sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:282883 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:282883
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Mwanamke anashona.
  sentences:
  - Mwanamke akishona blanketi pamoja.
  - Lakini Sir James alimkatiza.
  - Kwa kuongezea, wabunifu mashuhuri sasa wana maduka ya rejareja katika hoteli kadhaa
    za ununuzi.
- source_sentence: Mtandao huwawezesha watu kununua vitu.
  sentences:
  - Mwanamke fulani anakata kipande cha jibini.
  - Hakuna hata mmoja wa wafadhili hawa anayeweza kuruhusu kuacha sasa, haswa na uchumi
    unaoteseka ndani na kitaifa.
  - Kwa vyovyote vile, kama ningekuwa na nia ya kununua kitabu hicho, ningekuwa na
    nafasi nzuri zaidi ya kujadili bei nzuri.
- source_sentence: Je, kweli wewe ni hivyo gullible, Dave Hanson?
  sentences:
  - Mwanamume amesimama katika mashua paddling kuelekea pwani lined na fanicha na
    vitu vingine kubwa.
  - Dave Hanson, je, unaamini kila kitu wanachosema?
  - Wasichana watatu wakifanya mchezo wa kuigiza jukwaani.
- source_sentence: Wanandoa wakitembea pamoja.
  sentences:
  - Mwanamume aliyevalia koti la manjano anatembea kando ya gari la kubebea watu.
  - Wenzi wa ndoa wazee wanatembea barabarani wakishikamana mikono.
  - Msichana mdogo anapanda kwenye kifaa cha kamba.
- source_sentence: Kuna masuala ya sera.
  sentences:
  - Mwanamke mwenye makunyanzi sana akishikilia miwani yake na kutembea kwenye barabara
    ya jiji.
  - Mwanamume anayeigiza kwa ajili ya umati wa watu.
  - Masuala ya sera ya mbinu nyingi na maombi.
model-index:
- name: BGE base Swahili Matryoshka
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.26803894120641386
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3499618223466531
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3858806312038687
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.43318910664291166
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26803894120641386
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11665394078221768
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07717612624077373
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.043318910664291166
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.26803894120641386
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3499618223466531
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3858806312038687
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.43318910664291166
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.34611891078942064
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3188061049905684
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3251959746415499
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.26552557902774243
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.34601679816747266
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3810766098243828
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4290850089081191
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26552557902774243
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11533893272249085
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07621532196487656
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04290850089081191
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.26552557902774243
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.34601679816747266
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3810766098243828
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4290850089081191
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3425120728009226
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.31538232445349546
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.32174207802147353
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.2576355306693815
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.33790404683125475
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.37165945533214556
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.41950878086026977
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2576355306693815
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1126346822770849
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07433189106642912
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.041950878086026974
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2576355306693815
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.33790404683125475
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.37165945533214556
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.41950878086026977
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3338740008089949
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.30705069547968683
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3134101334652913
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.24494146093153474
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3218694324255536
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3557202850598117
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.402901501654365
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.24494146093153474
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.10728981080851785
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07114405701196233
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.0402901501654365
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.24494146093153474
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3218694324255536
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3557202850598117
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.402901501654365
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3191027723891013
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2928823673781056
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.299205934269314
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.21936243318910664
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.2918045304148638
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3234601679816747
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.3698778315092899
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.21936243318910664
