--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1115700 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: nomic-ai/nomic-embed-text-v1.5 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: Ndege mwenye mdomo mrefu katikati ya ndege. sentences: - Panya anayekimbia juu ya gurudumu. - Mtu anashindana katika mashindano ya mbio. - Ndege anayeruka. - source_sentence: Msichana mchanga mwenye nywele nyeusi anakabili kamera na kushikilia mfuko wa karatasi wakati amevaa shati la machungwa na mabawa ya kipepeo yenye rangi nyingi. sentences: - Mwanamke mzee anakataa kupigwa picha. - mtu akila na mvulana mdogo kwenye kijia cha jiji - Msichana mchanga anakabili kamera. - source_sentence: Wanawake na watoto wameketi nje katika kivuli wakati kikundi cha watoto wadogo wameketi ndani katika kivuli. sentences: - Mwanamke na watoto na kukaa chini. - Mwanamke huyo anakimbia. - Watu wanasafiri kwa baiskeli. - source_sentence: Mtoto mdogo anaruka mikononi mwa mwanamke aliyevalia suti nyeusi ya kuogelea akiwa kwenye dimbwi. sentences: - Mtoto akiruka mikononi mwa mwanamke aliyevalia suti ya kuogelea kwenye dimbwi. - Someone is holding oranges and walking - Mama na binti wakinunua viatu. - source_sentence: Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma. sentences: - tai huruka - mwanamume na mwanamke wenye mikoba - Wanaume wawili wameketi karibu na mwanamke. pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: 0.6944960057464138 name: Pearson Cosine - type: spearman_cosine value: 0.6872396378196957 name: Spearman Cosine - type: pearson_manhattan value: 0.7086043588614903 name: Pearson Manhattan - type: spearman_manhattan value: 0.7136479613274518 name: Spearman Manhattan - type: pearson_euclidean value: 0.7084460037709435 name: Pearson Euclidean - type: spearman_euclidean value: 0.7128357831285198 name: Spearman Euclidean - type: pearson_dot value: 0.481902874304561 name: Pearson Dot - type: spearman_dot value: 0.46588918379526945 name: Spearman Dot - type: pearson_max value: 0.7086043588614903 name: Pearson Max - type: spearman_max value: 0.7136479613274518 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.6925787246105148 name: Pearson Cosine - type: spearman_cosine value: 0.6859479129419207 name: Spearman Cosine - type: pearson_manhattan value: 0.7087290093387656 name: Pearson Manhattan - type: spearman_manhattan value: 0.7127968133455542 name: Spearman Manhattan - type: pearson_euclidean value: 0.7088805484816247 name: Pearson Euclidean - type: spearman_euclidean value: 0.7123606046721803 name: Spearman Euclidean - type: pearson_dot value: 0.4684333245586192 name: Pearson Dot - type: spearman_dot value: 0.45257836578849003 name: Spearman Dot - type: pearson_max value: 0.7088805484816247 name: Pearson Max - type: spearman_max value: 0.7127968133455542 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.6876956481856266 name: Pearson Cosine - type: spearman_cosine value: 0.6814892249857147 name: Spearman Cosine - type: pearson_manhattan value: 0.7083882582081078 name: Pearson Manhattan - type: spearman_manhattan value: 0.7097524143994903 name: Spearman Manhattan - type: pearson_euclidean value: 0.7094190252305796 name: Pearson Euclidean - type: spearman_euclidean value: 0.7104287347206688 name: Spearman Euclidean - type: pearson_dot value: 0.4438925722484721 