sentence-transformers safetensors electra sentence-similarity feature-extraction generated_from_trainer dataset_size:557850 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:UBC-NLP/serengeti-E250 base_model:finetune:UBC-NLP/serengeti-E250 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: UBC-NLP/serengeti-E250
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
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.
model-index:
- name: SentenceTransformer based on UBC-NLP/serengeti-E250
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.7113368462970326
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.706531149090894
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7134349154531519
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7023005843725415
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7137962920501839
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7020941994285994
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.3920803758314358
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3601086266312748
      name: Spearman Dot
    - type: pearson_max
      value: 0.7137962920501839
      name: Pearson Max
    - type: spearman_max
      value: 0.706531149090894
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.7090618585285485
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7045766195278508
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7129955390384859
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7021695501159393
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7138697740168334
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7032055408694606
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.39352767760073326
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3628376619678567
      name: Spearman Dot
    - type: pearson_max
      value: 0.7138697740168334
      name: Pearson Max
    - type: spearman_max
      value: 0.7045766195278508
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.7067837420770313
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7044452613349608
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7137425083925593
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7032345257234871
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7146861583047366
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7039212190752775
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.37462153895392747
      name: Pearson Dot
    - type: spearman_dot
      value: 0.34441190254194326
      name: Spearman Dot
    - type: pearson_max
      value: 0.7146861583047366
      name: Pearson Max
    - type: spearman_max
      value: 0.7044452613349608
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.7046839100746249
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7050559450173808
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7120431790616113
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7010054121016321
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7132280398983044
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.701626975970973
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.35455409787695585
      name: Pearson Dot
    - type: spearman_dot
      value: 0.32292034736383524
      name: Spearman Dot
    - type: pearson_max
      value: 0.7132280398983044
      name: Pearson Max
    - type: spearman_max
      value: 0.7050559450173808
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.7012310578605567
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7044132231714119
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7091211798265005
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6972792688781575
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7103033981031003
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6985716335223231
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.3379821887901175
      name: Pearson Dot
    - type: spearman_dot
      value: 0.30513652558145304
      name: Spearman Dot
    - type: pearson_max
      value: 0.7103033981031003
      name: Pearson Max
    - type: spearman_max
      value: 0.7044132231714119
      name: Spearman Max
---

# SentenceTransformer based on UBC-NLP/serengeti-E250

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UBC-NLP/serengeti-E250](https://huggingface.co/UBC-NLP/serengeti-E250). 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:** [UBC-NLP/serengeti-E250](https://huggingface.co/UBC-NLP/serengeti-E250) <!-- at revision 41b5b8b6179c4af2859768cbf4f0f03e928d651d -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: ElectraModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)
```

## 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-serengeti-E250-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, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7113     |
| **spearman_cosine** | **0.7065** |
| pearson_manhattan   | 0.7134     |
| spearman_manhattan  | 0.7023     |
| pearson_euclidean   | 0.7138     |
| spearman_euclidean  | 0.7021     |
| pearson_dot         | 0.3921     |
| spearman_dot        | 0.3601     |
| pearson_max         | 0.7138     |
| spearman_max        | 0.7065     |

#### Semantic Similarity
* Dataset: `sts-test-512`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7091     |
| **spearman_cosine** | **0.7046** |
| pearson_manhattan   | 0.713      |
| spearman_manhattan  | 0.7022     |
| pearson_euclidean   | 0.7139     |
| spearman_euclidean  | 0.7032     |
| pearson_dot         | 0.3935     |
| spearman_dot        | 0.3628     |
| pearson_max         | 0.7139     |
| spearman_max        | 0.7046     |

#### 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      | 0.7068     |
| **spearman_cosine** | **0.7044** |
| pearson_manhattan   | 0.7137     |
| spearman_manhattan  | 0.7032     |
| pearson_euclidean   | 0.7147     |
| spearman_euclidean  | 0.7039     |
| pearson_dot         | 0.3746     |
| spearman_dot        | 0.3444     |
| pearson_max         | 0.7147     |
| spearman_max        | 0.7044     |

#### 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      | 0.7047     |
| **spearman_cosine** | **0.7051** |
| pearson_manhattan   | 0.712      |
| spearman_manhattan  | 0.701      |
| pearson_euclidean   | 0.7132     |
| spearman_euclidean  | 0.7016     |
| pearson_dot         | 0.3546     |
| spearman_dot        | 0.3229     |
| pearson_max         | 0.7132     |
| spearman_max        | 0.7051     |

#### 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      | 0.7012     |
| **spearman_cosine** | **0.7044** |
| pearson_manhattan   | 0.7091     |
| spearman_manhattan  | 0.6973     |
| pearson_euclidean   | 0.7103     |
| spearman_euclidean  | 0.6986     |
| pearson_dot         | 0.338      |
| spearman_dot        | 0.3051     |
| pearson_max         | 0.7103     |
| spearman_max        | 0.7044     |

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## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: 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`: 16
- `per_device_eval_batch_size`: 16
- `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

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step  | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0057 | 100   | 25.7713       | -                            | -                            | -                            | -                           | -                            |
| 0.0115 | 200   | 20.7886       | -                            | -                            | -                            | -                           | -                            |
| 0.0172 | 300   | 17.0398       | -                            | -                            | -                            | -                           | -                            |
| 0.0229 | 400   | 15.3913       | -                            | -                            | -                            | -                           | -                            |