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.0972681768049546
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06469203359633495
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.03698778315092899
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.21936243318910664
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.2918045304148638
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3234601679816747
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3698778315092899
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2897472677253453
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.26472963050495346
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2710377326397304
      name: Cosine Map@100
---

# BGE base Swahili Matryoshka

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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:

```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/bge-base-swahili-matryoshka")
# Run inference
sentences = [
    'Kuna masuala ya sera.',
    'Masuala ya sera ya mbinu nyingi na maombi.',
    'Mwanamke mwenye makunyanzi sana akishikilia miwani yake na kutembea kwenye barabara ya jiji.',
]
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.268      |
| cosine_accuracy@3   | 0.35       |
| cosine_accuracy@5   | 0.3859     |
| cosine_accuracy@10  | 0.4332     |
| cosine_precision@1  | 0.268      |
| cosine_precision@3  | 0.1167     |
| cosine_precision@5  | 0.0772     |
| cosine_precision@10 | 0.0433     |
| cosine_recall@1     | 0.268      |
| cosine_recall@3     | 0.35       |
| cosine_recall@5     | 0.3859     |
| cosine_recall@10    | 0.4332     |
| cosine_ndcg@10      | 0.3461     |
| cosine_mrr@10       | 0.3188     |
| **cosine_map@100**  | **0.3252** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2655     |
| cosine_accuracy@3   | 0.346      |
| cosine_accuracy@5   | 0.3811     |
| cosine_accuracy@10  | 0.4291     |
| cosine_precision@1  | 0.2655     |
| cosine_precision@3  | 0.1153     |
| cosine_precision@5  | 0.0762     |
| cosine_precision@10 | 0.0429     |
| cosine_recall@1     | 0.2655     |
| cosine_recall@3     | 0.346      |
| cosine_recall@5     | 0.3811     |
| cosine_recall@10    | 0.4291     |
| cosine_ndcg@10      | 0.3425     |
| cosine_mrr@10       | 0.3154     |
| **cosine_map@100**  | **0.3217** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2576     |
| cosine_accuracy@3   | 0.3379     |
| cosine_accuracy@5   | 0.3717     |
| cosine_accuracy@10  | 0.4195     |
| cosine_precision@1  | 0.2576     |
| cosine_precision@3  | 0.1126     |
| cosine_precision@5  | 0.0743     |
| cosine_precision@10 | 0.042      |
| cosine_recall@1     | 0.2576     |
| cosine_recall@3     | 0.3379     |
| cosine_recall@5     | 0.3717     |
| cosine_recall@10    | 0.4195     |
| cosine_ndcg@10      | 0.3339     |
| cosine_mrr@10       | 0.3071     |
| **cosine_map@100**  | **0.3134** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.2449     |
| cosine_accuracy@3   | 0.3219     |
| cosine_accuracy@5   | 0.3557     |
| cosine_accuracy@10  | 0.4029     |
| cosine_precision@1  | 0.2449     |
| cosine_precision@3  | 0.1073     |
| cosine_precision@5  | 0.0711     |
| cosine_precision@10 | 0.0403     |
| cosine_recall@1     | 0.2449     |
| cosine_recall@3     | 0.3219     |
| cosine_recall@5     | 0.3557     |
| cosine_recall@10    | 0.4029     |
| cosine_ndcg@10      | 0.3191     |
| cosine_mrr@10       | 0.2929     |
| **cosine_map@100**  | **0.2992** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| cosine_accuracy@1   | 0.2194    |
| cosine_accuracy@3   | 0.2918    |
| cosine_accuracy@5   | 0.3235    |
| cosine_accuracy@10  | 0.3699    |
| cosine_precision@1  | 0.2194    |
| cosine_precision@3  | 0.0973    |
| cosine_precision@5  | 0.0647    |
| cosine_precision@10 | 0.037     |
| cosine_recall@1     | 0.2194    |
| cosine_recall@3     | 0.2918    |
| cosine_recall@5     | 0.3235    |
| cosine_recall@10    | 0.3699    |
| cosine_ndcg@10      | 0.2897    |
| cosine_mrr@10       | 0.2647    |
| **cosine_map@100**  | **0.271** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 282,883 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                         | anchor                                                                             |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 20.1 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 38.64 tokens</li><li>max: 184 tokens</li></ul> |
* Samples:
  | positive                                                              | anchor                                                                                                                                                                                       |
  |:----------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Alingoja mtu huyo mwingine arudi.</code>                        | <code>Ca'daan alingoja hadi alasiri yote mtu huyo atoke tena.</code>                                                                                                                         |
  | <code>Sheria hiyo huanzisha mfululizo wa ukaguzi wa majaribio.</code> | <code>Sheria hiyo pia inatoa sheria ya kudhibiti kwa ajili ya mashirika fulani ambayo yanahitaji kuandaa taarifa za kifedha za mashirika yote na kuzisimamisha kwa wakaguzi wa jumla.</code> |
  | <code>Mbwa anakimbia na kuruka nje.</code>                            | <code>Mbwa mwenye rangi ya kahawia anaruka na kukimbia shambani.</code>                                                                                                                      |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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
- `restore_callback_states_from_checkpoint`: 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`: True
- `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`: True
- `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, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch      | Step     | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:----------:|:--------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.0181     | 10       | 9.7089        | -                      | -                      | -                      | -                     | -                      |
