sentence-transformers safetensors sentence-similarity feature-extraction generated_from_trainer dataset_size:3012496 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en es dataset:sentence-transformers/gooaq arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- sentence-transformers/gooaq
language:
- en
- es
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3012496
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: how to sign legal documents as power of attorney?
  sentences:
  - 'After the principal''s name, write “by” and then sign your own name. Under or
    after the signature line, indicate your status as POA by including any of the
    following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.'
  - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap
    Menu (...).'', ''Tap Export to SD card.'']'
  - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking
    gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect
    nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect
    product for both cannabis and chocolate lovers, who appreciate a little twist.
- source_sentence: how to delete vdom in fortigate?
  sentences:
  - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully
    removed from the configuration.
  - 'Both combination birth control pills and progestin-only pills may cause headaches
    as a side effect. Additional side effects of birth control pills may include:
    breast tenderness. nausea.'
  - White cheese tends to show imperfections more readily and as consumers got more
    used to yellow-orange cheese, it became an expected option. Today, many cheddars
    are yellow. While most cheesemakers use annatto, some use an artificial coloring
    agent instead, according to Sachs.
- source_sentence: where are earthquakes most likely to occur on earth?
  sentences:
  - Zelle in the Bank of the America app is a fast, safe, and easy way to send and
    receive money with family and friends who have a bank account in the U.S., all
    with no fees. Money moves in minutes directly between accounts that are already
    enrolled with Zelle.
  - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft
    travels at least 240,000 miles (386,400 kilometers) which is the distance between
    Earth and the Moon.
  - Most earthquakes occur along the edge of the oceanic and continental plates. The
    earth's crust (the outer layer of the planet) is made up of several pieces, called
    plates. The plates under the oceans are called oceanic plates and the rest are
    continental plates.
- source_sentence: fix iphone is disabled connect to itunes without itunes?
  sentences:
  - To fix a disabled iPhone or iPad without iTunes, you have to erase your device.
    Click on the "Erase iPhone" option and confirm your selection. Wait for a while
    as the "Find My iPhone" feature will remotely erase your iOS device. Needless
    to say, it will also disable its lock.
  - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui
    lay beside his fire staring into the flames. ... In the middle of the night, while
    everyone was sleeping, Māui went from village to village and extinguished all
    the fires until not a single fire burned in the world.
  - Angry Orchard makes a variety of year-round craft cider styles, including Angry
    Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of
    culinary apples with dryness and bright acidity of bittersweet apples for a complex,
    refreshing taste.
- source_sentence: how to reverse a video on tiktok that's not yours?
  sentences:
  - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like
    a clock. Open the Effects menu. ... '', ''At the end of the new list that appears,
    tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then
    see a preview of your new, reversed video appear on the screen.'']'
  - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial
    investment range of $157,800 to $438,000. The initial cost of a franchise includes
    several fees -- Unlock this franchise to better understand the costs such as training
    and territory fees.
  - Relative age is the age of a rock layer (or the fossils it contains) compared
    to other layers. It can be determined by looking at the position of rock layers.
    Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can
    be determined by using radiometric dating.
