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---
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](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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': 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:
```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("sartifyllc/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]
```
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| 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
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| 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
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| 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 |
<!--
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## 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
<details><summary>Click to expand</summary>
- `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
</details>
### 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
```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}
}
```
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