| 0.0362     | 20       | 9.2806        | -                      | -                      | -                      | -                     | -                      |
| 0.0543     | 30       | 8.8905        | -                      | -                      | -                      | -                     | -                      |
| 0.0724     | 40       | 7.9651        | -                      | -                      | -                      | -                     | -                      |
| 0.0905     | 50       | 7.4201        | -                      | -                      | -                      | -                     | -                      |
| 0.1086     | 60       | 6.8346        | -                      | -                      | -                      | -                     | -                      |
| 0.1267     | 70       | 6.515         | -                      | -                      | -                      | -                     | -                      |
| 0.1448     | 80       | 6.2009        | -                      | -                      | -                      | -                     | -                      |
| 0.1629     | 90       | 5.8256        | -                      | -                      | -                      | -                     | -                      |
| 0.1810     | 100      | 5.549         | -                      | -                      | -                      | -                     | -                      |
| 0.1991     | 110      | 5.1667        | -                      | -                      | -                      | -                     | -                      |
| 0.2172     | 120      | 5.2684        | -                      | -                      | -                      | -                     | -                      |
| 0.2353     | 130      | 5.0678        | -                      | -                      | -                      | -                     | -                      |
| 0.2534     | 140      | 4.9183        | -                      | -                      | -                      | -                     | -                      |
| 0.2715     | 150      | 4.844         | -                      | -                      | -                      | -                     | -                      |
| 0.2896     | 160      | 4.5427        | -                      | -                      | -                      | -                     | -                      |
| 0.3077     | 170      | 4.3324        | -                      | -                      | -                      | -                     | -                      |
| 0.3258     | 180      | 4.4963        | -                      | -                      | -                      | -                     | -                      |
| 0.3439     | 190      | 4.1704        | -                      | -                      | -                      | -                     | -                      |
| 0.3620     | 200      | 4.1285        | -                      | -                      | -                      | -                     | -                      |
| 0.3800     | 210      | 4.0235        | -                      | -                      | -                      | -                     | -                      |
| 0.3981     | 220      | 4.0738        | -                      | -                      | -                      | -                     | -                      |
| 0.4162     | 230      | 3.9132        | -                      | -                      | -                      | -                     | -                      |
| 0.4343     | 240      | 3.9682        | -                      | -                      | -                      | -                     | -                      |
| 0.4524     | 250      | 3.7542        | -                      | -                      | -                      | -                     | -                      |
| 0.4705     | 260      | 3.6508        | -                      | -                      | -                      | -                     | -                      |
| 0.4886     | 270      | 3.7596        | -                      | -                      | -                      | -                     | -                      |
| 0.5067     | 280      | 3.5596        | -                      | -                      | -                      | -                     | -                      |
| 0.5248     | 290      | 3.5077        | -                      | -                      | -                      | -                     | -                      |
| 0.5429     | 300      | 3.3831        | -                      | -                      | -                      | -                     | -                      |
| 0.5610     | 310      | 3.4           | -                      | -                      | -                      | -                     | -                      |
| 0.5791     | 320      | 3.296         | -                      | -                      | -                      | -                     | -                      |
| 0.5972     | 330      | 3.3646        | -                      | -                      | -                      | -                     | -                      |
| 0.6153     | 340      | 3.3533        | -                      | -                      | -                      | -                     | -                      |
| 0.6334     | 350      | 3.2171        | -                      | -                      | -                      | -                     | -                      |
| 0.6515     | 360      | 3.2324        | -                      | -                      | -                      | -                     | -                      |
| 0.6696     | 370      | 3.1544        | -                      | -                      | -                      | -                     | -                      |
| 0.6877     | 380      | 3.3393        | -                      | -                      | -                      | -                     | -                      |