| 0.0287 | 500   | 14.0214       | -                            | -                            | -                            | -                           | -                            |
| 0.0344 | 600   | 12.2125       | -                            | -                            | -                            | -                           | -                            |
| 0.0402 | 700   | 10.3033       | -                            | -                            | -                            | -                           | -                            |
| 0.0459 | 800   | 9.3822        | -                            | -                            | -                            | -                           | -                            |
| 0.0516 | 900   | 8.9276        | -                            | -                            | -                            | -                           | -                            |
| 0.0574 | 1000  | 8.552         | -                            | -                            | -                            | -                           | -                            |
| 0.0631 | 1100  | 8.6293        | -                            | -                            | -                            | -                           | -                            |
| 0.0688 | 1200  | 8.5353        | -                            | -                            | -                            | -                           | -                            |
| 0.0746 | 1300  | 8.6431        | -                            | -                            | -                            | -                           | -                            |
| 0.0803 | 1400  | 8.3192        | -                            | -                            | -                            | -                           | -                            |
| 0.0860 | 1500  | 7.1834        | -                            | -                            | -                            | -                           | -                            |
| 0.0918 | 1600  | 6.7834        | -                            | -                            | -                            | -                           | -                            |
| 0.0975 | 1700  | 6.4758        | -                            | -                            | -                            | -                           | -                            |
| 0.1033 | 1800  | 6.756         | -                            | -                            | -                            | -                           | -                            |
| 0.1090 | 1900  | 7.807         | -                            | -                            | -                            | -                           | -                            |
| 0.1147 | 2000  | 6.8836        | -                            | -                            | -                            | -                           | -                            |
| 0.1205 | 2100  | 6.9948        | -                            | -                            | -                            | -                           | -                            |
| 0.1262 | 2200  | 6.5031        | -                            | -                            | -                            | -                           | -                            |
| 0.1319 | 2300  | 6.3596        | -                            | -                            | -                            | -                           | -                            |
| 0.1377 | 2400  | 6.0257        | -                            | -                            | -                            | -                           | -                            |
| 0.1434 | 2500  | 5.9757        | -                            | -                            | -                            | -                           | -                            |
| 0.1491 | 2600  | 5.464         | -                            | -                            | -                            | -                           | -                            |
| 0.1549 | 2700  | 5.6518        | -                            | -                            | -                            | -                           | -                            |
| 0.1606 | 2800  | 6.2899        | -                            | -                            | -                            | -                           | -                            |
| 0.1664 | 2900  | 6.4876        | -                            | -                            | -                            | -                           | -                            |
| 0.1721 | 3000  | 6.9466        | -                            | -                            | -                            | -                           | -                            |
| 0.1778 | 3100  | 6.8439        | -                            | -                            | -                            | -                           | -                            |
| 0.1836 | 3200  | 6.2545        | -                            | -                            | -                            | -                           | -                            |
| 0.1893 | 3300  | 5.9795        | -                            | -                            | -                            | -                           | -                            |
| 0.1950 | 3400  | 5.3904        | -                            | -                            | -                            | -                           | -                            |
| 0.2008 | 3500  | 6.2798        | -                            | -                            | -                            | -                           | -                            |
| 0.2065 | 3600  | 5.6882        | -                            | -                            | -                            | -                           | -                            |
| 0.2122 | 3700  | 6.195         | -                            | -                            | -                            | -                           | -                            |
| 0.2180 | 3800  | 5.8728        | -                            | -                            | -                            | -                           | -                            |
| 0.2237 | 3900  | 6.2428        | -                            | -                            | -                            | -                           | -                            |
| 0.2294 | 4000  | 5.801         | -                            | -                            | -                            | -                           | -                            |
| 0.2352 | 4100  | 5.6918        | -                            | -                            | -                            | -                           | -                            |
| 0.2409 | 4200  | 5.3977        | -                            | -                            | -                            | -                           | -                            |
| 0.2467 | 4300  | 5.8792        | -                            | -                            | -                            | -                           | -                            |
| 0.2524 | 4400  | 5.9297        | -                            | -                            | -                            | -                           | -                            |
| 0.2581 | 4500  | 6.161         | -                            | -                            | -                            | -                           | -                            |
| 0.2639 | 4600  | 5.6571        | -                            | -                            | -                            | -                           | -                            |
| 0.2696 | 4700  | 5.5849        | -                            | -                            | -                            | -                           | -                            |
