--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:557850 - 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: 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 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: 0.6868804546581948 name: Pearson Cosine - type: spearman_cosine value: 0.6801625382694466 name: Spearman Cosine - type: pearson_manhattan value: 0.6719079171543956 name: Pearson Manhattan - type: spearman_manhattan value: 0.6653137984517007 name: Spearman Manhattan - type: pearson_euclidean value: 0.6734384393604611 name: Pearson Euclidean - type: spearman_euclidean value: 0.6665812962708187 name: Spearman Euclidean - type: pearson_dot value: 0.5540255947111082 name: Pearson Dot - type: spearman_dot value: 0.5399212934179993 name: Spearman Dot - type: pearson_max value: 0.6868804546581948 name: Pearson Max - type: spearman_max value: 0.6801625382694466 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: 0.6827780698031986 name: Pearson Cosine - type: spearman_cosine value: 0.6770486364807735 name: Spearman Cosine - type: pearson_manhattan value: 0.6729437410000495 name: Pearson Manhattan - type: spearman_manhattan value: 0.6664360018282044 name: Spearman Manhattan - type: pearson_euclidean value: 0.6738342605019458 name: Pearson Euclidean - type: spearman_euclidean value: 0.6666791464094138 name: Spearman Euclidean - type: pearson_dot value: 0.5296210420398023 name: Pearson Dot - type: spearman_dot value: 0.5173769714392553 name: Spearman Dot - type: pearson_max value: 0.6827780698031986 name: Pearson Max - type: spearman_max value: 0.6770486364807735 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.6758051721795716 name: Pearson Cosine - type: spearman_cosine value: 0.6701833115162764 name: Spearman Cosine - type: pearson_manhattan value: 0.671762500960023 name: Pearson Manhattan - type: spearman_manhattan value: 0.6643430423969034 name: Spearman Manhattan - type: pearson_euclidean value: 0.6730238156482042 name: Pearson Euclidean - type: spearman_euclidean value: 0.6649839339725255 name: Spearman Euclidean - type: pearson_dot value: 0.48923961423508167 name: Pearson Dot - type: spearman_dot value: 0.4783312389130331 name: Spearman Dot - type: pearson_max value: 0.6758051721795716 name: Pearson Max - type: spearman_max value: 0.6701833115162764 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.6700363607439113 name: Pearson Cosine - type: spearman_cosine value: 0.6637709194412489 name: Spearman Cosine - type: pearson_manhattan value: 0.6692814840348797 name: Pearson Manhattan - type: spearman_manhattan value: 0.6594295578885248 name: Spearman Manhattan - type: pearson_euclidean value: 0.671006713633375 name: Pearson Euclidean - type: spearman_euclidean value: 0.6600674238087292 name: Spearman Euclidean - type: pearson_dot value: 0.45094972472157246 name: Pearson Dot - type: spearman_dot value: 0.44023350072779777 name: Spearman Dot - type: pearson_max value: 0.671006713633375 name: Pearson Max - type: spearman_max value: 0.6637709194412489 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.6614685875750459 name: Pearson Cosine - type: spearman_cosine value: 0.6556282400518681 name: Spearman Cosine - type: pearson_manhattan value: 0.665261323713716 name: Pearson Manhattan - type: spearman_manhattan value: 0.6533415018004937 name: Spearman Manhattan - type: pearson_euclidean value: 0.6671725346980402 name: Pearson Euclidean - type: spearman_euclidean value: 0.6540012112658994 name: Spearman Euclidean - type: pearson_dot value: 0.38682442010639634 name: Pearson Dot - type: spearman_dot value: 0.37712136401470375 name: Spearman Dot - type: pearson_max value: 0.6671725346980402 name: Pearson Max - type: spearman_max value: 0.6556282400518681 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). 