| 0.7058     | 390      | 3.0864        | -                      | -                      | -                      | -                     | -                      |
| 0.7239     | 400      | 3.1069        | -                      | -                      | -                      | -                     | -                      |
| 0.7420     | 410      | 3.0722        | -                      | -                      | -                      | -                     | -                      |
| 0.7601     | 420      | 3.1446        | -                      | -                      | -                      | -                     | -                      |
| 0.7782     | 430      | 3.0847        | -                      | -                      | -                      | -                     | -                      |
| 0.7963     | 440      | 3.0331        | -                      | -                      | -                      | -                     | -                      |
| 0.8144     | 450      | 3.0197        | -                      | -                      | -                      | -                     | -                      |
| 0.8325     | 460      | 2.9667        | -                      | -                      | -                      | -                     | -                      |
| 0.8506     | 470      | 2.8331        | -                      | -                      | -                      | -                     | -                      |
| 0.8687     | 480      | 2.9333        | -                      | -                      | -                      | -                     | -                      |
| 0.8868     | 490      | 2.8714        | -                      | -                      | -                      | -                     | -                      |
| 0.9049     | 500      | 2.8578        | -                      | -                      | -                      | -                     | -                      |
| 0.9230     | 510      | 2.9689        | -                      | -                      | -                      | -                     | -                      |
| 0.9411     | 520      | 2.7977        | -                      | -                      | -                      | -                     | -                      |
| 0.9592     | 530      | 2.9832        | -                      | -                      | -                      | -                     | -                      |
| 0.9773     | 540      | 2.9761        | -                      | -                      | -                      | -                     | -                      |
| 0.9954     | 550      | 2.7711        | -                      | -                      | -                      | -                     | -                      |
| 0.9990     | 552      | -             | 0.2772                 | 0.2954                 | 0.3052                 | 0.2445                | 0.3080                 |
| 1.0135     | 560      | 2.7194        | -                      | -                      | -                      | -                     | -                      |
| 1.0316     | 570      | 2.8489        | -                      | -                      | -                      | -                     | -                      |
| 1.0497     | 580      | 2.6559        | -                      | -                      | -                      | -                     | -                      |
| 1.0678     | 590      | 2.6239        | -                      | -                      | -                      | -                     | -                      |
| 1.0859     | 600      | 2.7081        | -                      | -                      | -                      | -                     | -                      |
| 1.1039     | 610      | 2.6581        | -                      | -                      | -                      | -                     | -                      |
| 1.1220     | 620      | 2.7709        | -                      | -                      | -                      | -                     | -                      |
| 1.1401     | 630      | 2.6191        | -                      | -                      | -                      | -                     | -                      |
| 1.1582     | 640      | 2.6712        | -                      | -                      | -                      | -                     | -                      |
| 1.1763     | 650      | 2.5445        | -                      | -                      | -                      | -                     | -                      |
| 1.1944     | 660      | 2.5264        | -                      | -                      | -                      | -                     | -                      |
| 1.2125     | 670      | 2.5782        | -                      | -                      | -                      | -                     | -                      |
| 1.2306     | 680      | 2.5652        | -                      | -                      | -                      | -                     | -                      |
| 1.2487     | 690      | 2.6229        | -                      | -                      | -                      | -                     | -                      |
| 1.2668     | 700      | 2.5557        | -                      | -                      | -                      | -                     | -                      |
| 1.2849     | 710      | 2.5251        | -                      | -                      | -                      | -                     | -                      |
| 1.3030     | 720      | 2.4555        | -                      | -                      | -                      | -                     | -                      |
| 1.3211     | 730      | 2.5335        | -                      | -                      | -                      | -                     | -                      |
| 1.3392     | 740      | 2.5027        | -                      | -                      | -                      | -                     | -                      |