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** inf tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
- **Language:** en
<!-- - **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): StaticEmbedding(
    (embedding): EmbeddingBag(256000, 1024, mode='mean')
  )
)
```

## 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("NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-gooaq-en")
# Run inference
sentences = [
    "how to reverse a video on tiktok that's not yours?",
    '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']',
    'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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.*
-->

<!--
## 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.*
-->

## Training Details

### Training Dataset

#### gooaq

* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 training samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                                       | answer                                                                                           |
  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | type    | string                                                                                         | string                                                                                           |
  | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> |
* Samples:
  | question                                                                           | answer                                                                                                                                                                                                                                                                                                                |
  |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is the difference between broilers and layers?</code>                   | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code>                |
  | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> |
  | <code>is kamagra same as viagra?</code>                                            | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code>                               |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### gooaq

* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 3,012,496 evaluation samples
* Columns: <code>question</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | question                                                                                       | answer                                                                                           |
  |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|
  | type    | string                                                                                         | string                                                                                           |
  | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> |
* Samples:
  | question                                                                     | answer                                                                                                                                                                                                                                                                                                                                     |
  |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>how do i program my directv remote with my tv?</code>                  | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code>                                                                                               |
  | <code>are rodrigues fruit bats nocturnal?</code>                             | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code>                                                                                                  |
  | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `learning_rate`: 0.2
