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Add new SentenceTransformer model.
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metadata
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 model finetuned from 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
  • Maximum Sequence Length: 256 tokens
  • Output Dimensionality: 384 tokens
  • Similarity Function: Cosine Similarity

Model Sources

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:

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/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

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

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

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

@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}
}