--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 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: Mwanamume aliyepangwa vizuri anasimama kwa mguu mmoja karibu na pwani safi ya bahari. sentences: - mtu anacheka wakati wa kufua nguo - Mwanamume fulani yuko nje karibu na ufuo wa bahari. - Mwanamume fulani ameketi kwenye sofa yake. - source_sentence: Mwanamume mwenye ngozi nyeusi akivuta sigareti karibu na chombo cha taka cha kijani. sentences: - Karibu na chombo cha taka mwanamume huyo alisimama na kuvuta sigareti - Kitanda ni chafu. - Alipokuwa kwenye dimbwi la kuogelea mvulana huyo mwenye ugonjwa wa albino alijihadhari na jua kupita kiasi - source_sentence: Mwanamume kijana mwenye nywele nyekundu anaketi ukutani akisoma gazeti huku mwanamke na msichana mchanga wakipita. sentences: - Mwanamume aliyevalia shati la bluu amegonga ukuta kando ya barabara na gari la bluu na gari nyekundu lenye maji nyuma. - Mwanamume mchanga anatazama gazeti huku wanawake wawili wakipita karibu naye. - Mwanamume huyo mchanga analala huku Mama akimwongoza binti yake kwenye bustani. - source_sentence: Wasichana wako nje. sentences: - Wasichana wawili wakisafiri kwenye sehemu ya kusisimua. - Kuna watu watatu wakiongoza gari linaloweza kugeuzwa-geuzwa wakipita watu wengine. - Wasichana watatu wamesimama pamoja katika chumba, mmoja anasikiliza, mwingine anaandika ukutani na wa tatu anaongea nao. - source_sentence: Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi. sentences: - Mwanamume amelala uso chini kwenye benchi ya bustani. - Mwanamke anaunganisha uzi katika mipira kando ya rundo la mipira - Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa. pipeline_tag: sentence-similarity model-index: - 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: 0.7073072916945755 name: Pearson Cosine - type: spearman_cosine value: 0.7038302407401268 name: Spearman Cosine - type: pearson_manhattan value: 0.6971720016143421 name: Pearson Manhattan - type: spearman_manhattan value: 0.6937589389074208 name: Spearman Manhattan - type: pearson_euclidean value: 0.6995677027369716 name: Pearson Euclidean - type: spearman_euclidean value: 0.6964739266023146 name: Spearman Euclidean - type: pearson_dot value: 0.610950397941133 name: Pearson Dot - type: spearman_dot value: 0.593970670155461 name: Spearman Dot - type: pearson_max value: 0.7073072916945755 name: Pearson Max - type: spearman_max value: 0.7038302407401268 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.7045540268598433 name: Pearson Cosine - type: spearman_cosine value: 0.7023621139637947 name: Spearman Cosine - type: pearson_manhattan value: 0.6975397258794529 name: Pearson Manhattan - type: spearman_manhattan value: 0.6927560109749419 name: Spearman Manhattan - type: pearson_euclidean value: 0.6985549032977664 name: Pearson Euclidean - type: spearman_euclidean value: 0.6941789537125014 name: Spearman Euclidean - type: pearson_dot value: 0.582046017636523 name: Pearson Dot - type: spearman_dot value: 0.565355081915806 name: Spearman Dot - type: pearson_max value: 0.7045540268598433 name: Pearson Max - type: spearman_max value: 0.7023621139637947 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.7012922000956245 name: Pearson Cosine - type: spearman_cosine value: 0.7016107934280537 name: Spearman Cosine - type: pearson_manhattan value: 0.69508092429561 name: Pearson Manhattan - type: spearman_manhattan value: 0.6879849400335534 name: Spearman Manhattan - type: pearson_euclidean value: 0.6956451936598814 name: Pearson Euclidean - type: spearman_euclidean value: 0.6890266593479353 name: Spearman Euclidean - type: pearson_dot value: 0.5501625376986127 name: Pearson Dot - type: spearman_dot value: 0.5332005894675337 name: Spearman Dot - type: pearson_max value: 0.7012922000956245 name: Pearson Max - type: spearman_max value: 0.7016107934280537 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.6980581280594836 name: Pearson Cosine - type: spearman_cosine value: 0.7000311940508227 name: Spearman Cosine - type: pearson_manhattan value: 0.6910651227829323 name: Pearson Manhattan - type: spearman_manhattan value: 0.6823572623875095 name: Spearman Manhattan - type: pearson_euclidean value: 0.6922658508243149 name: Pearson Euclidean - type: spearman_euclidean value: 0.6838439746630024 name: Spearman Euclidean - type: pearson_dot value: 0.5156063038618797 name: Pearson Dot - type: spearman_dot value: 0.5006742054178095 name: Spearman Dot - type: pearson_max value: 0.6980581280594836 name: Pearson Max - type: spearman_max value: 0.7000311940508227 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.6865218902262467 name: Pearson Cosine - type: spearman_cosine value: 0.6901005120722546 name: Spearman Cosine - type: pearson_manhattan value: 0.681113101036276 name: Pearson Manhattan - type: spearman_manhattan value: 0.6713556700583071 name: Spearman Manhattan - type: pearson_euclidean value: 0.6816926483485075 name: Pearson Euclidean - type: spearman_euclidean value: 0.6701163777153826 name: Spearman Euclidean - type: pearson_dot value: 0.4686654293362901 name: Pearson Dot - type: spearman_dot value: 0.4520420017929889 name: Spearman Dot - type: pearson_max value: 0.6865218902262467 name: Pearson Max - type: spearman_max value: 0.6901005120722546 name: Spearman Max --- # 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). 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 ### 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("sartifyllc/swahili-paraphrase-multilingual-mpnet-base-v2-nli-matryoshka") # Run inference sentences = [ 'Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi.', 'Mwanamume amelala uso chini kwenye benchi ya bustani.', 'Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.', ] 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 | 0.7073 | | **spearman_cosine** | **0.7038** | | pearson_manhattan | 0.6972 | | spearman_manhattan | 0.6938 | | pearson_euclidean | 0.6996 | | spearman_euclidean | 0.6965 | | pearson_dot | 0.611 | | spearman_dot | 0.594 | | pearson_max | 0.7073 | | spearman_max | 0.7038 | #### 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 | 0.7046 | | **spearman_cosine** | **0.7024** | | pearson_manhattan | 0.6975 | | spearman_manhattan | 0.6928 | | pearson_euclidean | 0.6986 | | spearman_euclidean | 0.6942 | | pearson_dot | 0.582 | | spearman_dot | 0.5654 | | pearson_max | 0.7046 | | spearman_max | 0.7024 | #### 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 | 0.7013 | | **spearman_cosine** | **0.7016** | | pearson_manhattan | 0.6951 | | spearman_manhattan | 0.688 | | pearson_euclidean | 0.6956 | | spearman_euclidean | 0.689 | | pearson_dot | 0.5502 | | spearman_dot | 0.5332 | | pearson_max | 0.7013 | | spearman_max | 0.7016 | #### 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 | 0.6981 | | **spearman_cosine** | **0.7** | | pearson_manhattan | 0.6911 | | spearman_manhattan | 0.6824 | | pearson_euclidean | 0.6923 | | spearman_euclidean | 0.6838 | | pearson_dot | 0.5156 | | spearman_dot | 0.5007 | | pearson_max | 0.6981 | | spearman_max | 0.7 | #### 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 | 0.6865 | | **spearman_cosine** | **0.6901** | | pearson_manhattan | 0.6811 | | spearman_manhattan | 0.6714 | | pearson_euclidean | 0.6817 | | spearman_euclidean | 0.6701 | | pearson_dot | 0.4687 | | spearman_dot | 0.452 | | pearson_max | 0.6865 | | spearman_max | 0.6901 | ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 16 - `per_device_eval_batch_size`: 16 - `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
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 | |:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | 0.0057 | 100 | 18.7677 | - | - | - | - | - | | 0.0115 | 200 | 10.5065 | - | - | - | - | - | | 0.0172 | 300 | 8.0917 | - | - | - | - | - | | 0.0229 | 400 | 8.4617 | - | - | - | - | - | | 0.0287 | 500 | 8.0789 | - | - | - | - | - | | 0.0344 | 600 | 7.3287 | - | - | - | - | - | | 0.0402 | 700 | 6.3282 | - | - | - | - | - | | 0.0459 | 800 | 5.5327 | - | - | - | - | - | | 0.0516 | 900 | 5.7985 | - | - | - | - | - | | 0.0574 | 1000 | 6.1129 | - | - | - | - | - | | 0.0631 | 1100 | 6.1784 | - | - | - | - | - | | 0.0688 | 1200 | 6.3647 | - | - | - | - | - | | 0.0746 | 1300 | 7.4443 | - | - | - | - | - | | 0.0803 | 1400 | 6.6881 | - | - | - | - | - | | 0.0860 | 1500 | 6.09 | - | - | - | - | - | | 0.0918 | 1600 | 5.4176 | - | - | - | - | - | | 0.0975 | 1700 | 5.4563 | - | - | - | - | - | | 0.1033 | 1800 | 5.7071 | - | - | - | - | - | | 0.1090 | 1900 | 7.0201 | - | - | - | - | - | | 0.1147 | 2000 | 6.2688 | - | - | - | - | - | | 0.1205 | 2100 | 6.1499 | - | - | - | - | - | | 0.1262 | 2200 | 5.947 | - | - | - | - | - | | 0.1319 | 2300 | 5.5437 | - | - | - | - | - | | 0.1377 | 2400 | 5.4958 | - | - | - | - | - | | 0.1434 | 2500 | 5.5032 | - | - | - | - | - | | 0.1491 | 2600 | 4.8026 | - | - | - | - | - | | 0.1549 | 2700 | 5.0879 | - | - | - | - | - | | 0.1606 | 2800 | 5.6166 | - | - | - | - | - | | 0.1664 | 2900 | 5.8146 | - | - | - | - | - | | 0.1721 | 3000 | 6.4168 | - | - | - | - | - | | 0.1778 | 3100 | 6.5094 | - | - | - | - | - | | 0.1836 | 3200 | 5.9273 | - | - | - | - | - | | 0.1893 | 3300 | 5.6202 | - | - | - | - | - | | 0.1950 | 3400 | 5.1419 | - | - | - | - | - | | 0.2008 | 3500 | 5.9303 | - | - | - | - | - | | 0.2065 | 3600 | 5.3225 | - | - | - | - | - | | 0.2122 | 3700 | 5.5183 | - | - | - | - | - | | 0.2180 | 3800 | 5.6644 | - | - | - | - | - | | 0.2237 | 3900 | 6.2006 | - | - | - | - | - | | 0.2294 | 4000 | 5.8684 | - | - | - | - | - | | 0.2352 | 4100 | 5.5406 | - | - | - | - | - | | 0.2409 | 4200 | 5.1763 | - | - | - | - | - | | 0.2467 | 4300 | 5.7639 | - | - | - | - | - | | 0.2524 | 