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sartifyllc
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bge-base-swahili-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:282883
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
text-embeddings-inference
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BGE base Swahili Matryoshka

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. 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: BAAI/bge-base-en-v1.5
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sartifyllc/bge-base-swahili-matryoshka")
# Run inference
sentences = [
    'Kuna masuala ya sera.',
    'Masuala ya sera ya mbinu nyingi na maombi.',
    'Mwanamke mwenye makunyanzi sana akishikilia miwani yake na kutembea kwenye barabara ya jiji.',
]
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

Information Retrieval

Metric Value
cosine_accuracy@1 0.268
cosine_accuracy@3 0.35
cosine_accuracy@5 0.3859
cosine_accuracy@10 0.4332
cosine_precision@1 0.268
cosine_precision@3 0.1167
cosine_precision@5 0.0772
cosine_precision@10 0.0433
cosine_recall@1 0.268
cosine_recall@3 0.35
cosine_recall@5 0.3859
cosine_recall@10 0.4332
cosine_ndcg@10 0.3461
cosine_mrr@10 0.3188
cosine_map@100 0.3252

Information Retrieval

Metric Value
cosine_accuracy@1 0.2655
cosine_accuracy@3 0.346
cosine_accuracy@5 0.3811
cosine_accuracy@10 0.4291
cosine_precision@1 0.2655
cosine_precision@3 0.1153
cosine_precision@5 0.0762
cosine_precision@10 0.0429
cosine_recall@1 0.2655
cosine_recall@3 0.346
cosine_recall@5 0.3811
cosine_recall@10 0.4291
cosine_ndcg@10 0.3425
cosine_mrr@10 0.3154
cosine_map@100 0.3217

Information Retrieval

Metric Value
cosine_accuracy@1 0.2576
cosine_accuracy@3 0.3379
cosine_accuracy@5 0.3717
cosine_accuracy@10 0.4195
cosine_precision@1 0.2576
cosine_precision@3 0.1126
cosine_precision@5 0.0743
cosine_precision@10 0.042
cosine_recall@1 0.2576
cosine_recall@3 0.3379
cosine_recall@5 0.3717
cosine_recall@10 0.4195
cosine_ndcg@10 0.3339
cosine_mrr@10 0.3071
cosine_map@100 0.3134

Information Retrieval

Metric Value
cosine_accuracy@1 0.2449
cosine_accuracy@3 0.3219
cosine_accuracy@5 0.3557
cosine_accuracy@10 0.4029
cosine_precision@1 0.2449
cosine_precision@3 0.1073
cosine_precision@5 0.0711
cosine_precision@10 0.0403
cosine_recall@1 0.2449
cosine_recall@3 0.3219
cosine_recall@5 0.3557
cosine_recall@10 0.4029
cosine_ndcg@10 0.3191
cosine_mrr@10 0.2929
cosine_map@100 0.2992

Information Retrieval

Metric Value
cosine_accuracy@1 0.2194
cosine_accuracy@3 0.2918
cosine_accuracy@5 0.3235
cosine_accuracy@10 0.3699
cosine_precision@1 0.2194
cosine_precision@3 0.0973
cosine_precision@5 0.0647
cosine_precision@10 0.037
cosine_recall@1 0.2194
cosine_recall@3 0.2918
cosine_recall@5 0.3235
cosine_recall@10 0.3699
cosine_ndcg@10 0.2897
cosine_mrr@10 0.2647
cosine_map@100 0.271

Training Details

Training Dataset

Unnamed Dataset

  • Size: 282,883 training samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 20.1 tokens
    • max: 78 tokens
    • min: 6 tokens
    • mean: 38.64 tokens
    • max: 184 tokens
  • Samples:
    positive anchor
    Alingoja mtu huyo mwingine arudi. Ca'daan alingoja hadi alasiri yote mtu huyo atoke tena.
    Sheria hiyo huanzisha mfululizo wa ukaguzi wa majaribio. Sheria hiyo pia inatoa sheria ya kudhibiti kwa ajili ya mashirika fulani ambayo yanahitaji kuandaa taarifa za kifedha za mashirika yote na kuzisimamisha kwa wakaguzi wa jumla.
    Mbwa anakimbia na kuruka nje. Mbwa mwenye rangi ya kahawia anaruka na kukimbia shambani.
  • Loss: MatryoshkaLoss with these parameters:
    {
    "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

