sentence-transformers safetensors nomic_bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1115700 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss custom_code arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:nomic-ai/nomic-embed-text-v1.5 base_model:finetune:nomic-ai/nomic-embed-text-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: nomic-ai/nomic-embed-text-v1.5
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1115700
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Ndege mwenye mdomo mrefu katikati ya ndege.
  sentences:
  - Panya anayekimbia juu ya gurudumu.
  - Mtu anashindana katika mashindano ya mbio.
  - Ndege anayeruka.
- source_sentence: Msichana mchanga mwenye nywele nyeusi anakabili kamera na kushikilia
    mfuko wa karatasi wakati amevaa shati la machungwa na mabawa ya kipepeo yenye
    rangi nyingi.
  sentences:
  - Mwanamke mzee anakataa kupigwa picha.
  - mtu akila na mvulana mdogo kwenye kijia cha jiji
  - Msichana mchanga anakabili kamera.
- source_sentence: Wanawake na watoto wameketi nje katika kivuli wakati kikundi cha
    watoto wadogo wameketi ndani katika kivuli.
  sentences:
  - Mwanamke na watoto na kukaa chini.
  - Mwanamke huyo anakimbia.
  - Watu wanasafiri kwa baiskeli.
- source_sentence: Mtoto mdogo anaruka mikononi mwa mwanamke aliyevalia suti nyeusi
    ya kuogelea akiwa kwenye dimbwi.
  sentences:
  - Mtoto akiruka mikononi mwa mwanamke aliyevalia suti ya kuogelea kwenye dimbwi.
  - Someone is holding oranges and walking
  - Mama na binti wakinunua viatu.
- source_sentence: Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa
    kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi
    nyuma.
  sentences:
  - tai huruka
  - mwanamume na mwanamke wenye mikoba
  - Wanaume wawili wameketi karibu na mwanamke.
model-index:
- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.6944960057464138
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6872396378196957
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7086043588614903
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7136479613274518
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7084460037709435
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7128357831285198
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.481902874304561
      name: Pearson Dot
    - type: spearman_dot
      value: 0.46588918379526945
      name: Spearman Dot
    - type: pearson_max
      value: 0.7086043588614903
      name: Pearson Max
    - type: spearman_max
      value: 0.7136479613274518
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.6925787246105148
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6859479129419207
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7087290093387656
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7127968133455542
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7088805484816247
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7123606046721803
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4684333245586192
      name: Pearson Dot
    - type: spearman_dot
      value: 0.45257836578849003
      name: Spearman Dot
    - type: pearson_max
      value: 0.7088805484816247
      name: Pearson Max
    - type: spearman_max
      value: 0.7127968133455542
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.6876956481856266
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6814892249857147
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7083882582081078
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7097524143994903
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7094190252305796
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7104287347206688
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4438925722484721
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4255299982188107
      name: Spearman Dot
    - type: pearson_max
      value: 0.7094190252305796
      name: Pearson Max
    - type: spearman_max
      value: 0.7104287347206688
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.6708560165075523
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6669935075512006
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7041961281711793
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7000807688296651
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7055061381768357
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7022686907818495
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.37855771167572094
      name: Pearson Dot
    - type: spearman_dot
      value: 0.35930717422088765
      name: Spearman Dot
    - type: pearson_max
      value: 0.7055061381768357
      name: Pearson Max
    - type: spearman_max
      value: 0.7022686907818495
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.6533817775144477
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6523997361414113
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6919834348567717
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6857245312336051
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6950438027503257
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6899151458827059
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.33502302384042637
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3097469345046609
      name: Spearman Dot
    - type: pearson_max
      value: 0.6950438027503257
      name: Pearson Max
    - type: spearman_max
      value: 0.6899151458827059
      name: Spearman Max
---

# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) on the Mollel/swahili-n_li-triplet-swh-eng dataset. 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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision b0753ae76394dd36bcfb912a46018088bca48be0 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - Mollel/swahili-n_li-triplet-swh-eng
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Mollel/MultiLinguSwahili-nomic-embed-text-v1.5-nli-matryoshka")
# Run inference
sentences = [
    'Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma.',
    'mwanamume na mwanamke wenye mikoba',
    'tai huruka',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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