4400 | 5.8734 | - | - | - | - | - | | 0.2581 | 4500 | 6.0215 | - | - | - | - | - | | 0.2639 | 4600 | 5.5183 | - | - | - | - | - | | 0.2696 | 4700 | 5.5938 | - | - | - | - | - | | 0.2753 | 4800 | 5.6869 | - | - | - | - | - | | 0.2811 | 4900 | 5.1235 | - | - | - | - | - | | 0.2868 | 5000 | 5.189 | - | - | - | - | - | | 0.2925 | 5100 | 5.081 | - | - | - | - | - | | 0.2983 | 5200 | 5.4992 | - | - | - | - | - | | 0.3040 | 5300 | 5.6662 | - | - | - | - | - | | 0.3098 | 5400 | 5.5772 | - | - | - | - | - | | 0.3155 | 5500 | 5.3595 | - | - | - | - | - | | 0.3212 | 5600 | 4.805 | - | - | - | - | - | | 0.3270 | 5700 | 5.1821 | - | - | - | - | - | | 0.3327 | 5800 | 5.3221 | - | - | - | - | - | | 0.3384 | 5900 | 5.4223 | - | - | - | - | - | | 0.3442 | 6000 | 5.2718 | - | - | - | - | - | | 0.3499 | 6100 | 5.2213 | - | - | - | - | - | | 0.3556 | 6200 | 5.5453 | - | - | - | - | - | | 0.3614 | 6300 | 4.8502 | - | - | - | - | - | | 0.3671 | 6400 | 4.8912 | - | - | - | - | - | | 0.3729 | 6500 | 4.8791 | - | - | - | - | - | | 0.3786 | 6600 | 5.2418 | - | - | - | - | - | | 0.3843 | 6700 | 4.7621 | - | - | - | - | - | | 0.3901 | 6800 | 4.9017 | - | - | - | - | - | | 0.3958 | 6900 | 4.8965 | - | - | - | - | - | | 0.4015 | 7000 | 4.6081 | - | - | - | - | - | | 0.4073 | 7100 | 5.4256 | - | - | - | - | - | | 0.4130 | 7200 | 5.0878 | - | - | - | - | - | | 0.4187 | 7300 | 4.9899 | - | - | - | - | - | | 0.4245 | 7400 | 4.8508 | - | - | - | - | - | | 0.4302 | 7500 | 5.253 | - | - | - | - | - | | 0.4360 | 7600 | 4.8363 | - | - | - | - | - | | 0.4417 | 7700 | 4.5555 | - | - | - | - | - | | 0.4474 | 7800 | 4.9668 | - | - | - | - | - | | 0.4532 | 7900 | 5.1911 | - | - | - | - | - | | 0.4589 | 8000 | 4.468 | - | - | - | - | - | | 0.4646 | 8100 | 4.8253 | - | - | - | - | - | | 0.4704 | 8200 | 4.89 | - | - | - | - | - | | 0.4761 | 8300 | 4.5547 | - | - | - | - | - | | 0.4818 | 8400 | 4.9499 | - | - | - | - | - | | 0.4876 | 8500 | 4.777 | - | - | - | - | - | | 0.4933 | 8600 | 4.8066 | - | - | - | - | - | | 0.4991 | 8700 | 5.0615 | - | - | - | - | - | | 0.5048 | 8800 | 4.9215 | - | - | - | - | - | | 0.5105 | 8900 | 4.8484 | - | - | - | - | - | | 0.5163 | 9000 | 4.6272 | - | - | - | - | - | | 0.5220 | 9100 | 4.8225 | - | - | - | - | - | | 0.5277 | 9200 | 4.7131 | - | - | - | - | - | | 0.5335 | 9300 | 4.3969 | - | - | - | - | - | | 0.5392 | 9400 | 4.4143 | - | - | - | - | - | | 0.5449 | 9500 | 4.9588 | - | - | - | - | - | | 0.5507 | 9600 | 4.7358 | - | - | - | - | - | | 0.5564 | 9700 | 5.0527 | - | - | - | - | - | | 0.5622 | 9800 | 4.852 | - | - | - | - | - | | 0.5679 | 9900 | 5.0855 | - | - | - | - | - | | 0.5736 | 10000 | 4.8507 | - | - | - | - | - | | 0.5794 | 10100 | 4.8007 | - | - | - | - | - | | 0.5851 | 10200 | 4.7279 | - | - | - | - | - | | 0.5908 | 10300 | 5.0171 | - | - | - | - | - | | 0.5966 | 10400 | 4.5288 | - | - | - | - | - | | 0.6023 | 10500 | 4.4488 | - | - | - | - | - | | 0.6080 | 10600 | 4.6557 | - | - | - | - | - | | 0.6138 | 10700 | 4.6881 | - | - | - | - | - | | 0.6195 | 10800 | 5.0514 | - | - | - | - | - | | 0.6253 | 10900 | 4.6301 | - | - | - | - | - | | 0.6310 | 11000 | 4.8233 | - | - | - | - | - | | 0.6367 | 11100 | 5.0136 | - | - | - | - | - | | 0.6425 | 11200 | 4.3774 | - | - | - | - | - | | 