- `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
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 2048
- `per_device_eval_batch_size`: 2048
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.2
- `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
- `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`: 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, 'non_blocking': False, '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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0007 | 1    | 48.9183       | -               |
| 0.0682 | 100  | 24.7453       | 3.5934          |
| 0.1363 | 200  | 8.3975        | 2.4385          |
| 0.2045 | 300  | 6.3171        | 1.9962          |
| 0.2727 | 400  | 5.3817        | 1.7536          |
| 0.3408 | 500  | 4.8295        | 1.6392          |
| 0.4090 | 600  | 4.4745        | 1.5070          |
| 0.4772 | 700  | 4.1783        | 1.4406          |
| 0.5453 | 800  | 3.952         | 1.3655          |
| 0.6135 | 900  | 3.7352        | 1.3114          |
| 0.6817 | 1000 | 3.6185        | 1.2551          |
| 0.7498 | 1100 | 3.4514        | 1.2143          |
| 0.8180 | 1200 | 3.3535        | 1.1816          |
| 0.8862 | 1300 | 3.2741        | 1.1527          |
| 0.9543 | 1400 | 3.1862        | 1.1411          |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0


## NanoBEIREvaluator > 0.8

```
{
    "NanoDBPedia_cosine_accuracy@3": 0.86,
    "NanoDBPedia_cosine_accuracy@5": 0.92,
    "NanoDBPedia_cosine_accuracy@10": 0.96,
    "NanoFEVER_cosine_accuracy@3": 0.86,
    "NanoFEVER_cosine_accuracy@5": 0.92,
    "NanoFEVER_cosine_accuracy@10": 0.96,
    "NanoHotpotQA_cosine_accuracy@3": 0.82,
    "NanoHotpotQA_cosine_accuracy@5": 0.84,
    "NanoHotpotQA_cosine_accuracy@10": 0.88,
    "NanoQuoraRetrieval_cosine_accuracy@1": 0.88,
    "NanoQuoraRetrieval_cosine_accuracy@3": 0.96,
    "NanoQuoraRetrieval_cosine_accuracy@5": 1.0,
    "NanoQuoraRetrieval_cosine_accuracy@10": 1.0,
    "NanoSCIDOCS_cosine_accuracy@5": 0.82,
    "NanoSCIDOCS_cosine_accuracy@10": 0.92,
    "NanoArguAna_cosine_accuracy@10": 0.92,
    "NanoSciFact_cosine_accuracy@10": 0.88,
    "NanoTouche2020_cosine_accuracy@3": 0.8367346938775511,
    "NanoTouche2020_cosine_accuracy@5": 0.9183673469387755,
    "NanoTouche2020_cosine_accuracy@10": 0.9387755102040817,
    "NanoBEIR_mean_cosine_accuracy@10": 0.8583673469387756
}

````

## All NanoBEIREvaluator

```bibtext
{'NanoClimateFEVER_cosine_accuracy@1': 0.28,
 'NanoClimateFEVER_cosine_accuracy@3': 0.44,
 'NanoClimateFEVER_cosine_accuracy@5': 0.54,
 'NanoClimateFEVER_cosine_accuracy@10': 0.72,
 'NanoClimateFEVER_cosine_precision@1': 0.28,
 'NanoClimateFEVER_cosine_precision@3': 0.15333333333333332,
 'NanoClimateFEVER_cosine_precision@5': 0.124,
 'NanoClimateFEVER_cosine_precision@10': 0.08999999999999998,
 'NanoClimateFEVER_cosine_recall@1': 0.145,
 'NanoClimateFEVER_cosine_recall@3': 0.205,
 'NanoClimateFEVER_cosine_recall@5': 0.264,
 'NanoClimateFEVER_cosine_recall@10': 0.36200000000000004,
 'NanoClimateFEVER_cosine_ndcg@10': 0.2957527689242254,
 'NanoClimateFEVER_cosine_mrr@10': 0.3996666666666668,
 'NanoClimateFEVER_cosine_map@100': 0.23258384801937396,
 'NanoDBPedia_cosine_accuracy@1': 0.68,
 'NanoDBPedia_cosine_accuracy@3': 0.86,
 'NanoDBPedia_cosine_accuracy@5': 0.92,
 'NanoDBPedia_cosine_accuracy@10': 0.96,
 'NanoDBPedia_cosine_precision@1': 0.68,
 'NanoDBPedia_cosine_precision@3': 0.56,
 'NanoDBPedia_cosine_precision@5': 0.5120000000000001,
 'NanoDBPedia_cosine_precision@10': 0.43800000000000006,
 'NanoDBPedia_cosine_recall@1': 0.07601531530835434,
 'NanoDBPedia_cosine_recall@3': 0.1438904710839341,
 'NanoDBPedia_cosine_recall@5': 0.20681359525684506,
 'NanoDBPedia_cosine_recall@10': 0.319966975132044,
 'NanoDBPedia_cosine_ndcg@10': 0.5501100350453579,
 'NanoDBPedia_cosine_mrr@10': 0.7855000000000001,
 'NanoDBPedia_cosine_map@100': 0.39476156890024533,
 'NanoFEVER_cosine_accuracy@1': 0.68,
 'NanoFEVER_cosine_accuracy@3': 0.86,
 'NanoFEVER_cosine_accuracy@5': 0.92,
 'NanoFEVER_cosine_accuracy@10': 0.96,
 'NanoFEVER_cosine_precision@1': 0.68,
