--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1115700 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Geotrend/bert-base-sw-cased 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 Geotrend/bert-base-sw-cased results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: 0.6937245827269046 name: Pearson Cosine - type: spearman_cosine value: 0.6872564222432196 name: Spearman Cosine - type: pearson_manhattan value: 0.6671541268726737 name: Pearson Manhattan - type: spearman_manhattan value: 0.6578428252987948 name: Spearman Manhattan - type: pearson_euclidean value: 0.6672292642346008 name: Pearson Euclidean - type: spearman_euclidean value: 0.6577692881532263 name: Spearman Euclidean - type: pearson_dot value: 0.5234944445417878 name: Pearson Dot - type: spearman_dot value: 0.5126395384896926 name: Spearman Dot - type: pearson_max value: 0.6937245827269046 name: Pearson Max - type: spearman_max value: 0.6872564222432196 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.689885399601221 name: Pearson Cosine - type: spearman_cosine value: 0.6847071916895495 name: Spearman Cosine - type: pearson_manhattan value: 0.6678379220949281 name: Pearson Manhattan - type: spearman_manhattan value: 0.6579957115799916 name: Spearman Manhattan - type: pearson_euclidean value: 0.6673062843667007 name: Pearson Euclidean - type: spearman_euclidean value: 0.6573006123381013 name: Spearman Euclidean - type: pearson_dot value: 0.49533316366864977 name: Pearson Dot - type: spearman_dot value: 0.48723679408818543 name: Spearman Dot - type: pearson_max value: 0.689885399601221 name: Pearson Max - type: spearman_max value: 0.6847071916895495 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.6873377612773459 name: Pearson Cosine - type: spearman_cosine value: 0.6816874105466478 name: Spearman Cosine - type: pearson_manhattan value: 0.667357515297651 name: Pearson Manhattan - type: spearman_manhattan value: 0.6557727891191705 name: Spearman Manhattan - type: pearson_euclidean value: 0.6674937201647584 name: Pearson Euclidean - type: spearman_euclidean value: 0.6560441259953166 name: Spearman Euclidean - type: pearson_dot value: 0.45660372834373963 name: Pearson Dot - type: spearman_dot value: 0.4533070407260065 name: Spearman Dot - type: pearson_max value: 0.6873377612773459 name: Pearson Max - type: spearman_max value: 0.6816874105466478 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.6836009506667413 name: Pearson Cosine - type: spearman_cosine value: 0.6795423695973911 name: Spearman Cosine - type: pearson_manhattan value: 0.6663652896396122 name: Pearson Manhattan - type: spearman_manhattan value: 0.6534731725514219 name: Spearman Manhattan - type: pearson_euclidean value: 0.6663726876345561 name: Pearson Euclidean - type: spearman_euclidean value: 0.6537216014002204 name: Spearman Euclidean - type: pearson_dot value: 0.43102957451470686 name: Pearson Dot - type: spearman_dot value: 0.431538008932168 name: Spearman Dot - type: pearson_max value: 0.6836009506667413 name: Pearson Max - type: spearman_max value: 0.6795423695973911 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.6715253560367674 name: Pearson Cosine - type: spearman_cosine value: 0.669070001537953 name: Spearman Cosine - type: pearson_manhattan value: 0.6571390159051358 name: Pearson Manhattan - type: spearman_manhattan value: 0.6456119247619697 name: Spearman Manhattan - type: pearson_euclidean value: 0.6598587843081631 name: Pearson Euclidean - type: spearman_euclidean value: 0.6472279949159918 name: Spearman Euclidean - type: pearson_dot value: 0.36757468941627225 name: Pearson Dot - type: spearman_dot value: 0.3678274698380672 name: Spearman Dot - type: pearson_max value: 0.6715253560367674 name: Pearson Max - type: spearman_max value: 0.669070001537953 name: Spearman Max --- # SentenceTransformer based on Geotrend/bert-base-sw-cased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased) 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:** [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased) - **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel (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/MultiLinguSwahili-bert-base-sw-cased-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 | 0.6937 | | **spearman_cosine** | **0.6873** | | pearson_manhattan | 0.6672 | | spearman_manhattan | 0.6578 | | pearson_euclidean | 0.6672 | | spearman_euclidean | 0.6578 | | pearson_dot | 0.5235 | | spearman_dot | 0.5126 | | pearson_max | 0.6937 | | 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 | 0.6899 | | **spearman_cosine** | **0.6847** | | pearson_manhattan | 0.6678 | | spearman_manhattan | 0.658 | | pearson_euclidean | 0.6673 | | spearman_euclidean | 0.6573 | | pearson_dot | 0.4953 | | spearman_dot | 0.4872 | | pearson_max | 0.6899 | | spearman_max | 0.6847 | #### 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 | 0.6873 | | **spearman_cosine** | **0.6817** | | pearson_manhattan | 0.6674 | | spearman_manhattan | 0.6558 | | pearson_euclidean | 0.6675 | | spearman_euclidean | 0.656 | | pearson_dot | 0.4566 | | spearman_dot | 0.4533 | | pearson_max | 0.6873 | | spearman_max | 0.6817 | #### 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 | 0.6836 | | **spearman_cosine** | **0.6795** | | pearson_manhattan | 0.6664 | | spearman_manhattan | 0.6535 | | pearson_euclidean | 0.6664 | | spearman_euclidean | 0.6537 | | pearson_dot | 0.431 | | spearman_dot | 0.4315 | | pearson_max | 0.6836 | | spearman_max | 0.6795 | #### 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 | 0.6715 | | **spearman_cosine** | **0.6691** | | pearson_manhattan | 0.6571 | | spearman_manhattan | 0.6456 | | pearson_euclidean | 0.6599 | | spearman_euclidean | 0.6472 | | pearson_dot | 0.3676 | | spearman_dot | 0.3678 | | pearson_max | 0.6715 | | spearman_max | 0.6691 | ## 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`: 32 - `per_device_eval_batch_size`: 32 - `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`: 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`: 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 | |:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | 0.0057 | 100 | 19.9104 | - | - | - | - | - | | 0.0115 | 200 | 15.4038 | - | - | - | - | - | | 0.0172 | 300 | 12.4565 | - | - | - | - | - | | 0.0229 | 400 | 11.8633 | - | - | - | - | - | | 0.0287 | 500 | 11.0601 | - | - | - | - | - | | 0.0344 | 600 | 9.7725 | - | - | - | - | - | | 0.0402 | 700 | 8.8549 | - | - | - | - | - | | 0.0459 | 800 | 8.0831 | - | - | - | - | - | | 0.0516 | 900 | 7.9941 | - | - | - | - | - | | 0.0574 | 1000 | 7.6537 | - | - | - | - | - | | 0.0631 | 1100 | 7.9303 | - | - | - | - | - | | 0.0688 | 1200 | 7.5246 | - | - | - | - | - | | 0.0746 | 1300 | 7.7754 | - | - | - | - | - | | 0.0803 | 1400 | 7.668 | - | - | - | - | - | | 0.0860 | 1500 | 6.7171 | - | - | - | - | - | | 0.0918 | 1600 | 6.347 | - | - | - | - | - | | 0.0975 | 1700 | 6.0 | - | - | - | - | - | | 0.1033 | 1800 | 6.4314 | - | - | - | - | - | | 0.1090 | 1900 | 6.7947 | - | - | - | - | - | | 0.1147 | 2000 | 6.9316 | - | - | - | - | - | | 0.1205 | 2100 | 6.6304 | - | - | - | - | - | | 0.1262 | 2200 | 6.132 | - | - | - | - | - | | 0.1319 | 2300 | 5.8953 | - | - | - | - | - | | 0.1377 | 2400 | 5.6954 | - | - | - | - | - | | 0.1434 | 2500 | 5.6832 | - | - | - | - | - | | 0.1491 | 2600 | 5.2266 | - | - | - | - | - | | 0.1549 | 2700 | 5.0678 | - | - | - | - | - | | 0.1606 | 2800 | 5.4733 | - | - | - | - | - | | 0.1664 | 2900 | 6.0899 | - | - | - | - | - | | 0.1721 | 3000 | 6.332 | - | - | - | - | - | | 0.1778 | 3100 | 6.4937 | - | - | - | - | - | | 0.1836 | 3200 | 6.2242 | - | - | - | - | - | | 0.1893 | 3300 | 5.8023 | - | - | - | - | - | | 0.1950 | 3400 | 5.0745 | - | - | - | - | - | | 0.2008 | 3500 | 5.5806 | - | - | - | - | - | | 0.2065 | 3600 | 5.5191 | - | - | - | - | - | | 0.2122 | 3700 | 5.3849 | - | - | - | - | - | | 0.2180 | 3800 | 5.4828 | - | - | - | - | - | | 0.2237 | 3900 | 5.9982 | - | - | - | - | - | | 0.2294 | 4000 | 5.6842 | - | - | - | - | - | | 0.2352 | 4100 | 5.1627 | - | - | - | - | - | | 0.2409 | 4200 | 5.154 | - | - | - | - | - | | 0.2467 | 4300 | 5.7932 | - | - | - | - | - | | 0.2524 | 4400 | 5.5758 | - | - | - | - | - | | 0.2581 | 4500 | 5.5212 | - | - | - | - | - | | 0.2639 | 4600 | 5.5692 | - | - | - | - | - | | 0.2696 | 4700 | 5.2699 | - | - | - | - | - | | 0.2753 | 4800 | 5.4919 | - | - | - | - | - | | 0.2811 | 4900 | 5.0754 | - | - | - | - | - | | 0.2868 | 5000 | 5.1514 | - | - | - | - | - | | 0.2925 | 5100 | 5.0241 | - | - | - | - | - | | 0.2983 | 5200 | 5.2679 | - | - | - | - | - | | 0.3040 | 5300 | 5.3576 | - | - | - | - | - | | 0.3098 | 5400 | 5.3454 | - | - | - | - | - | | 0.3155 | 5500 | 5.2142 | - | - | - | - | - | | 0.3212 | 5600 | 4.8418 | - | - | - | - | - | | 0.3270 | 5700 | 4.9597 | - | - | - | - | - | | 0.3327 | 5800 | 5.1989 | - | - | - | - | - | | 0.3384 | 5900 | 5.2624 | - | - | - | - | - | | 0.3442 | 6000 | 5.0705 | - | - | - | - | - | | 0.3499 | 6100 | 5.232 | - | - | - | - | - | | 0.3556 | 6200 | 5.2428 | - | - | - | - | - | | 0.3614 | 6300 | 4.755 | - | - | - | - | - | | 0.3671 | 6400 | 4.7266 | - | - | - | - | - | | 0.3729 | 6500 | 4.6452 | - | - | - | - | - | | 0.3786 | 6600 | 5.1431 | - | - | - | - | - | | 0.3843 | 6700 | 4.5343 | - | - | - | - | - | | 0.3901 | 6800 | 4.698 | - | - | - | - | - | | 0.3958 | 6900 | 4.6944 | - | - | - | - | - | | 0.4015 | 7000 | 4.6255 | - | - | - | - | - | | 0.4073 | 7100 | 5.0211 | - | - | - | - | - | | 0.4130 | 7200 | 4.6974 | - | - | - | - | - | | 0.4187 | 7300 | 4.9182 | - | - | - | - | - | | 0.4245 | 7400 | 4.652 | - | - | - | - | - | | 0.4302 | 7500 | 5.1015 | - | - | - | - | - | | 0.4360 | 7600 | 4.5249 | - | - | - | - | - | | 0.4417 | 7700 | 4.455 | - | - | - | - | - | | 0.4474 | 7800 | 4.8153 | - | - | - | - | - | | 0.4532 | 7900 | 4.7665 | - | - | - | - | - | | 0.4589 | 8000 | 4.3413 | - | - | - | - | - | | 0.4646 | 8100 | 4.4697 | - | - | - | - | - | | 0.4704 | 8200 | 4.6776 | - | - | - | - | - | | 0.4761 | 8300 | 4.2868 | - | - | - | - | - | | 0.4818 | 8400 | 4.7052 | - | - | - | - | - | | 0.4876 | 8500 | 4.4721 | - | - | - | - | - | | 0.4933 | 8600 | 4.6926 | - | - | - | - | - | | 0.4991 | 8700 | 4.9891 | - | - | - | - | - | | 0.5048 | 8800 | 4.4837 | - | - | - | - | - | | 0.5105 | 8900 | 4.8127 | - | - | - | - | - | | 0.5163 | 9000 | 4.3438 | - | - | - | - | - | | 0.5220 | 9100 | 4.4743 | - | - | - | - | - | | 0.5277 | 9200 | 4.6879 | - | - | - | - | - | | 0.5335 | 9300 | 4.3593 | - | - | - | - | - | | 0.5392 | 9400 | 4.3023 | - | - | - | - | - | | 0.5449 | 9500 | 4.8188 | - | - | - | - | - | | 0.5507 | 9600 | 4.6142 | - | - | - | - | - | | 0.5564 | 9700 | 4.7679 | - | - | - | - | - | | 0.5622 | 9800 | 4.6224 | - | - | - | - | - | | 0.5679 | 9900 | 4.9154 | - | - | - | - | - | | 0.5736 | 10000 | 4.7557 | - | - | - | - | - | | 0.5794 | 10100 | 4.6395 | - | - | - | - | - | | 