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 ### 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("Mollel/swahili-bert-base-sw-cased-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 | 0.6869 | | **spearman_cosine** | **0.6802** | | pearson_manhattan | 0.6719 | | spearman_manhattan | 0.6653 | | pearson_euclidean | 0.6734 | | spearman_euclidean | 0.6666 | | pearson_dot | 0.554 | | spearman_dot | 0.5399 | | pearson_max | 0.6869 | | spearman_max | 0.6802 | #### 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 | 0.6828 | | **spearman_cosine** | **0.677** | | pearson_manhattan | 0.6729 | | spearman_manhattan | 0.6664 | | pearson_euclidean | 0.6738 | | spearman_euclidean | 0.6667 | | pearson_dot | 0.5296 | | spearman_dot | 0.5174 | | pearson_max | 0.6828 | | spearman_max | 0.677 | #### 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 | 0.6758 | | **spearman_cosine** | **0.6702** | | pearson_manhattan | 0.6718 | | spearman_manhattan | 0.6643 | | pearson_euclidean | 0.673 | | spearman_euclidean | 0.665 | | pearson_dot | 0.4892 | | spearman_dot | 0.4783 | | pearson_max | 0.6758 | | spearman_max | 0.6702 | #### 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 | 0.67 | | **spearman_cosine** | **0.6638** | | pearson_manhattan | 0.6693 | | spearman_manhattan | 0.6594 | | pearson_euclidean | 0.671 | | spearman_euclidean | 0.6601 | | pearson_dot | 0.4509 | | spearman_dot | 0.4402 | | pearson_max | 0.671 | | spearman_max | 0.6638 | #### 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 | 0.6615 | | **spearman_cosine** | **0.6556** | | pearson_manhattan | 0.6653 | | spearman_manhattan | 0.6533 | | pearson_euclidean | 0.6672 | | spearman_euclidean | 0.654 | | pearson_dot | 0.3868 | | spearman_dot | 0.3771 | | pearson_max | 0.6672 | | spearman_max | 0.6556 | ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 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`: 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 | 20.0932 | - | - | - | - | - | | 0.0115 | 200 | 16.2641 | - | - | - | - | - | | 0.0172 | 300 | 12.797 | - | - | - | - | - | | 0.0229 | 400 | 12.1927 | - | - | - | - | - | | 0.0287 | 500 | 11.0423 | - | - | - | - | - | | 0.0344 | 600 | 9.676 | - | - | - | - | - | | 0.0402 | 700 | 8.1545 | - | - | - | - | - | | 0.0459 | 800 | 7.7822 | - | - | - | - | - | | 0.0516 | 900 | 7.9352 | - | - | - | - | - | | 0.0574 | 1000 | 7.9534 | - | - | - | - | - | | 0.0631 | 1100 | 8.1006 | - | - | - | - | - | | 0.0688 | 1200 | 7.4767 | - | - | - | - | - | | 0.0746 | 1300 | 8.3747 | - | - | - | - | - | | 0.0803 | 1400 | 7.7686 | - | - | - | - | - | | 0.0860 | 1500 | 6.8076 | - | - | - | - | - | | 0.0918 | 1600 | 6.9238 | - | - | - | - | - | | 0.0975 | 1700 | 6.5503 | - | - | - | - | - | | 0.1033 | 1800 | 6.74 | - | - | - | - | - | | 0.1090 | 1900 | 7.7802 | - | - | - | - | - | | 0.1147 | 2000 | 7.2594 | - | - | - | - | - | | 0.1205 | 2100 | 7.091 | - | - | - | - | - | | 0.1262 | 2200 | 6.8677 | - | - | - | - | - | | 0.1319 | 2300 | 6.4249 | - | - | - | - | - | | 0.1377 | 2400 | 6.1512 | - | - | - | - | - | | 0.1434 | 2500 | 5.9714 | - | - | - | - | - | | 0.1491 | 2600 | 5.4914 | - | - | - | - | - | | 0.1549 | 2700 | 5.5825 | - | - | - | - | - | | 0.1606 | 2800 | 5.9456 | - | - | - | - | - | | 