| 1.3573     | 750      | 2.3569        | -                      | -                      | -                      | -                     | -                      |
| 1.3754     | 760      | 2.4255        | -                      | -                      | -                      | -                     | -                      |
| 1.3935     | 770      | 2.4626        | -                      | -                      | -                      | -                     | -                      |
| 1.4116     | 780      | 2.363         | -                      | -                      | -                      | -                     | -                      |
| 1.4297     | 790      | 2.4           | -                      | -                      | -                      | -                     | -                      |
| 1.4478     | 800      | 2.3317        | -                      | -                      | -                      | -                     | -                      |
| 1.4659     | 810      | 2.2922        | -                      | -                      | -                      | -                     | -                      |
| 1.4840     | 820      | 2.4086        | -                      | -                      | -                      | -                     | -                      |
| 1.5021     | 830      | 2.3166        | -                      | -                      | -                      | -                     | -                      |
| 1.5202     | 840      | 2.3401        | -                      | -                      | -                      | -                     | -                      |
| 1.5383     | 850      | 2.1951        | -                      | -                      | -                      | -                     | -                      |
| 1.5564     | 860      | 2.214         | -                      | -                      | -                      | -                     | -                      |
| 1.5745     | 870      | 2.1859        | -                      | -                      | -                      | -                     | -                      |
| 1.5926     | 880      | 2.3605        | -                      | -                      | -                      | -                     | -                      |
| 1.6107     | 890      | 2.2528        | -                      | -                      | -                      | -                     | -                      |
| 1.6288     | 900      | 2.2759        | -                      | -                      | -                      | -                     | -                      |
| 1.6469     | 910      | 2.1458        | -                      | -                      | -                      | -                     | -                      |
| 1.6650     | 920      | 2.187         | -                      | -                      | -                      | -                     | -                      |
| 1.6831     | 930      | 2.3406        | -                      | -                      | -                      | -                     | -                      |
| 1.7012     | 940      | 2.2151        | -                      | -                      | -                      | -                     | -                      |
| 1.7193     | 950      | 2.2971        | -                      | -                      | -                      | -                     | -                      |
| 1.7374     | 960      | 2.2736        | -                      | -                      | -                      | -                     | -                      |
| 1.7555     | 970      | 2.2329        | -                      | -                      | -                      | -                     | -                      |
| 1.7736     | 980      | 2.2602        | -                      | -                      | -                      | -                     | -                      |
| 1.7917     | 990      | 2.2402        | -                      | -                      | -                      | -                     | -                      |
| 1.8098     | 1000     | 2.1971        | -                      | -                      | -                      | -                     | -                      |
| 1.8278     | 1010     | 2.1642        | -                      | -                      | -                      | -                     | -                      |
| 1.8459     | 1020     | 2.1274        | -                      | -                      | -                      | -                     | -                      |
| 1.8640     | 1030     | 2.1833        | -                      | -                      | -                      | -                     | -                      |
| 1.8821     | 1040     | 2.156         | -                      | -                      | -                      | -                     | -                      |
| 1.9002     | 1050     | 2.1252        | -                      | -                      | -                      | -                     | -                      |
| 1.9183     | 1060     | 2.161         | -                      | -                      | -                      | -                     | -                      |
| 1.9364     | 1070     | 2.1267        | -                      | -                      | -                      | -                     | -                      |
| 1.9545     | 1080     | 2.2017        | -                      | -                      | -                      | -                     | -                      |
| 1.9726     | 1090     | 2.3044        | -                      | -                      | -                      | -                     | -                      |
| 1.9907     | 1100     | 2.161         | -                      | -                      | -                      | -                     | -                      |
| 1.9998     | 1105     | -             | 0.2928                 | 0.3085                 | 0.3165                 | 0.2632                | 0.3204                 |
| 2.0088     | 1110     | 2.0594        | -                      | -                      | -                      | -                     | -                      |
