--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1115700 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-small-en-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 BAAI/bge-small-en-v1.5 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: 0.6831671531193453 name: Pearson Cosine - type: spearman_cosine value: 0.677143022633225 name: Spearman Cosine - type: pearson_manhattan value: 0.6891948944875336 name: Pearson Manhattan - type: spearman_manhattan value: 0.6892226446007472 name: Spearman Manhattan - type: pearson_euclidean value: 0.6916897298195501 name: Pearson Euclidean - type: spearman_euclidean value: 0.6916850273924392 name: Spearman Euclidean - type: pearson_dot value: 0.6418376172951465 name: Pearson Dot - type: spearman_dot value: 0.628581703082033 name: Spearman Dot - type: pearson_max value: 0.6916897298195501 name: Pearson Max - type: spearman_max value: 0.6916850273924392 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: 0.6753009254241098 name: Pearson Cosine - type: spearman_cosine value: 0.6731049071307844 name: Spearman Cosine - type: pearson_manhattan value: 0.6906782473185179 name: Pearson Manhattan - type: spearman_manhattan value: 0.6927883369656496 name: Spearman Manhattan - type: pearson_euclidean value: 0.6933649652149252 name: Pearson Euclidean - type: spearman_euclidean value: 0.694111832507592 name: Spearman Euclidean - type: pearson_dot value: 0.600449101550258 name: Pearson Dot - type: spearman_dot value: 0.5857671058687308 name: Spearman Dot - type: pearson_max value: 0.6933649652149252 name: Pearson Max - type: spearman_max value: 0.694111832507592 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: 0.6546200020168988 name: Pearson Cosine - type: spearman_cosine value: 0.6523958945855459 name: Spearman Cosine - type: pearson_manhattan value: 0.6837289470688535 name: Pearson Manhattan - type: spearman_manhattan value: 0.6796775815725002 name: Spearman Manhattan - type: pearson_euclidean value: 0.6861328219241016 name: Pearson Euclidean - type: spearman_euclidean value: 0.6815842202083926 name: Spearman Euclidean - type: pearson_dot value: 0.5120576666695955 name: Pearson Dot - type: spearman_dot value: 0.49141347385563683 name: Spearman Dot - type: pearson_max value: 0.6861328219241016 name: Pearson Max - type: spearman_max value: 0.6815842202083926 name: Spearman Max --- # SentenceTransformer based on BAAI/bge-small-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the Mollel/swahili-n_li-triplet-swh-eng dataset. 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 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': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("Mollel/MultiLinguSwahili-bge-small-en-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, 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 | 0.6832 | | **spearman_cosine** | **0.6771** | | pearson_manhattan | 0.6892 | | spearman_manhattan | 0.6892 | | pearson_euclidean | 0.6917 | | spearman_euclidean | 0.6917 | | pearson_dot | 0.6418 | | spearman_dot | 0.6286 | | pearson_max | 0.6917 | | spearman_max | 0.6917 | #### 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 | 0.6753 | | **spearman_cosine** | **0.6731** | | pearson_manhattan | 0.6907 | | spearman_manhattan | 0.6928 | | pearson_euclidean | 0.6934 | | spearman_euclidean | 0.6941 | | pearson_dot | 0.6004 | | spearman_dot | 0.5858 | | pearson_max | 0.6934 | | spearman_max | 0.6941 | #### 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 | 0.6546 | | **spearman_cosine** | **0.6524** | | pearson_manhattan | 0.6837 | | spearman_manhattan | 0.6797 | | pearson_euclidean | 0.6861 | | spearman_euclidean | 0.6816 | | pearson_dot | 0.5121 | | spearman_dot | 0.4914 | | pearson_max | 0.6861 | | spearman_max | 0.6816 | ## 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": [ 256, 128, 64 ], "matryoshka_weights": [ 