0.6482 | 11300 | 5.1213 | - | - | - | - | - | | 0.6539 | 11400 | 4.528 | - | - | - | - | - | | 0.6597 | 11500 | 4.8555 | - | - | - | - | - | | 0.6654 | 11600 | 4.2198 | - | - | - | - | - | | 0.6711 | 11700 | 5.0931 | - | - | - | - | - | | 0.6769 | 11800 | 4.9511 | - | - | - | - | - | | 0.6826 | 11900 | 4.5414 | - | - | - | - | - | | 0.6883 | 12000 | 4.5039 | - | - | - | - | - | | 0.6941 | 12100 | 4.8238 | - | - | - | - | - | | 0.6998 | 12200 | 4.6237 | - | - | - | - | - | | 0.7056 | 12300 | 4.6771 | - | - | - | - | - | | 0.7113 | 12400 | 4.6187 | - | - | - | - | - | | 0.7170 | 12500 | 4.4485 | - | - | - | - | - | | 0.7228 | 12600 | 4.2029 | - | - | - | - | - | | 0.7285 | 12700 | 4.5829 | - | - | - | - | - | | 0.7342 | 12800 | 4.617 | - | - | - | - | - | | 0.7400 | 12900 | 4.5606 | - | - | - | - | - | | 0.7457 | 13000 | 4.5745 | - | - | - | - | - | | 0.7514 | 13100 | 4.1457 | - | - | - | - | - | | 0.7572 | 13200 | 7.2499 | - | - | - | - | - | | 0.7629 | 13300 | 6.3681 | - | - | - | - | - | | 0.7687 | 13400 | 6.2052 | - | - | - | - | - | | 0.7744 | 13500 | 5.9569 | - | - | - | - | - | | 0.7801 | 13600 | 5.2649 | - | - | - | - | - | | 0.7859 | 13700 | 5.5198 | - | - | - | - | - | | 0.7916 | 13800 | 5.2808 | - | - | - | - | - | | 0.7973 | 13900 | 5.1534 | - | - | - | - | - | | 0.8031 | 14000 | 4.7831 | - | - | - | - | - | | 0.8088 | 14100 | 4.5975 | - | - | - | - | - | | 0.8145 | 14200 | 4.6134 | - | - | - | - | - | | 0.8203 | 14300 | 4.5497 | - | - | - | - | - | | 0.8260 | 14400 | 4.6003 | - | - | - | - | - | | 0.8318 | 14500 | 4.7011 | - | - | - | - | - | | 0.8375 | 14600 | 4.4208 | - | - | - | - | - | | 0.8432 | 14700 | 4.4052 | - | - | - | - | - | | 0.8490 | 14800 | 4.1121 | - | - | - | - | - | | 0.8547 | 14900 | 4.2418 | - | - | - | - | - | | 0.8604 | 15000 | 4.2314 | - | - | - | - | - | | 0.8662 | 15100 | 3.8679 | - | - | - | - | - | | 0.8719 | 15200 | 4.0173 | - | - | - | - | - | | 0.8776 | 15300 | 4.0758 | - | - | - | - | - | | 0.8834 | 15400 | 3.8581 | - | - | - | - | - | | 0.8891 | 15500 | 4.0601 | - | - | - | - | - | | 0.8949 | 15600 | 3.8738 | - | - | - | - | - | | 0.9006 | 15700 | 4.0744 | - | - | - | - | - | | 0.9063 | 15800 | 3.917 | - | - | - | - | - | | 0.9121 | 15900 | 3.7996 | - | - | - | - | - | | 0.9178 | 16000 | 3.7511 | - | - | - | - | - | | 0.9235 | 16100 | 3.7654 | - | - | - | - | - | | 0.9293 | 16200 | 3.6185 | - | - | - | - | - | | 0.9350 | 16300 | 3.5877 | - | - | - | - | - | | 0.9407 | 16400 | 3.8974 | - | - | - | - | - | | 0.9465 | 16500 | 3.5654 | - | - | - | - | - | | 0.9522 | 16600 | 3.6 | - | - | - | - | - | | 0.9580 | 16700 | 3.6468 | - | - | - | - | - | | 0.9637 | 16800 | 3.7221 | - | - | - | - | - | | 0.9694 | 16900 | 3.5939 | - | - | - | - | - | | 0.9752 | 17000 | 3.8597 | - | - | - | - | - | | 0.9809 | 17100 | 3.6323 | - | - | - | - | - | | 0.9866 | 17200 | 3.5251 | - | - | - | - | - | | 0.9924 | 17300 | 3.6949 | - | - | - | - | - | | 0.9981 | 17400 | 3.5682 | - | - | - | - | - | | 1.0 | 17433 | - | 0.7000 | 0.7016 | 0.7024 | 0.6901 | 0.7038 |
### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.0.1 - Transformers: 4.40.1 - PyTorch: 2.3.0+cu121 - Accelerate: 0.29.3 - 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} } ```