - type: euclidean_pearson value: 67.45121490183709 - type: euclidean_spearman value: 67.15273493989758 - type: main_score value: 69.41362982941537 - type: manhattan_pearson value: 67.6119022794479 - type: manhattan_spearman value: 67.51659865246586 task: type: STS - dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics: - type: cosine_pearson value: 83.61591268324493 - type: cosine_spearman value: 79.61914245705792 - type: euclidean_pearson value: 81.32044881859483 - type: euclidean_spearman value: 79.04866675279919 - type: main_score value: 79.61914245705792 - type: manhattan_pearson value: 81.09220518201322 - type: manhattan_spearman value: 78.87590523907905 task: type: STS - dataset: config: default name: MTEB STS12 (default) revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cosine_pearson value: 84.59807803376341 - type: cosine_spearman value: 77.38689922564416 - type: euclidean_pearson value: 83.92034850646732 - type: euclidean_spearman value: 76.75857193093438 - type: main_score value: 77.38689922564416 - type: manhattan_pearson value: 83.97191863964667 - type: manhattan_spearman value: 76.89790070725708 task: type: STS - dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cosine_pearson value: 78.18664268536664 - type: cosine_spearman value: 79.58989311630421 - type: euclidean_pearson value: 79.25259731614729 - type: euclidean_spearman value: 80.1701122827397 - type: main_score value: 79.58989311630421 - type: manhattan_pearson value: 79.12601451996869 - type: manhattan_spearman value: 79.98999436073663 task: type: STS - dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cosine_pearson value: 80.97541876658141 - type: cosine_spearman value: 79.78614320477877 - type: euclidean_pearson value: 81.01514505747167 - type: euclidean_spearman value: 80.73664735567839 - type: main_score value: 79.78614320477877 - type: manhattan_pearson value: 80.8746560526314 - type: manhattan_spearman value: 80.67025673179079 task: type: STS - dataset: config: default name: MTEB STS15 (default) revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cosine_pearson value: 85.23661155813113 - type: cosine_spearman value: 86.21134464371615 - type: euclidean_pearson value: 85.82518684522182 - type: euclidean_spearman value: 86.43600784349509 - type: main_score value: 86.21134464371615 - type: manhattan_pearson value: 85.83101152371589 - type: manhattan_spearman value: 86.42228695679498 task: type: STS - dataset: config: default name: MTEB STS16 (default) revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cosine_pearson value: 79.20106689077852 - type: cosine_spearman value: 81.39570893867825 - type: euclidean_pearson value: 80.39578888768929 - type: euclidean_spearman value: 81.19950443340412 - type: main_score value: 81.39570893867825 - type: manhattan_pearson value: 80.2226679341839 - type: manhattan_spearman value: 80.99142422593823 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: 81.05294851623468 - type: cosine_spearman value: 81.10570655134113 - type: euclidean_pearson value: 79.22292773537778 - type: euclidean_spearman value: 78.84204232638425 - type: main_score value: 81.10570655134113 - type: manhattan_pearson value: 79.43750460320484 - type: manhattan_spearman value: 79.33713593557482 task: type: STS - dataset: config: ar name: MTEB STS22 (ar) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 45.96875498680092 - type: cosine_spearman value: 52.405509117149904 - type: euclidean_pearson value: 42.097450896728226 - type: euclidean_spearman value: 50.89022884113707 - type: main_score value: 52.405509117149904 - type: manhattan_pearson value: 42.22827727075534 - type: manhattan_spearman value: 50.912841055442634 task: type: STS - dataset: config: default name: MTEB STSBenchmark (default) revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cosine_pearson value: 83.13261516884116 - type: cosine_spearman value: 84.3492527221498 - type: euclidean_pearson value: 82.691603178401 - type: euclidean_spearman value: 83.0499566200785 - type: main_score value: 84.3492527221498 - type: manhattan_pearson value: 82.68307441014618 - type: manhattan_spearman value: 83.01315787964519 task: type: STS - dataset: config: default name: MTEB SummEval (default) revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cosine_pearson value: 31.149232235402845 - type: cosine_spearman value: 30.685504130606255 - type: dot_pearson value: 27.466307571160375 - type: dot_spearman value: 28.93064261485915 - type: main_score value: 