name: Pearson Dot - type: spearman_dot value: 0.4255299982188107 name: Spearman Dot - type: pearson_max value: 0.7094190252305796 name: Pearson Max - type: spearman_max value: 0.7104287347206688 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.6708560165075523 name: Pearson Cosine - type: spearman_cosine value: 0.6669935075512006 name: Spearman Cosine - type: pearson_manhattan value: 0.7041961281711793 name: Pearson Manhattan - type: spearman_manhattan value: 0.7000807688296651 name: Spearman Manhattan - type: pearson_euclidean value: 0.7055061381768357 name: Pearson Euclidean - type: spearman_euclidean value: 0.7022686907818495 name: Spearman Euclidean - type: pearson_dot value: 0.37855771167572094 name: Pearson Dot - type: spearman_dot value: 0.35930717422088765 name: Spearman Dot - type: pearson_max value: 0.7055061381768357 name: Pearson Max - type: spearman_max value: 0.7022686907818495 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.6533817775144477 name: Pearson Cosine - type: spearman_cosine value: 0.6523997361414113 name: Spearman Cosine - type: pearson_manhattan value: 0.6919834348567717 name: Pearson Manhattan - type: spearman_manhattan value: 0.6857245312336051 name: Spearman Manhattan - type: pearson_euclidean value: 0.6950438027503257 name: Pearson Euclidean - type: spearman_euclidean value: 0.6899151458827059 name: Spearman Euclidean - type: pearson_dot value: 0.33502302384042637 name: Pearson Dot - type: spearman_dot value: 0.3097469345046609 name: Spearman Dot - type: pearson_max value: 0.6950438027503257 name: Pearson Max - type: spearman_max value: 0.6899151458827059 name: Spearman Max --- # SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) on the Mollel/swahili-n_li-triplet-swh-eng 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - Mollel/swahili-n_li-triplet-swh-eng ### 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel (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("Mollel/MultiLinguSwahili-nomic-embed-text-v1.5-nli-matryoshka") # Run inference sentences = [ 'Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma.', 'mwanamume na mwanamke wenye mikoba', 'tai huruka', ] 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 | 0.6945 | | **spearman_cosine** | **0.6872** | | pearson_manhattan | 0.7086 | | spearman_manhattan | 0.7136 | | pearson_euclidean | 0.7084 | | spearman_euclidean | 0.7128 | | pearson_dot | 0.4819 | | spearman_dot | 0.4659 | | pearson_max | 0.7086 | | spearman_max | 0.7136 | #### 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 | 0.6926 | | **spearman_cosine** | **0.6859** | | pearson_manhattan | 0.7087 | | spearman_manhattan | 0.7128 | | pearson_euclidean | 0.7089 | | spearman_euclidean | 0.7124 | | pearson_dot | 0.4684 | | spearman_dot | 0.4526 | | pearson_max | 0.7089 | | spearman_max | 0.7128 | #### 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 | 0.6877 | | **spearman_cosine** | **0.6815** | | pearson_manhattan | 0.7084 | | spearman_manhattan | 0.7098 | | pearson_euclidean | 0.7094 | | spearman_euclidean | 0.7104 | | pearson_dot | 0.4439 | | spearman_dot | 0.4255 | | pearson_max | 0.7094 | | spearman_max | 0.7104 | #### 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 | 0.6709 | | **spearman_cosine** | **0.667** | | pearson_manhattan | 0.7042 | | spearman_manhattan | 