--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: tomaarsen/mpnet-base-all-nli-triplet datasets: [] 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: SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: 0.66986244175229 name: Pearson Cosine - type: spearman_cosine value: 0.675651628513557 name: Spearman Cosine - type: pearson_manhattan value: 0.6943200977280434 name: Pearson Manhattan - type: spearman_manhattan value: 0.6839707658313092 name: Spearman Manhattan - type: pearson_euclidean value: 0.6973190148612566 name: Pearson Euclidean - type: spearman_euclidean value: 0.6872926092972673 name: Spearman Euclidean - type: pearson_dot value: 0.5534197296097646 name: Pearson Dot - type: spearman_dot value: 0.5421965591416092 name: Spearman Dot - type: pearson_max value: 0.6973190148612566 name: Pearson Max - type: spearman_max value: 0.6872926092972673 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.6628171358537143 name: Pearson Cosine - type: spearman_cosine value: 0.670314701212355 name: Spearman Cosine - type: pearson_manhattan value: 0.6916567677127377 name: Pearson Manhattan - type: spearman_manhattan value: 0.6815748132707206 name: Spearman Manhattan - type: pearson_euclidean value: 0.6948756461188812 name: Pearson Euclidean - type: spearman_euclidean value: 0.685329042213794 name: Spearman Euclidean - type: pearson_dot value: 0.5229142840207227 name: Pearson Dot - type: spearman_dot value: 0.5113740757424073 name: Spearman Dot - type: pearson_max value: 0.6948756461188812 name: Pearson Max - type: spearman_max value: 0.685329042213794 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.6368313837029833 name: Pearson Cosine - type: spearman_cosine value: 0.6512526280069127 name: Spearman Cosine - type: pearson_manhattan value: 0.6832129716443456 name: Pearson Manhattan - type: spearman_manhattan value: 0.674638334774044 name: Spearman Manhattan - type: pearson_euclidean value: 0.6843664039671002 name: Pearson Euclidean - type: spearman_euclidean value: 0.6760040651639672 name: Spearman Euclidean - type: pearson_dot value: 0.4266095536126992 name: Pearson Dot - type: spearman_dot value: 0.4179376458107888 name: Spearman Dot - type: pearson_max value: 0.6843664039671002 name: Pearson Max - type: spearman_max value: 0.6760040651639672 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.6147896744901056 name: Pearson Cosine - type: spearman_cosine value: 0.6354730852658397 name: Spearman Cosine - type: pearson_manhattan value: 0.6730782159165468 name: Pearson Manhattan - type: spearman_manhattan value: 0.6652649799789521 name: Spearman Manhattan - type: pearson_euclidean value: 0.676407799774529 name: Pearson Euclidean - type: spearman_euclidean value: 0.6691409653459247 name: Spearman Euclidean - type: pearson_dot value: 0.35130869784942953 name: Pearson Dot - type: spearman_dot value: 0.3445374275232203 name: Spearman Dot - type: pearson_max value: 0.676407799774529 name: Pearson Max - type: spearman_max value: 0.6691409653459247 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.5789158725954748 name: Pearson Cosine - type: spearman_cosine