SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a sentence-transformers model finetuned from 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
- 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
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
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:
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("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
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
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
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
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 7 tokens
- mean: 15.18 tokens
- max: 80 tokens
- min: 5 tokens
- mean: 18.53 tokens
- max: 52 tokens
- min: 5 tokens
- mean: 17.8 tokens
- max: 53 tokens
- 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
with these parameters:{ "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
, andnegative
- Approximate statistics based on the first 1000 samples:
anchor positive negative type string string string details - min: 6 tokens
- mean: 26.43 tokens
- max: 94 tokens
- min: 5 tokens
- mean: 13.37 tokens
- max: 65 tokens
- min: 5 tokens
- mean: 14.7 tokens
- max: 54 tokens
- 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
with these parameters:{ "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
: 64per_device_eval_batch_size
: 64learning_rate
: 2e-05num_train_epochs
: 1warmup_ratio
: 0.1bf16
: Truebatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseprediction_loss_only
: Trueper_device_train_batch_size
: 64per_device_eval_batch_size
: 64per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_sampler
: no_duplicatesmulti_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
@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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Model tree for Mollel/MultiLinguSwahili-bge-small-en-v1.5-nli-matryoshka
Base model
BAAI/bge-small-en-v1.5
Finetuned
this model
Evaluation results
- Pearson Cosine on sts test 256self-reported0.683
- Spearman Cosine on sts test 256self-reported0.677
- Pearson Manhattan on sts test 256self-reported0.689
- Spearman Manhattan on sts test 256self-reported0.689
- Pearson Euclidean on sts test 256self-reported0.692
- Spearman Euclidean on sts test 256self-reported0.692
- Pearson Dot on sts test 256self-reported0.642
- Spearman Dot on sts test 256self-reported0.629
- Pearson Max on sts test 256self-reported0.692
- Spearman Max on sts test 256self-reported0.692