0.7001 | | pearson_euclidean | 0.7055 | | spearman_euclidean | 0.7023 | | pearson_dot | 0.3786 | | spearman_dot | 0.3593 | | pearson_max | 0.7055 | | spearman_max | 0.7023 | #### 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 | 0.6534 | | **spearman_cosine** | **0.6524** | | pearson_manhattan | 0.692 | | spearman_manhattan | 0.6857 | | pearson_euclidean | 0.695 | | spearman_euclidean | 0.6899 | | pearson_dot | 0.335 | | spearman_dot | 0.3097 | | pearson_max | 0.695 | | spearman_max | 0.6899 | ## Training Details ### Training Dataset #### Mollel/swahili-n_li-triplet-swh-eng * Dataset: Mollel/swahili-n_li-triplet-swh-eng * Size: 1,115,700 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 | |:----------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. | | Mtu aliyepanda farasi anaruka juu ya ndege iliyovunjika. | Mtu yuko nje, juu ya farasi. | Mtu yuko kwenye mkahawa, akiagiza omelette. | | Children smiling and waving at camera | There are children present | The kids are frowning | * 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 #### Mollel/swahili-n_li-triplet-swh-eng * Dataset: Mollel/swahili-n_li-triplet-swh-eng * Size: 13,168 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 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:-------------------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding packages. | The men are fighting outside a deli. | | Wanawake wawili wanakumbatiana huku wakishikilia vifurushi vya kwenda. | Wanawake wawili wanashikilia vifurushi. | Wanaume hao wanapigana nje ya duka la vyakula vitamu. | | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | Two kids in jackets walk to school. | * 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`: 24 - `per_device_eval_batch_size`: 24 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: 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`: 24 - `per_device_eval_batch_size`: 24 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-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`: True - `fp16`: False - `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 | |:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | 0.0043 | 100 | 10.0627 | - | - | - | - | - | | 0.0086 | 200 | 8.2355 | - | - | - | - | - | | 0.0129 | 300 | 6.7233 | - | - | - | - | - | | 0.0172 | 400 | 6.5832 | - | - | - | - | - | | 0.0215 | 500 | 6.7512 | - | - | - | - | - | | 0.0258 | 600 | 6.7634 | - | - | - | - | - | | 0.0301 | 700 | 6.5592 | - | - | - | - | - | | 0.0344 | 800 | 5.0689 | - | - | - | - | - | | 0.0387 | 900 | 4.7079 | - | - | - | - | - | | 0.0430 | 1000 | 4.6359 | - | - | - | - | - | | 0.0473 | 1100 | 4.4513 | - | - | - | - | - | | 0.0516 | 1200 | 4.2328 | - | - | - | - | - | | 0.0559 | 1300 | 3.7454 | - | - | - | - | - | | 0.0602 | 1400 | 3.9198 | - | - | - | - | - | | 0.0645 | 1500 | 4.0727 | - | - | - | - | - | | 0.0688 | 1600 | 3.8923 | - | - | - | - | - | | 0.0731 | 1700 | 3.8137 | - | - | - | - | - | | 0.0774 | 1800 | 4.1512 | - | - | - | - | - | | 0.0817 | 1900 | 4.1304 | - | - | - | - | - | | 0.0860 | 2000 | 4.0195 | - | - | - | - | - | | 0.0903 | 2100 | 3.6836 | - | - | - | - | - | | 0.0946 | 2200 | 2.9968 | - | - | - | - | - | | 0.0990 | 2300 | 2.8909 | - | - | - | - | - | | 0.1033 | 2400 | 3.0884 | - | - | - | - | - | | 0.1076 | 2500 | 3.3081 | - | - | - | - | - | | 