value: 0.6081197115891086 name: Spearman Cosine - type: pearson_manhattan value: 0.6578631744829946 name: Pearson Manhattan - type: spearman_manhattan value: 0.6518503436513217 name: Spearman Manhattan - type: pearson_euclidean value: 0.6629734628760299 name: Pearson Euclidean - type: spearman_euclidean value: 0.6570510967281272 name: Spearman Euclidean - type: pearson_dot value: 0.24034366392620327 name: Pearson Dot - type: spearman_dot value: 0.2331392769925126 name: Spearman Dot - type: pearson_max value: 0.6629734628760299 name: Pearson Max - type: spearman_max value: 0.6570510967281272 name: Spearman Max --- # SentenceTransformer based on tomaarsen/mpnet-base-all-nli-triplet This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) 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:** [tomaarsen/mpnet-base-all-nli-triplet](https://huggingface.co/tomaarsen/mpnet-base-all-nli-triplet) - **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (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/mpnet-base-all-nli-triplet-Arabic-mpnet_base") # 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 | 0.6699 | | **spearman_cosine** | **0.6757** | | pearson_manhattan | 0.6943 | | spearman_manhattan | 0.684 | | pearson_euclidean | 0.6973 | | spearman_euclidean | 0.6873 | | pearson_dot | 0.5534 | | spearman_dot | 0.5422 | | pearson_max | 0.6973 | | spearman_max | 0.6873 | #### 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 | 0.6628 | | **spearman_cosine** | **0.6703** | | pearson_manhattan | 0.6917 | | spearman_manhattan | 0.6816 | | pearson_euclidean | 0.6949 | | spearman_euclidean | 0.6853 | | pearson_dot | 0.5229 | | spearman_dot | 0.5114 | | pearson_max | 0.6949 | | spearman_max | 0.6853 | #### 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 | 0.6368 | | **spearman_cosine** | **0.6513** | | pearson_manhattan | 0.6832 | | spearman_manhattan | 0.6746 | | pearson_euclidean | 0.6844 | | spearman_euclidean | 0.676 | | pearson_dot | 0.4266 | | spearman_dot | 0.4179 | | pearson_max | 0.6844 | | spearman_max | 0.676 | #### 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 | 0.6148 | | **spearman_cosine** | **0.6355** | | pearson_manhattan | 0.6731 | | spearman_manhattan | 0.6653 | | pearson_euclidean | 0.6764 | | spearman_euclidean | 0.6691 | | pearson_dot | 0.3513 | | spearman_dot | 0.3445 | | pearson_max | 0.6764 | | spearman_max | 0.6691 | #### 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 | 0.5789 | | **spearman_cosine** | **0.6081** | | pearson_manhattan | 0.6579 | | spearman_manhattan | 0.6519 | | pearson_euclidean | 0.663 | | spearman_euclidean | 0.6571 | | pearson_dot | 0.2403 | | spearman_dot | 0.2331 | | pearson_max | 0.663 | | spearman_max | 0.6571 | ## 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 - `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`: 3 - `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
