--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: intfloat/multilingual-e5-small 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 intfloat/multilingual-e5-small results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 384 type: sts-test-384 metrics: - type: pearson_cosine value: 0.7883137447514015 name: Pearson Cosine - type: spearman_cosine value: 0.7971624317482785 name: Spearman Cosine - type: pearson_manhattan value: 0.7845904338398069 name: Pearson Manhattan - type: spearman_manhattan value: 0.7939541836133244 name: Spearman Manhattan - type: pearson_euclidean value: 0.7882887522003604 name: Pearson Euclidean - type: spearman_euclidean value: 0.7971601260546269 name: Spearman Euclidean - type: pearson_dot value: 0.7883137483129774 name: Pearson Dot - type: spearman_dot value: 0.7971605875966696 name: Spearman Dot - type: pearson_max value: 0.7883137483129774 name: Pearson Max - type: spearman_max value: 0.7971624317482785 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.7851969391652749 name: Pearson Cosine - type: spearman_cosine value: 0.7968026743946358 name: Spearman Cosine - type: pearson_manhattan value: 0.7852783784725356 name: Pearson Manhattan - type: spearman_manhattan value: 0.7935883492889713 name: Spearman Manhattan - type: pearson_euclidean value: 0.7882018230746569 name: Pearson Euclidean - type: spearman_euclidean value: 0.7963116553267949 name: Spearman Euclidean - type: pearson_dot value: 0.7786421988393841 name: Pearson Dot - type: spearman_dot value: 0.7867782644180616 name: Spearman Dot - type: pearson_max value: 0.7882018230746569 name: Pearson Max - type: spearman_max value: 0.7968026743946358 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.7754967709350954 name: Pearson Cosine - type: spearman_cosine value: 0.7933453885370457 name: Spearman Cosine - type: pearson_manhattan value: 0.7832834632297865 name: Pearson Manhattan - type: spearman_manhattan value: 0.7907589269176767 name: Spearman Manhattan - type: pearson_euclidean value: 0.7867583047946054 name: Pearson Euclidean - type: spearman_euclidean value: 0.7935816990844704 name: Spearman Euclidean - type: pearson_dot value: 0.7317253736607925 name: Pearson Dot - type: spearman_dot value: 0.7335574962775742 name: Spearman Dot - type: pearson_max value: 0.7867583047946054 name: Pearson Max - type: spearman_max value: 0.7935816990844704 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.7625204599039478 name: Pearson Cosine - type: spearman_cosine value: 0.7837078735068292 name: Spearman Cosine - type: pearson_manhattan value: 0.7752889433866854 name: Pearson Manhattan - type: spearman_manhattan value: 0.7790888579029828 name: Spearman Manhattan - type: pearson_euclidean value: 0.777961287133872 name: Pearson Euclidean - type: spearman_euclidean value: 0.7815940757356076 name: Spearman Euclidean - type: pearson_dot value: 0.6685094830550401 name: Pearson Dot - type: spearman_dot value: 0.6621206899696827 name: Spearman Dot - type: pearson_max value: 0.777961287133872 name: Pearson Max - type: spearman_max value: 0.7837078735068292 name: Spearman Max --- # SentenceTransformer based on intfloat/multilingual-e5-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co./intfloat/multilingual-e5-small) 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:** [intfloat/multilingual-e5-small](https://huggingface.co./intfloat/multilingual-e5-small) - **Maximum Sequence Length:** 512 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': 512, '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}) (2): 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/E5-Matro") # 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-384` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7883 | | **spearman_cosine** | **0.7972** | | pearson_manhattan | 0.7846 | | spearman_manhattan | 0.794 | | pearson_euclidean | 0.7883 | | spearman_euclidean | 0.7972 | | pearson_dot | 0.7883 | | spearman_dot | 0.7972 | | pearson_max | 0.7883 | | spearman_max | 0.7972 | #### 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 | 0.7852 | | **spearman_cosine** | **0.7968** | | pearson_manhattan | 0.7853 | | spearman_manhattan | 0.7936 | | pearson_euclidean | 0.7882 | | spearman_euclidean | 0.7963 | | pearson_dot | 0.7786 | | spearman_dot | 0.7868 | | pearson_max | 0.7882 | | spearman_max | 0.7968 | #### 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 | 0.7755 | | **spearman_cosine** | **0.7933** | | pearson_manhattan | 0.7833 | | spearman_manhattan | 0.7908 | | pearson_euclidean | 0.7868 | | spearman_euclidean | 0.7936 | | pearson_dot | 0.7317 | | spearman_dot | 0.7336 | | pearson_max | 0.7868 | | spearman_max | 0.7936 | #### 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 | 0.7625 | | **spearman_cosine** | **0.7837** | | pearson_manhattan | 0.7753 | | spearman_manhattan | 0.7791 | | pearson_euclidean | 0.778 | | spearman_euclidean | 0.7816 | | pearson_dot | 0.6685 | | spearman_dot | 0.6621 | | pearson_max | 0.778 | | spearman_max | 0.7837 | ## 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": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 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": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: 32 - `per_device_eval_batch_size`: 32 - `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 | Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-384_spearman_cosine | sts-test-64_spearman_cosine | |:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:| | 0.0344 | 200 | 13.1208 | - | - | - | - | | 0.0688 | 400 | 9.1894 | - | - | - | - | | 0.1033 | 600 | 8.0222 | - | - | - | - | | 0.1377 | 800 | 7.2405 | - | - | - | - | | 0.1721 | 1000 | 7.1622 | - | - | - | - | | 0.2065 | 1200 | 6.4282 | - | - | - | - | | 0.2409 | 1400 | 6.0936 | - | - | - | - | | 0.2753 | 1600 | 5.99 | - | - | - | - | | 0.3098 | 1800 | 5.6939 | - | - | - | - | | 0.3442 | 2000 | 5.694 | - | - | - | - | | 0.3786 | 2200 | 5.2366 | - | - | - | - | | 0.4130 | 2400 | 5.2994 | - | - | - | - | | 0.4474 | 2600 | 5.2079 | - | - | - | - | | 0.4818 | 2800 | 5.0532 | - | - | - | - | | 0.5163 | 3000 | 4.9978 | - | - | - | - | | 0.5507 | 3200 | 5.1764 | - | - | - | - | | 0.5851 | 3400 | 5.1315 | - | - | - | - | | 0.6195 | 3600 | 5.0198 | - | - | - | - | | 0.6539 | 3800 | 5.0308 | - | - | - | - | | 0.6883 | 4000 | 5.1631 | - | - | - | - | | 0.7228 | 4200 | 4.7916 | - | - | - | - | | 0.7572 | 4400 | 4.363 | - | - | - | - | | 0.7916 | 4600 | 3.2357 | - | - | - | - | | 0.8260 | 4800 | 2.9915 | - | - | - | - | | 0.8604 | 5000 | 2.8143 | - | - | - | - | | 0.8949 | 5200 | 2.6125 | - | - | - | - | | 0.9293 | 5400 | 2.5493 | - | - | - | - | | 0.9637 | 5600 | 2.4991 | - | - | - | - | | 0.9981 | 5800 | 2.163 | - | - | - | - | | 1.0325 | 6000 | 0.0 | - | - | - | - | | 1.0669 | 6200 | 0.0 | - | - | - | - | | 1.1014 | 6400 | 0.0 | - | - | - | - | | 1.1358 | 6600 | 0.0 | - | - | - | - | | 1.1702 | 6800 | 0.0 | - | - | - | - | | 1.2046 | 7000 | 0.0 | - | - | - | - | | 1.2390 | 7200 | 0.0 | - | - | - | - | | 1.2734 | 7400 | 0.0 | - | - | - | - | | 1.3079 | 7600 | 0.0 | - | - | - | - | | 1.3423 | 7800 | 0.0 | - | - | - | - | | 1.3767 | 8000 | 0.0 | - | - | - | - | | 1.4111 | 8200 | 0.0037 | - | - | - | - | | 1.4455 | 8400 | 0.0372 | - | - | - | - | | 1.4800 | 8600 | 0.0221 | - | - | - | - | | 1.0229 | 8800 | 4.3738 | - | - | - | - | | 1.0573 | 9000 | 6.338 | - | - | - | - | | 1.0917 | 9200 | 6.2223 | - | - | - | - | | 1.1261 | 9400 | 5.8673 | - | - | - | - | | 1.1606 | 9600 | 5.5907 | - | - | - | - | | 1.1950 | 9800 | 5.0307 | - | - | - | - | | 1.2294 | 10000 | 4.9193 | - | - | - | - | | 1.2638 | 10200 | 4.8798 | - | - | - | - | | 1.2982 | 10400 | 4.401 | - | - | - | - | | 1.3326 | 10600 | 4.2705 | - | - | - | - | | 1.3671 | 10800 | 4.3023 | - | - | - | - | | 1.4015 | 11000 | 4.1344 | - | - | - | - | | 1.4359 | 11200 | 4.0464 | - | - | - | - | | 1.4703 | 11400 | 4.0115 | - | - | - | - | | 1.5047 | 11600 | 3.9206 | - | - | - | - | | 1.5391 | 11800 | 4.0106 | - | - | - | - | | 1.5736 | 12000 | 4.1365 | - | - | - | - | | 1.6080 | 12200 | 4.0401 | - | - | - | - | | 1.6424 | 12400 | 4.0602 | - | - | - | - | | 1.6768 | 12600 | 4.076 | - | - | - | - | | 1.7112 | 12800 | 3.97 | - | - | - | - | | 1.7457 | 13000 | 3.7905 | - | - | - | - | | 1.7801 | 13200 | 2.414 | - | - | - | - | | 1.8145 | 13400 | 2.1811 | - | - | - | - | | 1.8489 | 13600 | 2.1183 | - | - | - | - | | 1.8833 | 13800 | 2.0578 | - | - | - | - | | 1.9177 | 14000 | 2.0173 | - | - | - | - | | 1.9522 | 14200 | 2.0093 | - | - | - | - | | 1.9866 | 14400 | 1.9467 | - | - | - | - | | 2.0210 | 14600 | 0.4674 | - | - | - | - | | 2.0554 | 14800 | 0.0 | - | - | - | - | | 2.0898 | 15000 | 0.0 | - | - | - | - | | 2.1242 | 15200 | 0.0 | - | - | - | - | | 2.1587 | 15400 | 0.0 | - | - | - | - | | 2.1931 | 15600 | 0.0 | - | - | - | - | | 2.2275 | 15800 | 0.0 | - | - | - | - | | 2.2619 | 16000 | 0.0 | - | - | - | - | | 2.2963 | 16200 | 0.0 | - | - | - | - | | 2.3308 | 16400 | 0.0 | - | - | - | - | | 2.3652 | 16600 | 0.0 | - | - | - | - | | 2.3996 | 16800 | 0.0 | - | - | - | - | | 2.4340 | 17000 | 0.0 | - | - | - | - | | 2.4684 | 17200 | 0.0256 | - | - | - | - | | 2.0114 | 17400 | 2.4155 | - | - | - | - | | 2.0170 | 17433 | - | 0.7933 | 0.7968 | 0.7972 | 0.7837 | ### 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} } ```