0.1119 | 2600 | 3.6266 | - | - | - | - | - | | 0.1162 | 2700 | 4.3754 | - | - | - | - | - | | 0.1205 | 2800 | 4.0218 | - | - | - | - | - | | 0.1248 | 2900 | 3.7167 | - | - | - | - | - | | 0.1291 | 3000 | 3.4815 | - | - | - | - | - | | 0.1334 | 3100 | 3.6446 | - | - | - | - | - | | 0.1377 | 3200 | 3.44 | - | - | - | - | - | | 0.1420 | 3300 | 3.6725 | - | - | - | - | - | | 0.1463 | 3400 | 3.4699 | - | - | - | - | - | | 0.1506 | 3500 | 3.076 | - | - | - | - | - | | 0.1549 | 3600 | 3.1179 | - | - | - | - | - | | 0.1592 | 3700 | 3.1704 | - | - | - | - | - | | 0.1635 | 3800 | 3.4614 | - | - | - | - | - | | 0.1678 | 3900 | 4.1157 | - | - | - | - | - | | 0.1721 | 4000 | 4.1584 | - | - | - | - | - | | 0.1764 | 4100 | 4.5602 | - | - | - | - | - | | 0.1807 | 4200 | 3.6875 | - | - | - | - | - | | 0.1850 | 4300 | 4.1521 | - | - | - | - | - | | 0.1893 | 4400 | 3.5475 | - | - | - | - | - | | 0.1936 | 4500 | 3.4036 | - | - | - | - | - | | 0.1979 | 4600 | 3.0564 | - | - | - | - | - | | 0.2022 | 4700 | 3.7761 | - | - | - | - | - | | 0.2065 | 4800 | 3.6857 | - | - | - | - | - | | 0.2108 | 4900 | 3.3534 | - | - | - | - | - | | 0.2151 | 5000 | 4.1137 | - | - | - | - | - | | 0.2194 | 5100 | 3.5239 | - | - | - | - | - | | 0.2237 | 5200 | 4.1297 | - | - | - | - | - | | 0.2280 | 5300 | 3.5339 | - | - | - | - | - | | 0.2323 | 5400 | 3.9294 | - | - | - | - | - | | 0.2366 | 5500 | 3.717 | - | - | - | - | - | | 0.2409 | 5600 | 3.3346 | - | - | - | - | - | | 0.2452 | 5700 | 4.0495 | - | - | - | - | - | | 0.2495 | 5800 | 3.7869 | - | - | - | - | - | | 0.2538 | 5900 | 3.9533 | - | - | - | - | - | | 0.2581 | 6000 | 4.1135 | - | - | - | - | - | | 0.2624 | 6100 | 3.6655 | - | - | - | - | - | | 0.2667 | 6200 | 3.9111 | - | - | - | - | - | | 0.2710 | 6300 | 3.8582 | - | - | - | - | - | | 0.2753 | 6400 | 3.7712 | - | - | - | - | - | | 0.2796 | 6500 | 3.6536 | - | - | - | - | - | | 0.2839 | 6600 | 3.4516 | - | - | - | - | - | | 0.2882 | 6700 | 3.7151 | - | - | - | - | - | | 0.2925 | 6800 | 3.7659 | - | - | - | - | - | | 0.2969 | 6900 | 3.3159 | - | - | - | - | - | | 0.3012 | 7000 | 3.5753 | - | - | - | - | - | | 0.3055 | 7100 | 4.2095 | - | - | - | - | - | | 0.3098 | 7200 | 3.718 | - | - | - | - | - | | 0.3141 | 7300 | 4.0709 | - | - | - | - | - | | 0.3184 | 7400 | 3.8079 | - | - | - | - | - | | 0.3227 | 7500 | 3.3735 | - | - | - | - | - | | 0.3270 | 7600 | 3.7303 | - | - | - | - | - | | 0.3313 | 7700 | 3.2693 | - | - | - | - | - | | 0.3356 | 7800 | 3.6564 | - | - | - | - | - | | 0.3399 | 7900 | 3.6702 | - | - | - | - | - | | 0.3442 | 8000 | 3.7274 | - | - | - | - | - | | 0.3485 | 8100 | 3.8536 | - | - | - | - | - | | 0.3528 | 8200 | 3.9516 | - | - | - | - | - | | 0.3571 | 8300 | 3.7351 | - | - | - | - | - | | 0.3614 | 8400 | 3.649 | - | - | - | - | - | | 0.3657 | 8500 | 3.5913 | - | - | - | - | - | | 0.3700 | 8600 | 3.7733 | - | - | - | - | - | | 0.3743 | 8700 | 3.6359 | - | - | - | - | - | | 0.3786 | 8800 | 4.2983 | - | - | - | - | - | | 0.3829 | 8900 | 3.6692 | - | - | - | - | - | | 0.3872 | 9000 | 3.7309 | - | - | - | - | - | | 0.3915 | 9100 | 3.8886 | - | - | - | - | - | | 0.3958 | 9200 | 3.8999 | - | - | - | - | - | | 0.4001 | 9300 | 3.5528 | - | - | - | - | - | | 0.4044 | 9400 | 3.6309 | - | - | - | - | - | | 0.4087 | 9500 | 4.2475 | - | - | - | - | - | | 0.4130 | 9600 | 3.793 | - | - | - | - | - | | 