 'NanoFEVER_cosine_precision@3': 0.29333333333333333,
 'NanoFEVER_cosine_precision@5': 0.19199999999999995,
 'NanoFEVER_cosine_precision@10': 0.10199999999999998,
 'NanoFEVER_cosine_recall@1': 0.6266666666666666,
 'NanoFEVER_cosine_recall@3': 0.8133333333333332,
 'NanoFEVER_cosine_recall@5': 0.8833333333333333,
 'NanoFEVER_cosine_recall@10': 0.9233333333333333,
 'NanoFEVER_cosine_ndcg@10': 0.7933479848498471,
 'NanoFEVER_cosine_mrr@10': 0.7780793650793651,
 'NanoFEVER_cosine_map@100': 0.7406571665049926,
 'NanoFiQA2018_cosine_accuracy@1': 0.46,
 'NanoFiQA2018_cosine_accuracy@3': 0.64,
 'NanoFiQA2018_cosine_accuracy@5': 0.7,
 'NanoFiQA2018_cosine_accuracy@10': 0.72,
 'NanoFiQA2018_cosine_precision@1': 0.46,
 'NanoFiQA2018_cosine_precision@3': 0.2866666666666666,
 'NanoFiQA2018_cosine_precision@5': 0.22399999999999998,
 'NanoFiQA2018_cosine_precision@10': 0.12999999999999998,
 'NanoFiQA2018_cosine_recall@1': 0.23924603174603173,
 'NanoFiQA2018_cosine_recall@3': 0.4251031746031746,
 'NanoFiQA2018_cosine_recall@5': 0.5099603174603174,
 'NanoFiQA2018_cosine_recall@10': 0.566015873015873,
 'NanoFiQA2018_cosine_ndcg@10': 0.4774545077577204,
 'NanoFiQA2018_cosine_mrr@10': 0.5475555555555556,
 'NanoFiQA2018_cosine_map@100': 0.4125452702654584,
 'NanoHotpotQA_cosine_accuracy@1': 0.64,
 'NanoHotpotQA_cosine_accuracy@3': 0.82,
 'NanoHotpotQA_cosine_accuracy@5': 0.84,
 'NanoHotpotQA_cosine_accuracy@10': 0.88,
 'NanoHotpotQA_cosine_precision@1': 0.64,
 'NanoHotpotQA_cosine_precision@3': 0.3533333333333333,
 'NanoHotpotQA_cosine_precision@5': 0.23599999999999993,
 'NanoHotpotQA_cosine_precision@10': 0.128,
 'NanoHotpotQA_cosine_recall@1': 0.32,
 'NanoHotpotQA_cosine_recall@3': 0.53,
 'NanoHotpotQA_cosine_recall@5': 0.59,
 'NanoHotpotQA_cosine_recall@10': 0.64,
 'NanoHotpotQA_cosine_ndcg@10': 0.5959681682828366,
 'NanoHotpotQA_cosine_mrr@10': 0.723888888888889,
 'NanoHotpotQA_cosine_map@100': 0.5262469568756968,
 'NanoMSMARCO_cosine_accuracy@1': 0.36,
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 'NanoMSMARCO_cosine_accuracy@5': 0.58,
 'NanoMSMARCO_cosine_accuracy@10': 0.8,
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 'NanoMSMARCO_cosine_precision@10': 0.08,
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 'NanoMSMARCO_cosine_ndcg@10': 0.5539831330912274,
 'NanoMSMARCO_cosine_mrr@10': 0.47960317460317464,
 'NanoMSMARCO_cosine_map@100': 0.4907628900864195,
 'NanoNFCorpus_cosine_accuracy@1': 0.42,
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 'NanoNFCorpus_cosine_accuracy@5': 0.6,
 'NanoNFCorpus_cosine_accuracy@10': 0.7,
 'NanoNFCorpus_cosine_precision@1': 0.42,
 'NanoNFCorpus_cosine_precision@3': 0.3466666666666666,
 'NanoNFCorpus_cosine_precision@5': 0.32800000000000007,
 'NanoNFCorpus_cosine_precision@10': 0.286,
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 'NanoNFCorpus_cosine_recall@3': 0.06311668492872162,
 'NanoNFCorpus_cosine_recall@5': 0.08191277059586696,
 'NanoNFCorpus_cosine_recall@10': 0.13476845853527392,
 'NanoNFCorpus_cosine_ndcg@10': 0.3322933792371396,
 'NanoNFCorpus_cosine_mrr@10': 0.4983333333333333,
 'NanoNFCorpus_cosine_map@100': 0.13985354018581944,
 'NanoNQ_cosine_accuracy@1': 0.44,
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 'NanoNQ_cosine_accuracy@5': 0.66,
 'NanoNQ_cosine_accuracy@10': 0.76,
 'NanoNQ_cosine_precision@1': 0.44,
 'NanoNQ_cosine_precision@3': 0.22,
 'NanoNQ_cosine_precision@5': 0.14,
 'NanoNQ_cosine_precision@10': 0.08199999999999999,
 'NanoNQ_cosine_recall@1': 0.42,
 'NanoNQ_cosine_recall@3': 0.62,
 'NanoNQ_cosine_recall@5': 0.64,
 'NanoNQ_cosine_recall@10': 0.75,
 'NanoNQ_cosine_ndcg@10': 0.5903874296113161,
 'NanoNQ_cosine_mrr@10': 0.5456349206349206,
 'NanoNQ_cosine_map@100': 0.5437440035864959,
 'NanoQuoraRetrieval_cosine_accuracy@1': 0.88,
 'NanoQuoraRetrieval_cosine_accuracy@3': 0.96,
 'NanoQuoraRetrieval_cosine_accuracy@5': 1.0,
 'NanoQuoraRetrieval_cosine_accuracy@10': 1.0,
 'NanoQuoraRetrieval_cosine_precision@1': 0.88,
 'NanoQuoraRetrieval_cosine_precision@3': 0.3933333333333333,