| 2.0269     | 1120     | 2.2277        | -                      | -                      | -                      | -                     | -                      |
| 2.0450     | 1130     | 2.1591        | -                      | -                      | -                      | -                     | -                      |
| 2.0631     | 1140     | 2.0396        | -                      | -                      | -                      | -                     | -                      |
| 2.0812     | 1150     | 2.1007        | -                      | -                      | -                      | -                     | -                      |
| 2.0993     | 1160     | 2.0705        | -                      | -                      | -                      | -                     | -                      |
| 2.1174     | 1170     | 2.0894        | -                      | -                      | -                      | -                     | -                      |
| 2.1355     | 1180     | 2.0677        | -                      | -                      | -                      | -                     | -                      |
| 2.1536     | 1190     | 2.0893        | -                      | -                      | -                      | -                     | -                      |
| 2.1717     | 1200     | 1.984         | -                      | -                      | -                      | -                     | -                      |
| 2.1898     | 1210     | 1.9206        | -                      | -                      | -                      | -                     | -                      |
| 2.2079     | 1220     | 2.132         | -                      | -                      | -                      | -                     | -                      |
| 2.2260     | 1230     | 2.0457        | -                      | -                      | -                      | -                     | -                      |
| 2.2441     | 1240     | 2.1428        | -                      | -                      | -                      | -                     | -                      |
| 2.2622     | 1250     | 2.1116        | -                      | -                      | -                      | -                     | -                      |
| 2.2803     | 1260     | 1.993         | -                      | -                      | -                      | -                     | -                      |
| 2.2984     | 1270     | 2.0181        | -                      | -                      | -                      | -                     | -                      |
| 2.3165     | 1280     | 1.9742        | -                      | -                      | -                      | -                     | -                      |
| 2.3346     | 1290     | 2.081         | -                      | -                      | -                      | -                     | -                      |
| 2.3527     | 1300     | 1.9107        | -                      | -                      | -                      | -                     | -                      |
| 2.3708     | 1310     | 1.9507        | -                      | -                      | -                      | -                     | -                      |
| 2.3889     | 1320     | 1.9844        | -                      | -                      | -                      | -                     | -                      |
| 2.4070     | 1330     | 2.0035        | -                      | -                      | -                      | -                     | -                      |
| 2.4251     | 1340     | 1.9121        | -                      | -                      | -                      | -                     | -                      |
| 2.4432     | 1350     | 2.0057        | -                      | -                      | -                      | -                     | -                      |
| 2.4613     | 1360     | 1.9323        | -                      | -                      | -                      | -                     | -                      |
| 2.4794     | 1370     | 1.9216        | -                      | -                      | -                      | -                     | -                      |
| 2.4975     | 1380     | 1.995         | -                      | -                      | -                      | -                     | -                      |
| 2.5156     | 1390     | 1.9285        | -                      | -                      | -                      | -                     | -                      |
| 2.5337     | 1400     | 1.8886        | -                      | -                      | -                      | -                     | -                      |
| 2.5517     | 1410     | 1.8298        | -                      | -                      | -                      | -                     | -                      |
| 2.5698     | 1420     | 1.8452        | -                      | -                      | -                      | -                     | -                      |
| 2.5879     | 1430     | 1.9488        | -                      | -                      | -                      | -                     | -                      |
| 2.6060     | 1440     | 1.8928        | -                      | -                      | -                      | -                     | -                      |
| 2.6241     | 1450     | 2.0101        | -                      | -                      | -                      | -                     | -                      |
| 2.6422     | 1460     | 1.7591        | -                      | -                      | -                      | -                     | -                      |
| 2.6603     | 1470     | 1.9177        | -                      | -                      | -                      | -                     | -                      |
| 2.6784     | 1480     | 1.9329        | -                      | -                      | -                      | -                     | -                      |