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## Evaluation

### Metrics

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6945     |
| **spearman_cosine** | **0.6872** |
| pearson_manhattan   | 0.7086     |
| spearman_manhattan  | 0.7136     |
| pearson_euclidean   | 0.7084     |
| spearman_euclidean  | 0.7128     |
| pearson_dot         | 0.4819     |
| spearman_dot        | 0.4659     |
| pearson_max         | 0.7086     |
| spearman_max        | 0.7136     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6926     |
| **spearman_cosine** | **0.6859** |
| pearson_manhattan   | 0.7087     |
| spearman_manhattan  | 0.7128     |
| pearson_euclidean   | 0.7089     |
| spearman_euclidean  | 0.7124     |
| pearson_dot         | 0.4684     |
| spearman_dot        | 0.4526     |
| pearson_max         | 0.7089     |
| spearman_max        | 0.7128     |

#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6877     |
| **spearman_cosine** | **0.6815** |
| pearson_manhattan   | 0.7084     |
| spearman_manhattan  | 0.7098     |
| pearson_euclidean   | 0.7094     |
| spearman_euclidean  | 0.7104     |
| pearson_dot         | 0.4439     |
| spearman_dot        | 0.4255     |
| pearson_max         | 0.7094     |
| spearman_max        | 0.7104     |

#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.6709    |
| **spearman_cosine** | **0.667** |
| pearson_manhattan   | 0.7042    |
| spearman_manhattan  | 0.7001    |
| pearson_euclidean   | 0.7055    |
| spearman_euclidean  | 0.7023    |
| pearson_dot         | 0.3786    |
| spearman_dot        | 0.3593    |
| pearson_max         | 0.7055    |
| spearman_max        | 0.7023    |

#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6534     |
| **spearman_cosine** | **0.6524** |
| pearson_manhattan   | 0.692      |
| spearman_manhattan  | 0.6857     |
| pearson_euclidean   | 0.695      |
| spearman_euclidean  | 0.6899     |
| pearson_dot         | 0.335      |
| spearman_dot        | 0.3097     |
| pearson_max         | 0.695      |
| spearman_max        | 0.6899     |