0.1664 | 2900 | 6.4012 | - | - | - | - | - | | 0.1721 | 3000 | 7.1999 | - | - | - | - | - | | 0.1778 | 3100 | 6.8254 | - | - | - | - | - | | 0.1836 | 3200 | 6.541 | - | - | - | - | - | | 0.1893 | 3300 | 6.5411 | - | - | - | - | - | | 0.1950 | 3400 | 5.56 | - | - | - | - | - | | 0.2008 | 3500 | 6.4692 | - | - | - | - | - | | 0.2065 | 3600 | 5.9266 | - | - | - | - | - | | 0.2122 | 3700 | 6.2055 | - | - | - | - | - | | 0.2180 | 3800 | 6.0835 | - | - | - | - | - | | 0.2237 | 3900 | 6.6112 | - | - | - | - | - | | 0.2294 | 4000 | 6.3391 | - | - | - | - | - | | 0.2352 | 4100 | 5.8379 | - | - | - | - | - | | 0.2409 | 4200 | 5.8107 | - | - | - | - | - | | 0.2467 | 4300 | 6.1473 | - | - | - | - | - | | 0.2524 | 4400 | 6.2827 | - | - | - | - | - | | 0.2581 | 4500 | 6.2299 | - | - | - | - | - | | 0.2639 | 4600 | 6.1013 | - | - | - | - | - | | 0.2696 | 4700 | 5.6491 | - | - | - | - | - | | 0.2753 | 4800 | 5.8641 | - | - | - | - | - | | 0.2811 | 4900 | 5.4278 | - | - | - | - | - | | 0.2868 | 5000 | 5.7304 | - | - | - | - | - | | 0.2925 | 5100 | 5.4652 | - | - | - | - | - | | 0.2983 | 5200 | 5.9031 | - | - | - | - | - | | 0.3040 | 5300 | 6.1014 | - | - | - | - | - | | 0.3098 | 5400 | 5.9282 | - | - | - | - | - | | 0.3155 | 5500 | 5.6618 | - | - | - | - | - | | 0.3212 | 5600 | 5.3803 | - | - | - | - | - | | 0.3270 | 5700 | 5.5759 | - | - | - | - | - | | 0.3327 | 5800 | 5.6936 | - | - | - | - | - | | 0.3384 | 5900 | 5.7249 | - | - | - | - | - | | 0.3442 | 6000 | 5.5926 | - | - | - | - | - | | 0.3499 | 6100 | 5.6329 | - | - | - | - | - | | 0.3556 | 6200 | 5.7456 | - | - | - | - | - | | 0.3614 | 6300 | 5.1638 | - | - | - | - | - | | 0.3671 | 6400 | 5.3258 | - | - | - | - | - | | 0.3729 | 6500 | 5.1216 | - | - | - | - | - | | 0.3786 | 6600 | 5.7453 | - | - | - | - | - | | 0.3843 | 6700 | 4.9906 | - | - | - | - | - | | 0.3901 | 6800 | 5.1126 | - | - | - | - | - | | 0.3958 | 6900 | 5.2389 | - | - | - | - | - | | 0.4015 | 7000 | 5.1483 | - | - | - | - | - | | 0.4073 | 7100 | 5.6072 | - | - | - | - | - | | 0.4130 | 7200 | 5.2018 | - | - | - | - | - | | 0.4187 | 7300 | 5.4083 | - | - | - | - | - | | 0.4245 | 7400 | 5.1995 | - | - | - | - | - | | 0.4302 | 7500 | 5.5787 | - | - | - | - | - | | 0.4360 | 7600 | 4.9942 | - | - | - | - | - | | 0.4417 | 7700 | 4.9196 | - | - | - | - | - | | 0.4474 | 7800 | 5.3938 | - | - | - | - | - | | 0.4532 | 7900 | 5.381 | - | - | - | - | - | | 0.4589 | 8000 | 4.908 | - | - | - | - | - | | 0.4646 | 8100 | 4.8871 | - | - | - | - | - | | 0.4704 | 8200 | 5.2298 | - | - | - | - | - | | 0.4761 | 8300 | 4.6157 | - | - | - | - | - | | 0.4818 | 8400 | 5.0344 | - | - | - | - | - | | 0.4876 | 8500 | 5.0713 | - | - | - | - | - | | 0.4933 | 8600 | 5.1952 | - | - | - | - | - | | 0.4991 | 8700 | 5.5352 | - | - | - | - | - | | 0.5048 | 8800 | 5.1556 | - | - | - | - | - | | 0.5105 | 8900 | 5.2318 | - | - | - | - | - | | 0.5163 | 9000 | 4.7887 | - | - | - | - | - | | 0.5220 | 9100 | 4.868 | - | - | - | - | - | | 0.5277 | 9200 | 4.9544 | - | - | - | - | - | | 0.5335 | 9300 | 4.816 | - | - | - | - | - | | 0.5392 | 9400 | 4.8374 | - | - | - | - | - | | 0.5449 | 9500 | 5.3242 | - | - | - | - | - | | 0.5507 | 9600 | 4.9039 | - | - | - | - | - | | 0.5564 | 9700 | 5.2907 | - | - | - | - | - | | 0.5622 | 9800 | 5.4007 | - | - | - | - | - | | 0.5679 | 9900 | 5.3016 | - | - | - | - | - | | 0.5736 | 10000 | 5.3235 | - | - | - | - | - | | 0.5794 | 10100 | 5.1566 | - | - | - | - | - | | 0.5851 | 10200 | 5.1348 | - | - | - | - | - | | 0.5908 | 10300 | 5.4583 | - | - | - | - | - | | 