metadata

language:
  - sw
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
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 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("sartifyllc/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, and negative

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, and negative

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: 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

@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}
}