| 0.2753 | 4800  | 5.6382        | -                            | -                            | -                            | -                           | -                            |
| 0.2811 | 4900  | 5.2978        | -                            | -                            | -                            | -                           | -                            |
| 0.2868 | 5000  | 5.108         | -                            | -                            | -                            | -                           | -                            |
| 0.2925 | 5100  | 5.1158        | -                            | -                            | -                            | -                           | -                            |
| 0.2983 | 5200  | 5.6218        | -                            | -                            | -                            | -                           | -                            |
| 0.3040 | 5300  | 5.643         | -                            | -                            | -                            | -                           | -                            |
| 0.3098 | 5400  | 5.6894        | -                            | -                            | -                            | -                           | -                            |
| 0.3155 | 5500  | 5.373         | -                            | -                            | -                            | -                           | -                            |
| 0.3212 | 5600  | 5.0673        | -                            | -                            | -                            | -                           | -                            |
| 0.3270 | 5700  | 5.1915        | -                            | -                            | -                            | -                           | -                            |
| 0.3327 | 5800  | 5.3705        | -                            | -                            | -                            | -                           | -                            |
| 0.3384 | 5900  | 5.6432        | -                            | -                            | -                            | -                           | -                            |
| 0.3442 | 6000  | 5.2567        | -                            | -                            | -                            | -                           | -                            |
| 0.3499 | 6100  | 5.4516        | -                            | -                            | -                            | -                           | -                            |
| 0.3556 | 6200  | 5.4844        | -                            | -                            | -                            | -                           | -                            |
| 0.3614 | 6300  | 4.8238        | -                            | -                            | -                            | -                           | -                            |
| 0.3671 | 6400  | 4.8271        | -                            | -                            | -                            | -                           | -                            |
| 0.3729 | 6500  | 4.9863        | -                            | -                            | -                            | -                           | -                            |
| 0.3786 | 6600  | 5.4894        | -                            | -                            | -                            | -                           | -                            |
| 0.3843 | 6700  | 4.95          | -                            | -                            | -                            | -                           | -                            |
| 0.3901 | 6800  | 5.0881        | -                            | -                            | -                            | -                           | -                            |
| 0.3958 | 6900  | 5.249         | -                            | -                            | -                            | -                           | -                            |
| 0.4015 | 7000  | 5.0082        | -                            | -                            | -                            | -                           | -                            |
| 0.4073 | 7100  | 5.5064        | -                            | -                            | -                            | -                           | -                            |
| 0.4130 | 7200  | 5.0885        | -                            | -                            | -                            | -                           | -                            |
| 0.4187 | 7300  | 5.0321        | -                            | -                            | -                            | -                           | -                            |
| 0.4245 | 7400  | 4.8212        | -                            | -                            | -                            | -                           | -                            |
| 0.4302 | 7500  | 5.4231        | -                            | -                            | -                            | -                           | -                            |
| 0.4360 | 7600  | 4.7687        | -                            | -                            | -                            | -                           | -                            |
| 0.4417 | 7700  | 4.5707        | -                            | -                            | -                            | -                           | -                            |
| 0.4474 | 7800  | 5.2229        | -                            | -                            | -                            | -                           | -                            |
| 0.4532 | 7900  | 5.2446        | -                            | -                            | -                            | -                           | -                            |
| 0.4589 | 8000  | 4.682         | -                            | -                            | -                            | -                           | -                            |
| 0.4646 | 8100  | 4.888         | -                            | -                            | -                            | -                           | -                            |
| 0.4704 | 8200  | 5.0496        | -                            | -                            | -                            | -                           | -                            |
| 0.4761 | 8300  | 4.7089        | -                            | -                            | -                            | -                           | -                            |
| 0.4818 | 8400  | 4.9567        | -                            | -                            | -                            | -                           | -                            |
| 0.4876 | 8500  | 4.7913        | -                            | -                            | -                            | -                           | -                            |
| 0.4933 | 8600  | 4.8904        | -                            | -                            | -                            | -                           | -                            |
| 0.4991 | 8700  | 5.247         | -                            | -                            | -                            | -                           | -                            |
| 0.5048 | 8800  | 4.8254        | -                            | -                            | -                            | -                           | -                            |
| 0.5105 | 8900  | 4.973         | -                            | -                            | -                            | -                           | -                            |
| 0.5163 | 9000  | 4.6657        | -                            | -                            | -                            | -                           | -                            |