0.5966 | 10400 | 4.9528 | - | - | - | - | - | | 0.6023 | 10500 | 5.0073 | - | - | - | - | - | | 0.6080 | 10600 | 5.0324 | - | - | - | - | - | | 0.6138 | 10700 | 5.4107 | - | - | - | - | - | | 0.6195 | 10800 | 5.3643 | - | - | - | - | - | | 0.6253 | 10900 | 5.1267 | - | - | - | - | - | | 0.6310 | 11000 | 5.0443 | - | - | - | - | - | | 0.6367 | 11100 | 5.2001 | - | - | - | - | - | | 0.6425 | 11200 | 4.8813 | - | - | - | - | - | | 0.6482 | 11300 | 5.4734 | - | - | - | - | - | | 0.6539 | 11400 | 5.0344 | - | - | - | - | - | | 0.6597 | 11500 | 5.5043 | - | - | - | - | - | | 0.6654 | 11600 | 4.6201 | - | - | - | - | - | | 0.6711 | 11700 | 5.4626 | - | - | - | - | - | | 0.6769 | 11800 | 5.3813 | - | - | - | - | - | | 0.6826 | 11900 | 4.626 | - | - | - | - | - | | 0.6883 | 12000 | 4.87 | - | - | - | - | - | | 0.6941 | 12100 | 5.0015 | - | - | - | - | - | | 0.6998 | 12200 | 4.962 | - | - | - | - | - | | 0.7056 | 12300 | 5.1613 | - | - | - | - | - | | 0.7113 | 12400 | 5.2074 | - | - | - | - | - | | 0.7170 | 12500 | 4.958 | - | - | - | - | - | | 0.7228 | 12600 | 4.4516 | - | - | - | - | - | | 0.7285 | 12700 | 4.8421 | - | - | - | - | - | | 0.7342 | 12800 | 4.9242 | - | - | - | - | - | | 0.7400 | 12900 | 4.9256 | - | - | - | - | - | | 0.7457 | 13000 | 4.8254 | - | - | - | - | - | | 0.7514 | 13100 | 4.5114 | - | - | - | - | - | | 0.7572 | 13200 | 7.7118 | - | - | - | - | - | | 0.7629 | 13300 | 7.0822 | - | - | - | - | - | | 0.7687 | 13400 | 6.8022 | - | - | - | - | - | | 0.7744 | 13500 | 6.7295 | - | - | - | - | - | | 0.7801 | 13600 | 6.0547 | - | - | - | - | - | | 0.7859 | 13700 | 6.5285 | - | - | - | - | - | | 0.7916 | 13800 | 6.2666 | - | - | - | - | - | | 0.7973 | 13900 | 6.1031 | - | - | - | - | - | | 0.8031 | 14000 | 5.9138 | - | - | - | - | - | | 0.8088 | 14100 | 5.6636 | - | - | - | - | - | | 0.8145 | 14200 | 5.7073 | - | - | - | - | - | | 0.8203 | 14300 | 5.7963 | - | - | - | - | - | | 0.8260 | 14400 | 5.7336 | - | - | - | - | - | | 0.8318 | 14500 | 5.8113 | - | - | - | - | - | | 0.8375 | 14600 | 5.6708 | - | - | - | - | - | | 0.8432 | 14700 | 5.4565 | - | - | - | - | - | | 0.8490 | 14800 | 5.4293 | - | - | - | - | - | | 0.8547 | 14900 | 5.4166 | - | - | - | - | - | | 0.8604 | 15000 | 5.3616 | - | - | - | - | - | | 0.8662 | 15100 | 5.1579 | - | - | - | - | - | | 0.8719 | 15200 | 5.3887 | - | - | - | - | - | | 0.8776 | 15300 | 5.346 | - | - | - | - | - | | 0.8834 | 15400 | 5.2762 | - | - | - | - | - | | 0.8891 | 15500 | 5.3417 | - | - | - | - | - | | 0.8949 | 15600 | 5.1607 | - | - | - | - | - | | 0.9006 | 15700 | 5.4493 | - | - | - | - | - | | 0.9063 | 15800 | 5.0268 | - | - | - | - | - | | 0.9121 | 15900 | 5.0612 | - | - | - | - | - | | 0.9178 | 16000 | 5.1471 | - | - | - | - | - | | 0.9235 | 16100 | 4.8275 | - | - | - | - | - | | 0.9293 | 16200 | 5.1464 | - | - | - | - | - | | 0.9350 | 16300 | 4.958 | - | - | - | - | - | | 0.9407 | 16400 | 5.1968 | - | - | - | - | - | | 0.9465 | 16500 | 4.7783 | - | - | - | - | - | | 0.9522 | 16600 | 5.0834 | - | - | - | - | - | | 0.9580 | 16700 | 4.9839 | - | - | - | - | - | | 0.9637 | 16800 | 5.0078 | - | - | - | - | - | | 0.9694 | 16900 | 5.1624 | - | - | - | - | - | | 0.9752 | 17000 | 5.2132 | - | - | - | - | - | | 0.9809 | 17100 | 4.9741 | - | - | - | - | - | | 0.9866 | 17200 | 4.96 | - | - | - | - | - | | 0.9924 | 17300 | 5.1834 | - | - | - | - | - | | 0.9981 | 17400 | 4.8955 | - | - | - | - | - | | 1.0 | 17433 | - | 0.6638 | 0.6702 | 0.6770 | 0.6556 | 0.6802 |
### 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} } ```