0.4173 | 9700 | 3.6575 | - | - | - | - | - | | 0.4216 | 9800 | 3.84 | - | - | - | - | - | | 0.4259 | 9900 | 3.3721 | - | - | - | - | - | | 0.4302 | 10000 | 4.3743 | - | - | - | - | - | | 0.4345 | 10100 | 3.5054 | - | - | - | - | - | | 0.4388 | 10200 | 3.54 | - | - | - | - | - | | 0.4431 | 10300 | 3.6197 | - | - | - | - | - | | 0.4474 | 10400 | 3.7567 | - | - | - | - | - | | 0.4517 | 10500 | 3.9814 | - | - | - | - | - | | 0.4560 | 10600 | 3.6277 | - | - | - | - | - | | 0.4603 | 10700 | 3.5071 | - | - | - | - | - | | 0.4646 | 10800 | 3.8348 | - | - | - | - | - | | 0.4689 | 10900 | 3.8674 | - | - | - | - | - | | 0.4732 | 11000 | 3.0325 | - | - | - | - | - | | 0.4775 | 11100 | 3.7262 | - | - | - | - | - | | 0.4818 | 11200 | 3.6921 | - | - | - | - | - | | 0.4861 | 11300 | 3.4946 | - | - | - | - | - | | 0.4904 | 11400 | 3.7541 | - | - | - | - | - | | 0.4948 | 11500 | 3.6751 | - | - | - | - | - | | 0.4991 | 11600 | 3.8765 | - | - | - | - | - | | 0.5034 | 11700 | 3.5058 | - | - | - | - | - | | 0.5077 | 11800 | 3.5135 | - | - | - | - | - | | 0.5120 | 11900 | 3.8052 | - | - | - | - | - | | 0.5163 | 12000 | 3.3015 | - | - | - | - | - | | 0.5206 | 12100 | 3.5389 | - | - | - | - | - | | 0.5249 | 12200 | 3.5226 | - | - | - | - | - | | 0.5292 | 12300 | 3.6715 | - | - | - | - | - | | 0.5335 | 12400 | 3.2256 | - | - | - | - | - | | 0.5378 | 12500 | 3.3447 | - | - | - | - | - | | 0.5421 | 12600 | 3.6315 | - | - | - | - | - | | 0.5464 | 12700 | 3.8674 | - | - | - | - | - | | 0.5507 | 12800 | 3.4066 | - | - | - | - | - | | 0.5550 | 12900 | 3.7356 | - | - | - | - | - | | 0.5593 | 13000 | 3.5742 | - | - | - | - | - | | 0.5636 | 13100 | 3.7676 | - | - | - | - | - | | 0.5679 | 13200 | 3.7907 | - | - | - | - | - | | 0.5722 | 13300 | 3.8089 | - | - | - | - | - | | 0.5765 | 13400 | 3.4742 | - | - | - | - | - | | 0.5808 | 13500 | 3.6536 | - | - | - | - | - | | 0.5851 | 13600 | 3.7736 | - | - | - | - | - | | 0.5894 | 13700 | 3.9072 | - | - | - | - | - | | 0.5937 | 13800 | 3.7386 | - | - | - | - | - | | 0.5980 | 13900 | 3.3387 | - | - | - | - | - | | 0.6023 | 14000 | 3.5509 | - | - | - | - | - | | 0.6066 | 14100 | 3.7056 | - | - | - | - | - | | 0.6109 | 14200 | 3.7283 | - | - | - | - | - | | 0.6152 | 14300 | 3.7301 | - | - | - | - | - | | 0.6195 | 14400 | 3.8027 | - | - | - | - | - | | 0.6238 | 14500 | 3.5606 | - | - | - | - | - | | 0.6281 | 14600 | 3.9467 | - | - | - | - | - | | 0.6324 | 14700 | 3.3394 | - | - | - | - | - | | 0.6367 | 14800 | 4.1254 | - | - | - | - | - | | 0.6410 | 14900 | 3.7121 | - | - | - | - | - | | 0.6453 | 15000 | 3.9167 | - | - | - | - | - | | 0.6496 | 15100 | 3.8084 | - | - | - | - | - | | 0.6539 | 15200 | 3.7794 | - | - | - | - | - | | 0.6582 | 15300 | 3.7664 | - | - | - | - | - | | 0.6625 | 15400 | 3.4378 | - | - | - | - | - | | 0.6668 | 15500 | 3.6632 | - | - | - | - | - | | 0.6711 | 15600 | 3.8493 | - | - | - | - | - | | 0.6754 | 15700 | 4.1475 | - | - | - | - | - | | 0.6797 | 15800 | 3.5782 | - | - | - | - | - | | 0.6840 | 15900 | 3.4341 | - | - | - | - | - | | 0.6883 | 16000 | 3.3295 | - | - | - | - | - | | 0.6927 | 16100 | 3.8165 | - | - | - | - | - | | 0.6970 | 16200 | 3.9702 | - | - | - | - | - | | 0.7013 | 16300 | 3.6555 | - | - | - | - | - | | 0.7056 | 16400 | 3.6946 | - | - | - | - | - | | 0.7099 | 16500 | 3.8027 | - | - | - | - | - | | 0.7142 | 