30.685504130606255 - type: pearson value: 31.149232235402845 - type: spearman value: 30.685504130606255 task: type: Summarization - name: >- SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.8264447022356382 name: Pearson Cosine - type: spearman_cosine value: 0.8386403752382455 name: Spearman Cosine - type: pearson_manhattan value: 0.8219134931449013 name: Pearson Manhattan - type: spearman_manhattan value: 0.825509659109493 name: Spearman Manhattan - type: pearson_euclidean value: 0.8223094468630248 name: Pearson Euclidean - type: spearman_euclidean value: 0.8260503151751462 name: Spearman Euclidean - type: pearson_dot value: 0.6375226884845725 name: Pearson Dot - type: spearman_dot value: 0.6287228614640888 name: Spearman Dot - type: pearson_max value: 0.8264447022356382 name: Pearson Max - type: spearman_max value: 0.8386403752382455 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.8209661910768973 name: Pearson Cosine - type: spearman_cosine value: 0.8347149482673766 name: Spearman Cosine - type: pearson_manhattan value: 0.8082811559854036 name: Pearson Manhattan - type: spearman_manhattan value: 0.8148314269262763 name: Spearman Manhattan - type: pearson_euclidean value: 0.8093138512113149 name: Pearson Euclidean - type: spearman_euclidean value: 0.8156468458613929 name: Spearman Euclidean - type: pearson_dot value: 0.5795109620454884 name: Pearson Dot - type: spearman_dot value: 0.5760223026552876 name: Spearman Dot - type: pearson_max value: 0.8209661910768973 name: Pearson Max - type: spearman_max value: 0.8347149482673766 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.808708530451336 name: Pearson Cosine - type: spearman_cosine value: 0.8217532539767914 name: Spearman Cosine - type: pearson_manhattan value: 0.7876121380998453 name: Pearson Manhattan - type: spearman_manhattan value: 0.7969092304137347 name: Spearman Manhattan - type: pearson_euclidean value: 0.7902997966909958 name: Pearson Euclidean - type: spearman_euclidean value: 0.7987635968785215 name: Spearman Euclidean - type: pearson_dot value: 0.495047136234386 name: Pearson Dot - type: spearman_dot value: 0.49287000679901516 name: Spearman Dot - type: pearson_max value: 0.808708530451336 name: Pearson Max - type: spearman_max value: 0.8217532539767914 name: Spearman Max license: apache-2.0 datasets: - Omartificial-Intelligence-Space/Arabic-NLi-Triplet --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 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. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 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: BertModel (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}) ) ``` ## 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/MiniLM-L12-v2-all-nli-triplet") # 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, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### 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 | 0.8264 | | **spearman_cosine** | **0.8386** | | pearson_manhattan | 0.8219 | | spearman_manhattan | 0.8255 | | pearson_euclidean | 0.8223 | | spearman_euclidean | 0.8261 | | pearson_dot | 0.6375 | | spearman_dot | 0.6287 | | pearson_max | 0.8264 | | spearman_max | 0.8386 | #### 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 | 0.821 | | **spearman_cosine** | **0.8347** | | pearson_manhattan | 0.8083 | | spearman_manhattan | 0.8148 | | pearson_euclidean | 0.8093 | | spearman_euclidean | 0.8156 | | pearson_dot | 0.5795 | | spearman_dot | 0.576 | | pearson_max | 0.821 | | spearman_max | 0.8347 | #### 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 | 0.8087 | | **spearman_cosine** | **0.8218** | | pearson_manhattan | 0.7876 | | spearman_manhattan | 0.7969 | | pearson_euclidean | 0.7903 | | spearman_euclidean | 0.7988 | | pearson_dot | 0.495 | | spearman_dot | 0.4929 | | pearson_max | 0.8087 | | spearman_max | 0.8218 | ## 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": [ 256, 128, 64 ], "matryoshka_weights": [ 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": [ 256, 128, 64 ], "matryoshka_weights": [ 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-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} } ``` ## 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: ```bibtex @software{nacar2024, author = {Omer Nacar}, title = {Arabic Matryoshka Embeddings Model - Arabic MiniLM L12 v2 All Nli Triplet}, year = 2024, url = {https://huggingface.co/Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet}, version = {1.0.0}, }