| 2.6965     | 1490     | 1.8978        | -                      | -                      | -                      | -                     | -                      |
| 2.7146     | 1500     | 1.9589        | -                      | -                      | -                      | -                     | -                      |
| 2.7327     | 1510     | 1.9744        | -                      | -                      | -                      | -                     | -                      |
| 2.7508     | 1520     | 1.9272        | -                      | -                      | -                      | -                     | -                      |
| 2.7689     | 1530     | 1.9234        | -                      | -                      | -                      | -                     | -                      |
| 2.7870     | 1540     | 1.9667        | -                      | -                      | -                      | -                     | -                      |
| 2.8051     | 1550     | 1.853         | -                      | -                      | -                      | -                     | -                      |
| 2.8232     | 1560     | 1.9191        | -                      | -                      | -                      | -                     | -                      |
| 2.8413     | 1570     | 1.8083        | -                      | -                      | -                      | -                     | -                      |
| 2.8594     | 1580     | 1.8543        | -                      | -                      | -                      | -                     | -                      |
| 2.8775     | 1590     | 1.9091        | -                      | -                      | -                      | -                     | -                      |
| 2.8956     | 1600     | 1.8079        | -                      | -                      | -                      | -                     | -                      |
| 2.9137     | 1610     | 1.8992        | -                      | -                      | -                      | -                     | -                      |
| 2.9318     | 1620     | 1.8742        | -                      | -                      | -                      | -                     | -                      |
| 2.9499     | 1630     | 1.9313        | -                      | -                      | -                      | -                     | -                      |
| 2.9680     | 1640     | 1.9832        | -                      | -                      | -                      | -                     | -                      |
| 2.9861     | 1650     | 1.9037        | -                      | -                      | -                      | -                     | -                      |
| 2.9988     | 1657     | -             | 0.2982                 | 0.3130                 | 0.3211                 | 0.2697                | 0.3247                 |
| 3.0042     | 1660     | 1.7924        | -                      | -                      | -                      | -                     | -                      |
| 3.0223     | 1670     | 1.9677        | -                      | -                      | -                      | -                     | -                      |
| 3.0404     | 1680     | 1.9123        | -                      | -                      | -                      | -                     | -                      |
| 3.0585     | 1690     | 1.7691        | -                      | -                      | -                      | -                     | -                      |
| 3.0766     | 1700     | 1.8822        | -                      | -                      | -                      | -                     | -                      |
| 3.0947     | 1710     | 1.8543        | -                      | -                      | -                      | -                     | -                      |
| 3.1128     | 1720     | 1.8127        | -                      | -                      | -                      | -                     | -                      |
| 3.1309     | 1730     | 1.8844        | -                      | -                      | -                      | -                     | -                      |
| 3.1490     | 1740     | 1.911         | -                      | -                      | -                      | -                     | -                      |
| 3.1671     | 1750     | 1.7695        | -                      | -                      | -                      | -                     | -                      |
| 3.1852     | 1760     | 1.8134        | -                      | -                      | -                      | -                     | -                      |
| 3.2033     | 1770     | 1.7794        | -                      | -                      | -                      | -                     | -                      |
| 3.2214     | 1780     | 1.8851        | -                      | -                      | -                      | -                     | -                      |
| 3.2395     | 1790     | 1.8381        | -                      | -                      | -                      | -                     | -                      |
| 3.2576     | 1800     | 1.9184        | -                      | -                      | -                      | -                     | -                      |
| 3.2756     | 1810     | 1.8074        | -                      | -                      | -                      | -                     | -                      |
| 3.2937     | 1820     | 1.8236        | -                      | -                      | -                      | -                     | -                      |
| 3.3118     | 1830     | 1.8203        | -                      | -                      | -                      | -                     | -                      |
| 3.3299     | 1840     | 1.8874        | -                      | -                      | -                      | -                     | -                      |
| 3.3480     | 1850     | 1.7457        | -                      | -                      | -                      | -                     | -                      |