  • eval_strategy: epoch
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 16
  • gradient_accumulation_steps: 16
  • learning_rate: 2e-05
  • num_train_epochs: 4
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.1
  • bf16: True
  • tf32: True
  • load_best_model_at_end: True
  • optim: adamw_torch_fused
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: epoch
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 16
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 16
  • 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: 4
  • max_steps: -1
  • lr_scheduler_type: cosine
  • 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
  • restore_callback_states_from_checkpoint: 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: True
  • 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: True
  • 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, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • 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_eval_metrics: False
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss dim_128_cosine_map@100 dim_256_cosine_map@100 dim_512_cosine_map@100 dim_64_cosine_map@100 dim_768_cosine_map@100
0.0181 10 9.7089 - - - - -
0.0362 20 9.2806 - - - - -
0.0543 30 8.8905 - - - - -
0.0724 40 7.9651 - - - - -
0.0905 50 7.4201 - - - - -
0.1086 60 6.8346 - - - - -
0.1267 70 6.515 - - - - -
0.1448 80 6.2009 - - - - -
0.1629 90 5.8256 - - - - -
0.1810 100 5.549 - - - - -
0.1991 110 5.1667 - - - - -
0.2172 120 5.2684 - - - - -
0.2353 130 5.0678 - - - - -
0.2534 140 4.9183 - - - - -
0.2715 150 4.844 - - - - -
0.2896 160 4.5427 - - - - -
0.3077 170 4.3324 - - - - -
0.3258 180 4.4963 - - - - -
0.3439 190 4.1704 - - - - -
0.3620 200 4.1285 - - - - -
0.3800 210 4.0235 - - - - -
0.3981 220 4.0738 - - - - -
0.4162 230 3.9132 - - - - -
0.4343 240 3.9682 - - - - -
0.4524 250 3.7542 - - - - -
0.4705 260 3.6508 - - - - -
0.4886 270 3.7596 - - - - -
0.5067 280 3.5596 - - - - -
0.5248 290 3.5077 - - - - -
0.5429 300 3.3831 - - - - -
0.5610 310 3.4 - - - - -
0.5791 320 3.296 - - - - -
0.5972 330 3.3646 - - - - -
0.6153 340 3.3533 - - - - -
0.6334 350 3.2171 - - - - -
0.6515 360 3.2324 - - - - -
0.6696 370 3.1544 - - - - -
0.6877 380 3.3393 - - - - -
0.7058 390 3.0864 - - - - -
0.7239 400 3.1069 - - - - -
0.7420 410 3.0722 - - - - -
0.7601 420 3.1446 - - - - -
0.7782 430 3.0847 - - - - -
0.7963 440 3.0331 - - - - -
0.8144 450 3.0197 - - - - -
0.8325 460 2.9667 - - - - -
0.8506 470 2.8331 - - - - -
0.8687 480 2.9333 - - - - -
0.8868 490 2.8714 - - - - -
0.9049 500 2.8578 - - - - -
0.9230 510 2.9689 - - - - -
0.9411 520 2.7977 - - - - -
0.9592 530 2.9832 - - - - -