Click to expand | 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 | |:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | 0.0229 | 200 | 21.5318 | - | - | - | - | - | | 0.0459 | 400 | 17.2344 | - | - | - | - | - | | 0.0688 | 600 | 15.393 | - | - | - | - | - | | 0.0918 | 800 | 13.7897 | - | - | - | - | - | | 0.1147 | 1000 | 13.534 | - | - | - | - | - | | 0.1377 | 1200 | 12.2683 | - | - | - | - | - | | 0.1606 | 1400 | 10.9271 | - | - | - | - | - | | 0.1835 | 1600 | 11.071 | - | - | - | - | - | | 0.2065 | 1800 | 10.0153 | - | - | - | - | - | | 0.2294 | 2000 | 9.8463 | - | - | - | - | - | | 0.2524 | 2200 | 10.0194 | - | - | - | - | - | | 0.2753 | 2400 | 9.8371 | - | - | - | - | - | | 0.2983 | 2600 | 9.6315 | - | - | - | - | - | | 0.3212 | 2800 | 8.9858 | - | - | - | - | - | | 0.3442 | 3000 | 9.1876 | - | - | - | - | - | | 0.3671 | 3200 | 8.8028 | - | - | - | - | - | | 0.3900 | 3400 | 8.6075 | - | - | - | - | - | | 0.4130 | 3600 | 8.4285 | - | - | - | - | - | | 0.4359 | 3800 | 8.1258 | - | - | - | - | - | | 0.4589 | 4000 | 8.2508 | - | - | - | - | - | | 0.4818 | 4200 | 7.8037 | - | - | - | - | - | | 0.5048 | 4400 | 7.7133 | - | - | - | - | - | | 0.5277 | 4600 | 7.5006 | - | - | - | - | - | | 0.5506 | 4800 | 7.7025 | - | - | - | - | - | | 0.5736 | 5000 | 7.7593 | - | - | - | - | - | | 0.5965 | 5200 | 7.6305 | - | - | - | - | - | | 0.6195 | 5400 | 7.7502 | - | - | - | - | - | | 0.6424 | 5600 | 7.5624 | - | - | - | - | - | | 0.6654 | 5800 | 7.5287 | - | - | - | - | - | | 0.6883 | 6000 | 7.4261 | - | - | - | - | - | | 0.7113 | 6200 | 7.239 | - | - | - | - | - | | 0.7342 | 6400 | 7.1631 | - | - | - | - | - | | 0.7571 | 6600 | 7.6865 | - | - | - | - | - | | 0.7801 | 6800 | 7.6124 | - | - | - | - | - | | 0.8030 | 7000 | 6.9936 | - | - | - | - | - | | 0.8260 | 7200 | 6.7331 | - | - | - | - | - | | 0.8489 | 7400 | 6.4542 | - | - | - | - | - | | 0.8719 | 7600 | 6.1994 | - | - | - | - | - | | 0.8948 | 7800 | 5.9798 | - | - | - | - | - | | 0.9177 | 8000 | 5.7808 | - | - | - | - | - | | 0.9407 | 8200 | 5.6952 | - | - | - | - | - | | 0.9636 | 8400 | 5.5082 | - | - | - | - | - | | 0.9866 | 8600 | 5.4421 | - | - | - | - | - | | 1.0095 | 8800 | 3.0309 | - | - | - | - | - | | 1.0026 | 9000 | 1.1835 | - | - | - | - | - | | 1.0256 | 9200 | 8.1196 | - | - | - | - | - | | 1.0485 | 9400 | 8.0326 | - | - | - | - | - | | 1.0715 | 9600 | 8.5028 | - | - | - | - | - | | 1.0944 | 9800 | 7.6923 | - | - | - | - | - | | 1.1174 | 10000 | 8.029 | - | - | - | - | - | | 1.1403 | 10200 | 7.5052 | - | - | - | - | - | | 1.1632 | 10400 | 7.1177 | - | - | - | - | - | | 1.1862 | 10600 | 6.9594 | - | - | - | - | - | | 1.2091 | 10800 | 6.6662 | - | - | - | - | - | | 1.2321 | 11000 | 6.6903 | - | - | - | - | - | | 1.2550 | 11200 | 6.9523 | - | - | - | - | - | | 1.2780 | 11400 | 6.676 | - | - | - | - | - | | 1.3009 | 11600 | 6.7141 | - | - | - | - | - | | 1.3238 | 11800 | 6.568 | - | - | - | - | - | | 1.3468 | 12000 | 6.8938 | - | - | - | - | - | | 1.3697 | 12200 | 6.3745 | - | - | - | - | - | | 1.3927 | 12400 | 6.2513 | - | - | - | - | - | | 1.4156 | 12600 | 6.2589 | - | - | - | - | - | | 1.4386 | 12800 | 6.1388 | - | - | - | - | - | | 1.4615 | 13000 | 6.1835 | - | - | - | - | - | | 1.4845 | 13200 | 5.9004 | - | - | - | - | - | | 1.5074 | 13400 | 5.7891 | - | - | - | - | - | | 1.5303 | 13600 | 5.6184 | - | - | - | - | - | | 1.5533 | 13800 | 5.9762 | - | - | - | - | - | | 1.5762 | 14000 | 5.9737 | - | - | - | - | - | | 1.5992 | 14200 | 5.8563 | - | - | - | - | - | | 1.6221 | 14400 | 5.8904 | - | - | - | - | - | | 1.6451 | 14600 | 5.8484 | - | - | - | - | - | | 1.6680 | 14800 | 5.8906 | - | - | - | - | - | | 1.6909 | 15000 | 5.7613 | - | - | - | - | - | | 1.7139 | 15200 | 5.5744 | - | - | - | - | - | | 1.7368 | 15400 | 5.6569 | - | - | - | - | - | | 1.7598 | 15600 | 5.7439 | - | - | - | - | - | | 1.7827 | 15800 | 5.5593 | - | - | - | - | - | | 1.8057 | 16000 | 5.2935 | - | - | - | - | - | | 1.8286 | 16200 | 5.088 | - | - | - | - | - | | 1.8516 | 16400 | 5.0167 | - | - | - | - | - | | 1.8745 | 16600 | 4.84 | - | - | - | - | - | | 1.8974 | 16800 | 4.6731 | - | - | - | - | - | | 1.9204 | 17000 | 4.6404 | - | - | - | - | - | | 1.9433 | 17200 | 4.6413 | - | - | - | - | - | | 1.9663 | 17400 | 4.4495 | - | - | - | - | - | | 1.9892 | 17600 | 4.4262 | - | - | - | - | - | | 2.0122 | 17800 | 2.01 | - | - | - | - | - | | 2.0053 | 18000 | 1.8418 | - | - | - | - | - | | 2.0282 | 18200 | 6.2714 | - | - | - | - | - | | 2.0512 | 18400 | 6.1742 | - | - | - | - | - | | 2.0741 | 18600 | 6.5996 | - | - | - | - | - | | 2.0971 | 18800 | 6.0907 | - | - | - | - | - | | 2.1200 | 19000 | 6.2418 | - | - | - | - | - | | 2.1429 | 19200 | 5.7817 | - | - | - | - | - | | 2.1659 | 19400 | 5.7073 | - | - | - | - | - | | 2.1888 | 19600 | 5.2645 | - | - | - | - | - | | 2.2118 | 19800 | 5.3451 | - | - | - | - | - | | 2.2347 | 20000 | 5.2453 | - | - | - | - | - | | 2.2577 | 20200 | 5.6161 | - | - | - | - | - | | 2.2806 | 20400 | 5.2289 | - | - | - | - | - | | 2.3035 | 20600 | 5.3888 | - | - | - | - | - | | 2.3265 | 20800 | 5.2483 | - | - | - | - | - | | 2.3494 | 21000 | 5.5791 | - | - | - | - | - | | 2.3724 | 21200 | 5.1643 | - | - | - | - | - | | 2.3953 | 21400 | 5.1231 | - | - | - | - | - | | 2.4183 | 21600 | 5.1055 | - | - | - | - | - | | 2.4412 | 21800 | 5.1778 | - | - | - | - | - | | 2.4642 | 22000 | 5.0466 | - | - | - | - | - | | 2.4871 | 22200 | 4.8321 | - | - | - | - | - | | 2.5100 | 22400 | 4.7056 | - | - | - | - | - | | 2.5330 | 22600 | 4.6858 | - | - | - | - | - | | 2.5559 | 22800 | 4.9189 | - | - | - | - | - | | 2.5789 | 23000 | 4.912 | - | - | - | - | - | | 2.6018 | 23200 | 4.8289 | - | - | - | - | - | | 2.6248 | 23400 | 4.8959 | - | - | - | - | - | | 2.6477 | 23600 | 4.9441 | - | - | - | - | - | | 2.6706 | 23800 | 4.9334 | - | - | - | - | - | | 2.6936 | 24000 | 4.8328 | - | - | - | - | - | | 2.7165 | 24200 | 4.601 | - | - | - | - | - | | 2.7395 | 24400 | 4.834 | - | - | - | - | - | | 2.7624 | 24600 | 5.152 | - | - | - | - | - | | 2.7854 | 24800 | 4.9232 | - | - | - | - | - | | 2.8083 | 25000 | 4.6556 | - | - | - | - | - | | 2.8312 | 25200 | 4.6229 | - | - | - | - | - | | 2.8542 | 25400 | 4.5768 | - | - | - | - | - | | 2.8771 | 25600 | 4.3619 | - | - | - | - | - | | 2.9001 | 25800 | 4.3608 | - | - | - | - | - | | 2.9230 | 26000 | 4.2834 | - | - | - | - | - | | 2.9403 | 26151 | - | 0.6355 | 0.6513 | 0.6703 | 0.6081 | 0.6757 |
### 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} } ```