| 3.3661     | 1860     | 1.7933        | -                      | -                      | -                      | -                     | -                      |
| 3.3842     | 1870     | 1.759         | -                      | -                      | -                      | -                     | -                      |
| 3.4023     | 1880     | 1.8514        | -                      | -                      | -                      | -                     | -                      |
| 3.4204     | 1890     | 1.8163        | -                      | -                      | -                      | -                     | -                      |
| 3.4385     | 1900     | 1.8299        | -                      | -                      | -                      | -                     | -                      |
| 3.4566     | 1910     | 1.8112        | -                      | -                      | -                      | -                     | -                      |
| 3.4747     | 1920     | 1.7446        | -                      | -                      | -                      | -                     | -                      |
| 3.4928     | 1930     | 1.8314        | -                      | -                      | -                      | -                     | -                      |
| 3.5109     | 1940     | 1.742         | -                      | -                      | -                      | -                     | -                      |
| 3.5290     | 1950     | 1.7519        | -                      | -                      | -                      | -                     | -                      |
| 3.5471     | 1960     | 1.722         | -                      | -                      | -                      | -                     | -                      |
| 3.5652     | 1970     | 1.7454        | -                      | -                      | -                      | -                     | -                      |
| 3.5833     | 1980     | 1.7875        | -                      | -                      | -                      | -                     | -                      |
| 3.6014     | 1990     | 1.7596        | -                      | -                      | -                      | -                     | -                      |
| 3.6195     | 2000     | 1.8348        | -                      | -                      | -                      | -                     | -                      |
| 3.6376     | 2010     | 1.6954        | -                      | -                      | -                      | -                     | -                      |
| 3.6557     | 2020     | 1.7334        | -                      | -                      | -                      | -                     | -                      |
| 3.6738     | 2030     | 1.8318        | -                      | -                      | -                      | -                     | -                      |
| 3.6919     | 2040     | 1.7982        | -                      | -                      | -                      | -                     | -                      |
| 3.7100     | 2050     | 1.7987        | -                      | -                      | -                      | -                     | -                      |
| 3.7281     | 2060     | 1.8402        | -                      | -                      | -                      | -                     | -                      |
| 3.7462     | 2070     | 1.8569        | -                      | -                      | -                      | -                     | -                      |
| 3.7643     | 2080     | 1.8285        | -                      | -                      | -                      | -                     | -                      |
| 3.7824     | 2090     | 1.8652        | -                      | -                      | -                      | -                     | -                      |
| 3.8005     | 2100     | 1.7731        | -                      | -                      | -                      | -                     | -                      |
| 3.8186     | 2110     | 1.8697        | -                      | -                      | -                      | -                     | -                      |
| 3.8367     | 2120     | 1.6953        | -                      | -                      | -                      | -                     | -                      |
| 3.8548     | 2130     | 1.7493        | -                      | -                      | -                      | -                     | -                      |
| 3.8729     | 2140     | 1.8031        | -                      | -                      | -                      | -                     | -                      |
| 3.8910     | 2150     | 1.7053        | -                      | -                      | -                      | -                     | -                      |
| 3.9091     | 2160     | 1.8436        | -                      | -                      | -                      | -                     | -                      |
| 3.9272     | 2170     | 1.7572        | -                      | -                      | -                      | -                     | -                      |
| 3.9453     | 2180     | 1.7797        | -                      | -                      | -                      | -                     | -                      |
| 3.9634     | 2190     | 1.8827        | -                      | -                      | -                      | -                     | -                      |
| 3.9815     | 2200     | 1.8678        | -                      | -                      | -                      | -                     | -                      |
| **3.9959** | **2208** | **-**         | **0.2992**             | **0.3134**             | **0.3217**             | **0.271**             | **0.3252**             |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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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