0.9773 540 2.9761 - - - - -
0.9954 550 2.7711 - - - - -
0.9990 552 - 0.2772 0.2954 0.3052 0.2445 0.3080
1.0135 560 2.7194 - - - - -
1.0316 570 2.8489 - - - - -
1.0497 580 2.6559 - - - - -
1.0678 590 2.6239 - - - - -
1.0859 600 2.7081 - - - - -
1.1039 610 2.6581 - - - - -
1.1220 620 2.7709 - - - - -
1.1401 630 2.6191 - - - - -
1.1582 640 2.6712 - - - - -
1.1763 650 2.5445 - - - - -
1.1944 660 2.5264 - - - - -
1.2125 670 2.5782 - - - - -
1.2306 680 2.5652 - - - - -
1.2487 690 2.6229 - - - - -
1.2668 700 2.5557 - - - - -
1.2849 710 2.5251 - - - - -
1.3030 720 2.4555 - - - - -
1.3211 730 2.5335 - - - - -
1.3392 740 2.5027 - - - - -
1.3573 750 2.3569 - - - - -
1.3754 760 2.4255 - - - - -
1.3935 770 2.4626 - - - - -
1.4116 780 2.363 - - - - -
1.4297 790 2.4 - - - - -
1.4478 800 2.3317 - - - - -
1.4659 810 2.2922 - - - - -
1.4840 820 2.4086 - - - - -
1.5021 830 2.3166 - - - - -
1.5202 840 2.3401 - - - - -
1.5383 850 2.1951 - - - - -
1.5564 860 2.214 - - - - -
1.5745 870 2.1859 - - - - -
1.5926 880 2.3605 - - - - -
1.6107 890 2.2528 - - - - -
1.6288 900 2.2759 - - - - -
1.6469 910 2.1458 - - - - -
1.6650 920 2.187 - - - - -
1.6831 930 2.3406 - - - - -
1.7012 940 2.2151 - - - - -
1.7193 950 2.2971 - - - - -
1.7374 960 2.2736 - - - - -
1.7555 970 2.2329 - - - - -
1.7736 980 2.2602 - - - - -
1.7917 990 2.2402 - - - - -
1.8098 1000 2.1971 - - - - -
1.8278 1010 2.1642 - - - - -
1.8459 1020 2.1274 - - - - -
1.8640 1030 2.1833 - - - - -
1.8821 1040 2.156 - - - - -
1.9002 1050 2.1252 - - - - -
1.9183 1060 2.161 - - - - -
1.9364 1070 2.1267 - - - - -
1.9545 1080 2.2017 - - - - -
1.9726 1090 2.3044 - - - - -
1.9907 1100 2.161 - - - - -
1.9998 1105 - 0.2928 0.3085 0.3165 0.2632 0.3204
2.0088 1110 2.0594 - - - - -
2.0269 1120 2.2277 - - - - -
2.0450 1130 2.1591 - - - - -
2.0631 1140 2.0396 - - - - -
2.0812 1150 2.1007 - - - - -
2.0993 1160 2.0705 - - - - -
2.1174 1170 2.0894 - - - - -
2.1355 1180 2.0677 - - - - -
2.1536 1190 2.0893 - - - - -
2.1717 1200 1.984 - - - - -
2.1898 1210 1.9206 - - - - -
2.2079 1220 2.132 - - - - -
2.2260 1230 2.0457 - - - - -
2.2441 1240 2.1428 - - - - -
2.2622 1250 2.1116 - - - - -
2.2803 1260 1.993 - - - - -
2.2984 1270 2.0181 - - - - -
2.3165 1280 1.9742 - - - - -
2.3346 1290 2.081 - - - - -
2.3527 1300 1.9107 - - - - -
2.3708 1310 1.9507 - - - - -
2.3889 1320 1.9844 - - - - -
2.4070 1330 2.0035 - - - - -
2.4251 1340 1.9121 - - - - -
2.4432 1350 2.0057 - - - - -
2.4613 1360 1.9323 - - - - -
2.4794 1370 1.9216 - - - - -
2.4975 1380 1.995 - - - - -
2.5156 1390 1.9285 - - - - -
2.5337 1400 1.8886 - - - - -
2.5517 1410 1.8298 - - - - -
2.5698 1420 1.8452 - - - - -
2.5879 1430 1.9488 - - - - -
2.6060 1440 1.8928 - - - - -