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": [ 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `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`: 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`: 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 | Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-64_spearman_cosine | |:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:---------------------------:| | 0.0115 | 100 | 9.6847 | - | - | - | | 0.0229 | 200 | 8.5336 | - | - | - | | 0.0344 | 300 | 7.768 | - | - | - | | 0.0459 | 400 | 7.2049 | - | - | - | | 0.0574 | 500 | 6.9425 | - | - | - | | 0.0688 | 600 | 7.029 | - | - | - | | 0.0803 | 700 | 6.259 | - | - | - | | 0.0918 | 800 | 6.0939 | - | - | - | | 0.1032 | 900 | 5.991 | - | - | - | | 0.1147 | 1000 | 5.39 | - | - | - | | 0.1262 | 1100 | 5.3214 | - | - | - | | 0.1377 | 1200 | 5.1469 | - | - | - | | 0.1491 | 1300 | 4.901 | - | - | - | | 0.1606 | 1400 | 5.2725 | - | - | - | | 0.1721 | 1500 | 5.077 | - | - | - | | 0.1835 | 1600 | 4.8006 | - | - | - | | 0.1950 | 1700 | 4.5318 | - | - | - | | 0.2065 | 1800 | 4.48 | - | - | - | | 0.2180 | 1900 | 4.5752 | - | - | - | | 0.2294 | 2000 | 4.427 | - | - | - | | 0.2409 | 2100 | 4.4021 | - | - | - | | 0.2524 | 2200 | 4.5903 | - | - | - | | 0.2639 | 2300 | 4.4561 | - | - | - | | 0.2753 | 2400 | 4.372 | - | - | - | | 0.2868 | 2500 | 4.2698 | - | - | - | | 0.2983 | 2600 | 4.3954 | - | - | - | | 0.3097 | 2700 | 4.2697 | - | - | - | | 0.3212 | 2800 | 4.125 | - | - | - | | 0.3327 | 2900 | 4.3611 | - | - | - | | 0.3442 | 3000 | 4.2527 | - | - | - | | 0.3556 | 3100 | 4.1892 | - | - | - | | 0.3671 | 3200 | 4.0417 | - | - | - | | 0.3786 | 3300 | 3.9434 | - | - | - | | 0.3900 | 3400 | 3.9797 | - | - | - | | 0.4015 | 3500 | 3.9611 | - | - | - | | 0.4130 | 3600 | 4.04 | - | - | - | | 0.4245 | 3700 | 3.965 | - | - | - | | 0.4359 | 3800 | 3.778 | - | - | - | | 0.4474 | 3900 | 4.0624 | - | - | - | | 0.4589 | 4000 | 3.8972 | - | - | - | | 0.4703 | 4100 | 3.7882 | - | - | - | | 0.4818 | 4200 | 3.8048 | - | - | - | | 0.4933 | 4300 | 3.9253 | - | - | - | | 0.5048 | 4400 | 3.9832 | - | - | - | | 0.5162 | 4500 | 3.6644 | - | - | - | | 0.5277 | 4600 | 3.7353 | - | - | - | | 0.5392 | 4700 | 3.7768 | - | - | - | | 0.5506 | 4800 | 3.796 | - | - | - | | 0.5621 | 4900 | 3.875 | - | - | - | | 0.5736 | 5000 | 3.7856 | - | - | - | | 0.5851 | 5100 | 3.8898 | - | - | - | | 0.5965 | 5200 | 3.6327 | - | - | - | | 0.6080 | 5300 | 3.7727 | - | - | - | | 0.6195 | 5400 | 3.8582 | - | - | - | | 0.6310 | 5500 | 3.729 | - | - | - | | 0.6424 | 5600 | 3.7088 | - | - | - | | 0.6539 | 5700 | 3.8414 | - | - | - | | 0.6654 | 5800 | 3.7624 | - | - | - | | 0.6768 | 5900 | 3.8816 | - | - | - | | 0.6883 | 6000 | 3.7483 | - | - | - | | 0.6998 | 6100 | 3.7759 | - | - | - | | 0.7113 | 6200 | 3.6674 | - | - | - | | 0.7227 | 6300 | 3.6441 | - | - | - | | 0.7342 | 6400 | 3.7779 | - | - | - | | 0.7457 | 6500 | 3.6691 | - | - | - | | 0.7571 | 6600 | 3.7636 | - | - | - | | 0.7686 | 6700 | 3.7424 | - | - | - | | 0.7801 | 6800 | 3.4943 | - | - | - | | 0.7916 | 6900 | 3.5399 | - | - | - | | 0.8030 | 7000 | 3.3658 | - | - | - | | 0.8145 | 7100 | 3.2856 | - | - | - | | 0.8260 | 7200 | 3.3702 | - | - | - | | 0.8374 | 7300 | 3.3121 | - | - | - | | 0.8489 | 7400 | 3.2322 | - | - | - | | 0.8604 | 7500 | 3.1577 | - | - | - | | 0.8719 | 7600 | 3.1873 | - | - | - | | 0.8833 | 7700 | 3.1492 | - | - | - | | 0.8948 | 7800 | 3.2035 | - | - | - | | 0.9063 | 7900 | 3.1607 | - | - | - | | 0.9177 | 8000 | 3.1557 | - | - | - | | 0.9292 | 8100 | 3.0915 | - | - | - | | 0.9407 | 8200 | 3.1335 | - | - | - | | 0.9522 | 8300 | 3.14 | - | - | - | | 0.9636 | 8400 | 3.1422 | - | - | - | | 0.9751 | 8500 | 3.1923 | - | - | - | | 0.9866 | 8600 | 3.1085 | - | - | - | | 0.9980 | 8700 | 3.089 | - | - | - | | 1.0 | 8717 | - | 0.6731 | 0.6771 | 0.6524 | ### 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} } ```