--- 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/all-MiniLM-L6-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/all-MiniLM-L6-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.6942864389866223 name: Pearson Cosine - type: spearman_cosine value: 0.6856061049537777 name: Spearman Cosine - type: pearson_manhattan value: 0.6885375818451587 name: Pearson Manhattan - type: spearman_manhattan value: 0.6872214410233022 name: Spearman Manhattan - type: pearson_euclidean value: 0.6914785578290242 name: Pearson Euclidean - type: spearman_euclidean value: 0.6905722127311041 name: Spearman Euclidean - type: pearson_dot value: 0.6799233396985102 name: Pearson Dot - type: spearman_dot value: 0.667743621858275 name: Spearman Dot - type: pearson_max value: 0.6942864389866223 name: Pearson Max - type: spearman_max value: 0.6905722127311041 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.6891584502617563 name: Pearson Cosine - type: spearman_cosine value: 0.6814103986417178 name: Spearman Cosine - type: pearson_manhattan value: 0.6968187377070036 name: Pearson Manhattan - type: spearman_manhattan value: 0.6920002958564649 name: Spearman Manhattan - type: pearson_euclidean value: 0.7000628001426884 name: Pearson Euclidean - type: spearman_euclidean value: 0.6960243670969477 name: Spearman Euclidean - type: pearson_dot value: 0.6364862920838279 name: Pearson Dot - type: spearman_dot value: 0.6189765115954626 name: Spearman Dot - type: pearson_max value: 0.7000628001426884 name: Pearson Max - type: spearman_max value: 0.6960243670969477 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.6782226699898293 name: Pearson Cosine - type: spearman_cosine value: 0.6755345411699644 name: Spearman Cosine - type: pearson_manhattan value: 0.6962074727926596 name: Pearson Manhattan - type: spearman_manhattan value: 0.689094339218281 name: Spearman Manhattan - type: pearson_euclidean value: 0.6996133052307816 name: Pearson Euclidean - type: spearman_euclidean value: 0.6937517032138506 name: Spearman Euclidean - type: pearson_dot value: 0.58122590177631 name: Pearson Dot - type: spearman_dot value: 0.5606971476688047 name: Spearman Dot - type: pearson_max value: 0.6996133052307816 name: Pearson Max - type: spearman_max value: 0.6937517032138506 name: Spearman Max --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sartifyllc/swahili-all-MiniLM-L6-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, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test-256` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.6943 | | **spearman_cosine** | **0.6856** | | pearson_manhattan | 0.6885 | | spearman_manhattan | 0.6872 | | pearson_euclidean | 0.6915 | | spearman_euclidean | 0.6906 | | pearson_dot | 0.6799 | | spearman_dot | 0.6677 | | pearson_max | 0.6943 | | spearman_max | 0.6906 | #### 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 | 0.6892 | | **spearman_cosine** | **0.6814** | | pearson_manhattan | 0.6968 | | spearman_manhattan | 0.692 | | pearson_euclidean | 0.7001 | | spearman_euclidean | 0.696 | | pearson_dot | 0.6365 | | spearman_dot | 0.619 | | pearson_max | 0.7001 | | spearman_max | 0.696 | #### 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 | 0.6782 | | **spearman_cosine** | **0.6755** | | pearson_manhattan | 0.6962 | | spearman_manhattan | 0.6891 | | pearson_euclidean | 0.6996 | | spearman_euclidean | 0.6938 | | pearson_dot | 0.5812 | | spearman_dot | 0.5607 | | pearson_max | 0.6996 | | spearman_max | 0.6938 | ## Training Details ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `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`: 64 - `per_device_eval_batch_size`: 64 - `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 | Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-64_spearman_cosine | |:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:---------------------------:| | 0.0229 | 100 | 12.9498 | - | - | - | | 0.0459 | 200 | 9.9003 | - | - | - | | 0.0688 | 300 | 8.6333 | - | - | - | | 0.0918 | 400 | 8.0124 | - | - | - | | 0.1147 | 500 | 7.2322 | - | - | - | | 0.1376 | 600 | 6.936 | - | - | - | | 0.1606 | 700 | 7.2855 | - | - | - | | 0.1835 | 800 | 6.5985 | - | - | - | | 0.2065 | 900 | 6.4369 | - | - | - | | 0.2294 | 1000 | 6.2767 | - | - | - | | 0.2524 | 1100 | 6.4011 | - | - | - | | 0.2753 | 1200 | 6.1288 | - | - | - | | 0.2982 | 1300 | 6.1466 | - | - | - | | 0.3212 | 1400 | 5.9279 | - | - | - | | 0.3441 | 1500 | 5.8959 | - | - | - | | 0.3671 | 1600 | 5.5911 | - | - | - | | 0.3900 | 1700 | 5.5258 | - | - | - | | 0.4129 | 1800 | 5.5835 | - | - | - | | 0.4359 | 1900 | 5.4701 | - | - | - | | 0.4588 | 2000 | 5.3888 | - | - | - | | 0.4818 | 2100 | 5.4474 | - | - | - | | 0.5047 | 2200 | 5.1465 | - | - | - | | 0.5276 | 2300 | 5.28 | - | - | - | | 0.5506 | 2400 | 5.4184 | - | - | - | | 0.5735 | 2500 | 5.3811 | - | - | - | | 0.5965 | 2600 | 5.2171 | - | - | - | | 0.6194 | 2700 | 5.3212 | - | - | - | | 0.6423 | 2800 | 5.2493 | - | - | - | | 0.6653 | 2900 | 5.459 | - | - | - | | 0.6882 | 3000 | 5.068 | - | - | - | | 0.7112 | 3100 | 5.1415 | - | - | - | | 0.7341 | 3200 | 5.0764 | - | - | - | | 0.7571 | 3300 | 6.1606 | - | - | - | | 0.7800 | 3400 | 6.1028 | - | - | - | | 0.8029 | 3500 | 5.7441 | - | - | - | | 0.8259 | 3600 | 5.7148 | - | - | - | | 0.8488 | 3700 | 5.4799 | - | - | - | | 0.8718 | 3800 | 5.4396 | - | - | - | | 0.8947 | 3900 | 5.3519 | - | - | - | | 0.9176 | 4000 | 5.2394 | - | - | - | | 0.9406 | 4100 | 5.2311 | - | - | - | | 0.9635 | 4200 | 5.3486 | - | - | - | | 0.9865 | 4300 | 5.215 | - | - | - | | 1.0 | 4359 | - | 0.6814 | 0.6856 | 0.6755 | ### 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} } ```