16600 | 3.4523 | - | - | - | - | - | | 0.7185 | 16700 | 3.461 | - | - | - | - | - | | 0.7228 | 16800 | 3.4403 | - | - | - | - | - | | 0.7271 | 16900 | 3.6398 | - | - | - | - | - | | 0.7314 | 17000 | 3.8443 | - | - | - | - | - | | 0.7357 | 17100 | 3.6012 | - | - | - | - | - | | 0.7400 | 17200 | 3.6645 | - | - | - | - | - | | 0.7443 | 17300 | 3.4899 | - | - | - | - | - | | 0.7486 | 17400 | 3.7186 | - | - | - | - | - | | 0.7529 | 17500 | 3.6199 | - | - | - | - | - | | 0.7572 | 17600 | 4.4274 | - | - | - | - | - | | 0.7615 | 17700 | 4.0262 | - | - | - | - | - | | 0.7658 | 17800 | 3.9325 | - | - | - | - | - | | 0.7701 | 17900 | 3.6338 | - | - | - | - | - | | 0.7744 | 18000 | 3.6136 | - | - | - | - | - | | 0.7787 | 18100 | 3.4514 | - | - | - | - | - | | 0.7830 | 18200 | 3.4427 | - | - | - | - | - | | 0.7873 | 18300 | 3.3601 | - | - | - | - | - | | 0.7916 | 18400 | 3.313 | - | - | - | - | - | | 0.7959 | 18500 | 3.4062 | - | - | - | - | - | | 0.8002 | 18600 | 3.098 | - | - | - | - | - | | 0.8045 | 18700 | 3.183 | - | - | - | - | - | | 0.8088 | 18800 | 3.1482 | - | - | - | - | - | | 0.8131 | 18900 | 3.0122 | - | - | - | - | - | | 0.8174 | 19000 | 3.0828 | - | - | - | - | - | | 0.8217 | 19100 | 3.063 | - | - | - | - | - | | 0.8260 | 19200 | 2.9688 | - | - | - | - | - | | 0.8303 | 19300 | 3.0425 | - | - | - | - | - | | 0.8346 | 19400 | 3.2018 | - | - | - | - | - | | 0.8389 | 19500 | 2.9111 | - | - | - | - | - | | 0.8432 | 19600 | 2.9516 | - | - | - | - | - | | 0.8475 | 19700 | 2.9115 | - | - | - | - | - | | 0.8518 | 19800 | 2.9323 | - | - | - | - | - | | 0.8561 | 19900 | 2.8753 | - | - | - | - | - | | 0.8604 | 20000 | 2.8344 | - | - | - | - | - | | 0.8647 | 20100 | 2.7665 | - | - | - | - | - | | 0.8690 | 20200 | 2.7732 | - | - | - | - | - | | 0.8733 | 20300 | 2.8622 | - | - | - | - | - | | 0.8776 | 20400 | 2.8749 | - | - | - | - | - | | 0.8819 | 20500 | 2.8534 | - | - | - | - | - | | 0.8863 | 20600 | 2.9254 | - | - | - | - | - | | 0.8906 | 20700 | 2.7366 | - | - | - | - | - | | 0.8949 | 20800 | 2.7287 | - | - | - | - | - | | 0.8992 | 20900 | 2.9469 | - | - | - | - | - | | 0.9035 | 21000 | 2.9052 | - | - | - | - | - | | 0.9078 | 21100 | 2.7256 | - | - | - | - | - | | 0.9121 | 21200 | 2.8469 | - | - | - | - | - | | 0.9164 | 21300 | 2.6626 | - | - | - | - | - | | 0.9207 | 21400 | 2.6796 | - | - | - | - | - | | 0.9250 | 21500 | 2.6927 | - | - | - | - | - | | 0.9293 | 21600 | 2.7125 | - | - | - | - | - | | 0.9336 | 21700 | 2.6734 | - | - | - | - | - | | 0.9379 | 21800 | 2.7199 | - | - | - | - | - | | 0.9422 | 21900 | 2.6635 | - | - | - | - | - | | 0.9465 | 22000 | 2.5218 | - | - | - | - | - | | 0.9508 | 22100 | 2.7595 | - | - | - | - | - | | 0.9551 | 22200 | 2.6821 | - | - | - | - | - | | 0.9594 | 22300 | 2.6578 | - | - | - | - | - | | 0.9637 | 22400 | 2.568 | - | - | - | - | - | | 0.9680 | 22500 | 2.5527 | - | - | - | - | - | | 0.9723 | 22600 | 2.6857 | - | - | - | - | - | | 0.9766 | 22700 | 2.6637 | - | - | - | - | - | | 0.9809 | 22800 | 2.6311 | - | - | - | - | - | | 0.9852 | 22900 | 2.4635 | - | - | - | - | - | | 0.9895 | 23000 | 2.6239 | - | - | - | - | - | | 0.9938 | 23100 | 2.6873 | - | - | - | - | - | | 0.9981 | 23200 | 2.5138 | - | - | - | - | - | | 1.0 | 23244 | - | 0.6670 | 0.6815 | 0.6859 | 0.6524 | 0.6872 |
### 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} } ```