 'NanoQuoraRetrieval_cosine_precision@5': 0.256,
 'NanoQuoraRetrieval_cosine_precision@10': 0.13599999999999998,
 'NanoQuoraRetrieval_cosine_recall@1': 0.784,
 'NanoQuoraRetrieval_cosine_recall@3': 0.9186666666666667,
 'NanoQuoraRetrieval_cosine_recall@5': 0.976,
 'NanoQuoraRetrieval_cosine_recall@10': 0.9933333333333334,
 'NanoQuoraRetrieval_cosine_ndcg@10': 0.9367841595958026,
 'NanoQuoraRetrieval_cosine_mrr@10': 0.9246666666666666,
 'NanoQuoraRetrieval_cosine_map@100': 0.913554834054834,
 'NanoSCIDOCS_cosine_accuracy@1': 0.52,
 'NanoSCIDOCS_cosine_accuracy@3': 0.68,
 'NanoSCIDOCS_cosine_accuracy@5': 0.82,
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 'NanoSCIDOCS_cosine_precision@5': 0.33599999999999997,
 'NanoSCIDOCS_cosine_precision@10': 0.21600000000000003,
 'NanoSCIDOCS_cosine_recall@1': 0.10966666666666666,
 'NanoSCIDOCS_cosine_recall@3': 0.24466666666666664,
 'NanoSCIDOCS_cosine_recall@5': 0.34566666666666657,
 'NanoSCIDOCS_cosine_recall@10': 0.44266666666666665,
 'NanoSCIDOCS_cosine_ndcg@10': 0.4328110226758414,
 'NanoSCIDOCS_cosine_mrr@10': 0.6317222222222222,
 'NanoSCIDOCS_cosine_map@100': 0.34997841607847063,
 'NanoArguAna_cosine_accuracy@1': 0.2,
 'NanoArguAna_cosine_accuracy@3': 0.56,
 'NanoArguAna_cosine_accuracy@5': 0.76,
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 'NanoArguAna_cosine_precision@1': 0.2,
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 'NanoArguAna_cosine_precision@5': 0.15200000000000002,
 'NanoArguAna_cosine_precision@10': 0.092,
 'NanoArguAna_cosine_recall@1': 0.2,
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 'NanoArguAna_cosine_ndcg@10': 0.5499071039525992,
 'NanoArguAna_cosine_mrr@10': 0.43229365079365073,
 'NanoArguAna_cosine_map@100': 0.43523820792684886,
 'NanoSciFact_cosine_accuracy@1': 0.6,
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 'NanoSciFact_cosine_precision@5': 0.18,
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 'NanoSciFact_cosine_recall@1': 0.58,
 'NanoSciFact_cosine_recall@3': 0.7,
 'NanoSciFact_cosine_recall@5': 0.8,
 'NanoSciFact_cosine_recall@10': 0.87,
 'NanoSciFact_cosine_ndcg@10': 0.7265348054031264,
 'NanoSciFact_cosine_mrr@10': 0.6841031746031746,
 'NanoSciFact_cosine_map@100': 0.6810233866101422,
 'NanoTouche2020_cosine_accuracy@1': 0.5102040816326531,
 'NanoTouche2020_cosine_accuracy@3': 0.8367346938775511,
 'NanoTouche2020_cosine_accuracy@5': 0.9183673469387755,
 'NanoTouche2020_cosine_accuracy@10': 0.9387755102040817,
 'NanoTouche2020_cosine_precision@1': 0.5102040816326531,
 'NanoTouche2020_cosine_precision@3': 0.5374149659863945,
 'NanoTouche2020_cosine_precision@5': 0.5061224489795918,
 'NanoTouche2020_cosine_precision@10': 0.43265306122448977,
 'NanoTouche2020_cosine_recall@1': 0.03546508562664911,
 'NanoTouche2020_cosine_recall@3': 0.11189238805791148,
 'NanoTouche2020_cosine_recall@5': 0.1673503566176574,
 'NanoTouche2020_cosine_recall@10': 0.2818808841266296,
 'NanoTouche2020_cosine_ndcg@10': 0.47479704449085264,
 'NanoTouche2020_cosine_mrr@10': 0.6714285714285714,
 'NanoTouche2020_cosine_map@100': 0.3438320372291555,
 'NanoBEIR_mean_cosine_accuracy@1': 0.5130926216640502,
 'NanoBEIR_mean_cosine_accuracy@3': 0.6997488226059654,
 'NanoBEIR_mean_cosine_accuracy@5': 0.7737205651491367,
 'NanoBEIR_mean_cosine_accuracy@10': 0.8583673469387756,
 'NanoBEIR_mean_cosine_precision@1': 0.5130926216640502,
 'NanoBEIR_mean_cosine_precision@3': 0.31928833071690216,
 'NanoBEIR_mean_cosine_precision@5': 0.2540094191522763,
 'NanoBEIR_mean_cosine_precision@10': 0.1777425431711146,
 'NanoBEIR_mean_cosine_recall@1': 0.302305611570001,
 'NanoBEIR_mean_cosine_recall@3': 0.4504361065646467,
 'NanoBEIR_mean_cosine_recall@5': 0.5234643876869758,
 'NanoBEIR_mean_cosine_recall@10': 0.6156896557033196,
 'NanoBEIR_mean_cosine_ndcg@10': 0.5623178109936842,
 'NanoBEIR_mean_cosine_mrr@10': 0.6232673992673993,
 'NanoBEIR_mean_cosine_map@100': 0.47729093279415025}
```

## 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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