| 0.5220 | 9100  | 4.9224        | -                            | -                            | -                            | -                           | -                            |
| 0.5277 | 9200  | 4.8163        | -                            | -                            | -                            | -                           | -                            |
| 0.5335 | 9300  | 4.3673        | -                            | -                            | -                            | -                           | -                            |
| 0.5392 | 9400  | 4.6509        | -                            | -                            | -                            | -                           | -                            |
| 0.5449 | 9500  | 5.0667        | -                            | -                            | -                            | -                           | -                            |
| 0.5507 | 9600  | 4.8771        | -                            | -                            | -                            | -                           | -                            |
| 0.5564 | 9700  | 5.1056        | -                            | -                            | -                            | -                           | -                            |
| 0.5622 | 9800  | 4.8297        | -                            | -                            | -                            | -                           | -                            |
| 0.5679 | 9900  | 5.0156        | -                            | -                            | -                            | -                           | -                            |
| 0.5736 | 10000 | 5.0758        | -                            | -                            | -                            | -                           | -                            |
| 0.5794 | 10100 | 4.9551        | -                            | -                            | -                            | -                           | -                            |
| 0.5851 | 10200 | 4.9594        | -                            | -                            | -                            | -                           | -                            |
| 0.5908 | 10300 | 5.136         | -                            | -                            | -                            | -                           | -                            |
| 0.5966 | 10400 | 4.7873        | -                            | -                            | -                            | -                           | -                            |
| 0.6023 | 10500 | 4.5154        | -                            | -                            | -                            | -                           | -                            |
| 0.6080 | 10600 | 4.928         | -                            | -                            | -                            | -                           | -                            |
| 0.6138 | 10700 | 5.1825        | -                            | -                            | -                            | -                           | -                            |
| 0.6195 | 10800 | 5.046         | -                            | -                            | -                            | -                           | -                            |
| 0.6253 | 10900 | 5.0111        | -                            | -                            | -                            | -                           | -                            |
| 0.6310 | 11000 | 4.9458        | -                            | -                            | -                            | -                           | -                            |
| 0.6367 | 11100 | 5.188         | -                            | -                            | -                            | -                           | -                            |
| 0.6425 | 11200 | 4.6219        | -                            | -                            | -                            | -                           | -                            |
| 0.6482 | 11300 | 5.3367        | -                            | -                            | -                            | -                           | -                            |
| 0.6539 | 11400 | 4.9851        | -                            | -                            | -                            | -                           | -                            |
| 0.6597 | 11500 | 5.2068        | -                            | -                            | -                            | -                           | -                            |
| 0.6654 | 11600 | 4.3789        | -                            | -                            | -                            | -                           | -                            |
| 0.6711 | 11700 | 5.3533        | -                            | -                            | -                            | -                           | -                            |
| 0.6769 | 11800 | 5.3983        | -                            | -                            | -                            | -                           | -                            |
| 0.6826 | 11900 | 4.6           | -                            | -                            | -                            | -                           | -                            |
| 0.6883 | 12000 | 4.6668        | -                            | -                            | -                            | -                           | -                            |
| 0.6941 | 12100 | 5.0814        | -                            | -                            | -                            | -                           | -                            |
| 0.6998 | 12200 | 5.0787        | -                            | -                            | -                            | -                           | -                            |
| 0.7056 | 12300 | 4.6325        | -                            | -                            | -                            | -                           | -                            |
| 0.7113 | 12400 | 4.9415        | -                            | -                            | -                            | -                           | -                            |
| 0.7170 | 12500 | 4.7053        | -                            | -                            | -                            | -                           | -                            |
| 0.7228 | 12600 | 4.3212        | -                            | -                            | -                            | -                           | -                            |
| 0.7285 | 12700 | 4.8205        | -                            | -                            | -                            | -                           | -                            |
| 0.7342 | 12800 | 4.8602        | -                            | -                            | -                            | -                           | -                            |
| 0.7400 | 12900 | 4.6944        | -                            | -                            | -                            | -                           | -                            |
| 0.7457 | 13000 | 4.7785        | -                            | -                            | -                            | -                           | -                            |
| 0.7514 | 13100 | 4.3515        | -                            | -                            | -                            | -                           | -                            |
| 0.7572 | 13200 | 5.7561        | -                            | -                            | -                            | -                           | -                            |