0.5851 | 10200 | 4.7977 | - | - | - | - | - | | 0.5908 | 10300 | 4.915 | - | - | - | - | - | | 0.5966 | 10400 | 4.4854 | - | - | - | - | - | | 0.6023 | 10500 | 4.3973 | - | - | - | - | - | | 0.6080 | 10600 | 4.6964 | - | - | - | - | - | | 0.6138 | 10700 | 4.8853 | - | - | - | - | - | | 0.6195 | 10800 | 4.786 | - | - | - | - | - | | 0.6253 | 10900 | 4.5482 | - | - | - | - | - | | 0.6310 | 11000 | 4.4857 | - | - | - | - | - | | 0.6367 | 11100 | 4.7415 | - | - | - | - | - | | 0.6425 | 11200 | 4.2596 | - | - | - | - | - | | 0.6482 | 11300 | 4.8578 | - | - | - | - | - | | 0.6539 | 11400 | 4.5471 | - | - | - | - | - | | 0.6597 | 11500 | 4.8337 | - | - | - | - | - | | 0.6654 | 11600 | 4.2244 | - | - | - | - | - | | 0.6711 | 11700 | 4.9619 | - | - | - | - | - | | 0.6769 | 11800 | 4.9369 | - | - | - | - | - | | 0.6826 | 11900 | 4.2697 | - | - | - | - | - | | 0.6883 | 12000 | 4.2711 | - | - | - | - | - | | 0.6941 | 12100 | 4.6396 | - | - | - | - | - | | 0.6998 | 12200 | 4.5626 | - | - | - | - | - | | 0.7056 | 12300 | 4.5767 | - | - | - | - | - | | 0.7113 | 12400 | 4.6449 | - | - | - | - | - | | 0.7170 | 12500 | 4.4217 | - | - | - | - | - | | 0.7228 | 12600 | 4.0203 | - | - | - | - | - | | 0.7285 | 12700 | 4.5381 | - | - | - | - | - | | 0.7342 | 12800 | 4.5865 | - | - | - | - | - | | 0.7400 | 12900 | 4.4203 | - | - | - | - | - | | 0.7457 | 13000 | 4.3761 | - | - | - | - | - | | 0.7514 | 13100 | 4.093 | - | - | - | - | - | | 0.7572 | 13200 | 5.9235 | - | - | - | - | - | | 0.7629 | 13300 | 5.4098 | - | - | - | - | - | | 0.7687 | 13400 | 5.3079 | - | - | - | - | - | | 0.7744 | 13500 | 5.0946 | - | - | - | - | - | | 0.7801 | 13600 | 4.7098 | - | - | - | - | - | | 0.7859 | 13700 | 4.9471 | - | - | - | - | - | | 0.7916 | 13800 | 4.5742 | - | - | - | - | - | | 0.7973 | 13900 | 4.6178 | - | - | - | - | - | | 0.8031 | 14000 | 4.4516 | - | - | - | - | - | | 0.8088 | 14100 | 4.429 | - | - | - | - | - | | 0.8145 | 14200 | 4.3812 | - | - | - | - | - | | 0.8203 | 14300 | 4.3739 | - | - | - | - | - | | 0.8260 | 14400 | 4.3821 | - | - | - | - | - | | 0.8318 | 14500 | 4.4396 | - | - | - | - | - | | 0.8375 | 14600 | 4.2667 | - | - | - | - | - | | 0.8432 | 14700 | 4.1963 | - | - | - | - | - | | 0.8490 | 14800 | 4.1298 | - | - | - | - | - | | 0.8547 | 14900 | 4.1843 | - | - | - | - | - | | 0.8604 | 15000 | 4.0735 | - | - | - | - | - | | 0.8662 | 15100 | 3.9319 | - | - | - | - | - | | 0.8719 | 15200 | 4.1544 | - | - | - | - | - | | 0.8776 | 15300 | 4.105 | - | - | - | - | - | | 0.8834 | 15400 | 4.014 | - | - | - | - | - | | 0.8891 | 15500 | 4.0345 | - | - | - | - | - | | 0.8949 | 15600 | 3.9127 | - | - | - | - | - | | 0.9006 | 15700 | 4.1002 | - | - | - | - | - | | 0.9063 | 15800 | 3.8564 | - | - | - | - | - | | 0.9121 | 15900 | 3.9297 | - | - | - | - | - | | 0.9178 | 16000 | 3.8487 | - | - | - | - | - | | 0.9235 | 16100 | 3.7099 | - | - | - | - | - | | 0.9293 | 16200 | 3.8545 | - | - | - | - | - | | 0.9350 | 16300 | 3.8122 | - | - | - | - | - | | 0.9407 | 16400 | 3.8951 | - | - | - | - | - | | 0.9465 | 16500 | 3.6996 | - | - | - | - | - | | 0.9522 | 16600 | 3.9081 | - | - | - | - | - | | 0.9580 | 16700 | 3.8603 | - | - | - | - | - | | 0.9637 | 16800 | 3.8534 | - | - | - | - | - | | 0.9694 | 16900 | 3.8145 | - | - | - | - | - | | 0.9752 | 17000 | 3.9858 | - | - | - | - | - | | 0.9809 | 17100 | 3.8224 | - | - | - | - | - | | 0.9866 | 17200 | 3.7469 | - | - | - | - | - | | 0.9924 | 17300 | 3.9066 | - | - | - | - | - | | 0.9981 | 17400 | 3.6754 | - | - | - | - | - | | 1.0 | 17433 | - | 0.6795 | 0.6817 | 0.6847 | 0.6691 | 0.6873 |
### 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} } ```