2.6241 1450 2.0101 - - - - -
2.6422 1460 1.7591 - - - - -
2.6603 1470 1.9177 - - - - -
2.6784 1480 1.9329 - - - - -
2.6965 1490 1.8978 - - - - -
2.7146 1500 1.9589 - - - - -
2.7327 1510 1.9744 - - - - -
2.7508 1520 1.9272 - - - - -
2.7689 1530 1.9234 - - - - -
2.7870 1540 1.9667 - - - - -
2.8051 1550 1.853 - - - - -
2.8232 1560 1.9191 - - - - -
2.8413 1570 1.8083 - - - - -
2.8594 1580 1.8543 - - - - -
2.8775 1590 1.9091 - - - - -
2.8956 1600 1.8079 - - - - -
2.9137 1610 1.8992 - - - - -
2.9318 1620 1.8742 - - - - -
2.9499 1630 1.9313 - - - - -
2.9680 1640 1.9832 - - - - -
2.9861 1650 1.9037 - - - - -
2.9988 1657 - 0.2982 0.3130 0.3211 0.2697 0.3247
3.0042 1660 1.7924 - - - - -
3.0223 1670 1.9677 - - - - -
3.0404 1680 1.9123 - - - - -
3.0585 1690 1.7691 - - - - -
3.0766 1700 1.8822 - - - - -
3.0947 1710 1.8543 - - - - -
3.1128 1720 1.8127 - - - - -
3.1309 1730 1.8844 - - - - -
3.1490 1740 1.911 - - - - -
3.1671 1750 1.7695 - - - - -
3.1852 1760 1.8134 - - - - -
3.2033 1770 1.7794 - - - - -
3.2214 1780 1.8851 - - - - -
3.2395 1790 1.8381 - - - - -
3.2576 1800 1.9184 - - - - -
3.2756 1810 1.8074 - - - - -
3.2937 1820 1.8236 - - - - -
3.3118 1830 1.8203 - - - - -
3.3299 1840 1.8874 - - - - -
3.3480 1850 1.7457 - - - - -
3.3661 1860 1.7933 - - - - -
3.3842 1870 1.759 - - - - -
3.4023 1880 1.8514 - - - - -
3.4204 1890 1.8163 - - - - -
3.4385 1900 1.8299 - - - - -
3.4566 1910 1.8112 - - - - -
3.4747 1920 1.7446 - - - - -
3.4928 1930 1.8314 - - - - -
3.5109 1940 1.742 - - - - -
3.5290 1950 1.7519 - - - - -
3.5471 1960 1.722 - - - - -
3.5652 1970 1.7454 - - - - -
3.5833 1980 1.7875 - - - - -
3.6014 1990 1.7596 - - - - -
3.6195 2000 1.8348 - - - - -
3.6376 2010 1.6954 - - - - -
3.6557 2020 1.7334 - - - - -
3.6738 2030 1.8318 - - - - -
3.6919 2040 1.7982 - - - - -
3.7100 2050 1.7987 - - - - -
3.7281 2060 1.8402 - - - - -
3.7462 2070 1.8569 - - - - -
3.7643 2080 1.8285 - - - - -
3.7824 2090 1.8652 - - - - -
3.8005 2100 1.7731 - - - - -
3.8186 2110 1.8697 - - - - -
3.8367 2120 1.6953 - - - - -
3.8548 2130 1.7493 - - - - -
3.8729 2140 1.8031 - - - - -
3.8910 2150 1.7053 - - - - -
3.9091 2160 1.8436 - - - - -
3.9272 2170 1.7572 - - - - -
3.9453 2180 1.7797 - - - - -
3.9634 2190 1.8827 - - - - -
3.9815 2200 1.8678 - - - - -
3.9959 2208 - 0.2992 0.3134 0.3217 0.271 0.3252
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.1.2+cu121
  • Accelerate: 0.31.0
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers 

@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 

@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 

@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}
}
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