| 0.7629 | 13300 | 5.3526        | -                            | -                            | -                            | -                           | -                            |
| 0.7687 | 13400 | 5.187         | -                            | -                            | -                            | -                           | -                            |
| 0.7744 | 13500 | 5.0143        | -                            | -                            | -                            | -                           | -                            |
| 0.7801 | 13600 | 4.515         | -                            | -                            | -                            | -                           | -                            |
| 0.7859 | 13700 | 4.639         | -                            | -                            | -                            | -                           | -                            |
| 0.7916 | 13800 | 4.5556        | -                            | -                            | -                            | -                           | -                            |
| 0.7973 | 13900 | 4.3526        | -                            | -                            | -                            | -                           | -                            |
| 0.8031 | 14000 | 4.3091        | -                            | -                            | -                            | -                           | -                            |
| 0.8088 | 14100 | 4.1761        | -                            | -                            | -                            | -                           | -                            |
| 0.8145 | 14200 | 4.0484        | -                            | -                            | -                            | -                           | -                            |
| 0.8203 | 14300 | 4.1886        | -                            | -                            | -                            | -                           | -                            |
| 0.8260 | 14400 | 4.237         | -                            | -                            | -                            | -                           | -                            |
| 0.8318 | 14500 | 4.2167        | -                            | -                            | -                            | -                           | -                            |
| 0.8375 | 14600 | 4.0329        | -                            | -                            | -                            | -                           | -                            |
| 0.8432 | 14700 | 3.9902        | -                            | -                            | -                            | -                           | -                            |
| 0.8490 | 14800 | 3.8211        | -                            | -                            | -                            | -                           | -                            |
| 0.8547 | 14900 | 4.0048        | -                            | -                            | -                            | -                           | -                            |
| 0.8604 | 15000 | 3.7979        | -                            | -                            | -                            | -                           | -                            |
| 0.8662 | 15100 | 3.8117        | -                            | -                            | -                            | -                           | -                            |
| 0.8719 | 15200 | 3.909         | -                            | -                            | -                            | -                           | -                            |
| 0.8776 | 15300 | 3.8526        | -                            | -                            | -                            | -                           | -                            |
| 0.8834 | 15400 | 3.79          | -                            | -                            | -                            | -                           | -                            |
| 0.8891 | 15500 | 3.7792        | -                            | -                            | -                            | -                           | -                            |
| 0.8949 | 15600 | 3.7469        | -                            | -                            | -                            | -                           | -                            |
| 0.9006 | 15700 | 3.8387        | -                            | -                            | -                            | -                           | -                            |
| 0.9063 | 15800 | 3.6418        | -                            | -                            | -                            | -                           | -                            |
| 0.9121 | 15900 | 3.645         | -                            | -                            | -                            | -                           | -                            |
| 0.9178 | 16000 | 3.4861        | -                            | -                            | -                            | -                           | -                            |
| 0.9235 | 16100 | 3.6416        | -                            | -                            | -                            | -                           | -                            |
| 0.9293 | 16200 | 3.6665        | -                            | -                            | -                            | -                           | -                            |
| 0.9350 | 16300 | 3.6809        | -                            | -                            | -                            | -                           | -                            |
| 0.9407 | 16400 | 3.7944        | -                            | -                            | -                            | -                           | -                            |
| 0.9465 | 16500 | 3.6585        | -                            | -                            | -                            | -                           | -                            |
| 0.9522 | 16600 | 3.5398        | -                            | -                            | -                            | -                           | -                            |
| 0.9580 | 16700 | 3.7036        | -                            | -                            | -                            | -                           | -                            |
| 0.9637 | 16800 | 3.6386        | -                            | -                            | -                            | -                           | -                            |
| 0.9694 | 16900 | 3.5501        | -                            | -                            | -                            | -                           | -                            |
| 0.9752 | 17000 | 3.7957        | -                            | -                            | -                            | -                           | -                            |
| 0.9809 | 17100 | 3.6076        | -                            | -                            | -                            | -                           | -                            |
| 0.9866 | 17200 | 3.4653        | -                            | -                            | -                            | -                           | -                            |
| 0.9924 | 17300 | 3.6768        | -                            | -                            | -                            | -                           | -                            |
| 0.9981 | 17400 | 3.49          | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 17433 | -             | 0.7051                       | 0.7044                       | 0.7046                       | 0.7044                      | 0.7065                       |

</details>

### 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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