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## Training Details

### Training Dataset

#### Mollel/swahili-n_li-triplet-swh-eng

* Dataset: Mollel/swahili-n_li-triplet-swh-eng
* Size: 1,115,700 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 15.18 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 18.53 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.8 tokens</li><li>max: 53 tokens</li></ul> |
* Samples:
  | anchor                                                                | positive                                       | negative                                                   |
  |:----------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|
  | <code>A person on a horse jumps over a broken down airplane.</code>   | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
  | <code>Mtu aliyepanda farasi anaruka juu ya ndege iliyovunjika.</code> | <code>Mtu yuko nje, juu ya farasi.</code>      | <code>Mtu yuko kwenye mkahawa, akiagiza omelette.</code>   |
  | <code>Children smiling and waving at camera</code>                    | <code>There are children present</code>        | <code>The kids are frowning</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
  }
  ```

### Evaluation Dataset

#### Mollel/swahili-n_li-triplet-swh-eng

* Dataset: Mollel/swahili-n_li-triplet-swh-eng
* Size: 13,168 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                           |
  | details | <ul><li>min: 6 tokens</li><li>mean: 26.43 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.37 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.7 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                         | positive                                                    | negative                                                           |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:-------------------------------------------------------------------|
  | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                | <code>The men are fighting outside a deli.</code>                  |
  | <code>Wanawake wawili wanakumbatiana huku wakishikilia vifurushi vya kwenda.</code>                                                                                            | <code>Wanawake wawili wanashikilia vifurushi.</code>        | <code>Wanaume hao wanapigana nje ya duka la vyakula vitamu.</code> |
  | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</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

- `per_device_train_batch_size`: 24
- `per_device_eval_batch_size`: 24
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 24
- `per_device_eval_batch_size`: 24
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch  | Step  | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0043 | 100   | 10.0627       | -                            | -                            | -                            | -                           | -                            |
| 0.0086 | 200   | 8.2355        | -                            | -                            | -                            | -                           | -                            |
| 0.0129 | 300   | 6.7233        | -                            | -                            | -                            | -                           | -                            |
| 0.0172 | 400   | 6.5832        | -                            | -                            | -                            | -                           | -                            |
| 0.0215 | 500   | 6.7512        | -                            | -                            | -                            | -                           | -                            |
| 0.0258 | 600   | 6.7634        | -                            | -                            | -                            | -                           | -                            |
| 0.0301 | 700   | 6.5592        | -                            | -                            | -                            | -                           | -                            |
| 0.0344 | 800   | 5.0689        | -                            | -                            | -                            | -                           | -                            |
| 0.0387 | 900   | 4.7079        | -                            | -                            | -                            | -                           | -                            |
| 0.0430 | 1000  | 4.6359        | -                            | -                            | -                            | -                           | -                            |
| 0.0473 | 1100  | 4.4513        | -                            | -                            | -                            | -                           | -                            |
| 0.0516 | 1200  | 4.2328        | -                            | -                            | -                            | -                           | -                            |
| 0.0559 | 1300  | 3.7454        | -                            | -                            | -                            | -                           | -                            |
| 0.0602 | 1400  | 3.9198        | -                            | -                            | -                            | -                           | -                            |
| 0.0645 | 1500  | 4.0727        | -                            | -                            | -                            | -                           | -                            |
| 0.0688 | 1600  | 3.8923        | -                            | -                            | -                            | -                           | -                            |
| 0.0731 | 1700  | 3.8137        | -                            | -                            | -                            | -                           | -                            |
| 0.0774 | 1800  | 4.1512        | -                            | -                            | -                            | -                           | -                            |
| 0.0817 | 1900  | 4.1304        | -                            | -                            | -                            | -                           | -                            |
| 0.0860 | 2000  | 4.0195        | -                            | -                            | -                            | -                           | -                            |
| 0.0903 | 2100  | 3.6836        | -                            | -                            | -                            | -                           | -                            |
| 0.0946 | 2200  | 2.9968        | -                            | -                            | -                            | -                           | -                            |
| 0.0990 | 2300  | 2.8909        | -                            | -                            | -                            | -                           | -                            |
| 0.1033 | 2400  | 3.0884        | -                            | -                            | -                            | -                           | -                            |
| 0.1076 | 2500  | 3.3081        | -                            | -                            | -                            | -                           | -                            |
| 0.1119 | 2600  | 3.6266        | -                            | -                            | -                            | -                           | -                            |
| 0.1162 | 2700  | 4.3754        | -                            | -                            | -                            | -                           | -                            |
| 0.1205 | 2800  | 4.0218        | -                            | -                            | -                            | -                           | -                            |
| 0.1248 | 2900  | 3.7167        | -                            | -                            | -                            | -                           | -                            |
| 0.1291 | 3000  | 3.4815        | -                            | -                            | -                            | -                           | -                            |
| 0.1334 | 3100  | 3.6446        | -                            | -                            | -                            | -                           | -                            |
| 0.1377 | 3200  | 3.44          | -                            | -                            | -                            | -                           | -                            |
| 0.1420 | 3300  | 3.6725        | -                            | -                            | -                            | -                           | -                            |
| 0.1463 | 3400  | 3.4699        | -                            | -                            | -                            | -                           | -                            |
| 0.1506 | 3500  | 3.076         | -                            | -                            | -                            | -                           | -                            |
| 0.1549 | 3600  | 3.1179        | -                            | -                            | -                            | -                           | -                            |
| 0.1592 | 3700  | 3.1704        | -                            | -                            | -                            | -                           | -                            |
| 0.1635 | 3800  | 3.4614        | -                            | -                            | -                            | -                           | -                            |
| 0.1678 | 3900  | 4.1157        | -                            | -                            | -                            | -                           | -                            |
| 0.1721 | 4000  | 4.1584        | -                            | -                            | -                            | -                           | -                            |
| 0.1764 | 4100  | 4.5602        | -                            | -                            | -                            | -                           | -                            |
| 0.1807 | 4200  | 3.6875        | -                            | -                            | -                            | -                           | -                            |
| 0.1850 | 4300  | 4.1521        | -                            | -                            | -                            | -                           | -                            |
| 0.1893 | 4400  | 3.5475        | -                            | -                            | -                            | -                           | -                            |
| 0.1936 | 4500  | 3.4036        | -                            | -                            | -                            | -                           | -                            |
| 0.1979 | 4600  | 3.0564        | -                            | -                            | -                            | -                           | -                            |
| 0.2022 | 4700  | 3.7761        | -                            | -                            | -                            | -                           | -                            |
| 0.2065 | 4800  | 3.6857        | -                            | -                            | -                            | -                           | -                            |
| 0.2108 | 4900  | 3.3534        | -                            | -                            | -                            | -                           | -                            |
| 0.2151 | 5000  | 4.1137        | -                            | -                            | -                            | -                           | -                            |
| 0.2194 | 5100  | 3.5239        | -                            | -                            | -                            | -                           | -                            |
| 0.2237 | 5200  | 4.1297        | -                            | -                            | -                            | -                           | -                            |
| 0.2280 | 5300  | 3.5339        | -                            | -                            | -                            | -                           | -                            |
| 0.2323 | 5400  | 3.9294        | -                            | -                            | -                            | -                           | -                            |
| 0.2366 | 5500  | 3.717         | -                            | -                            | -                            | -                           | -                            |
| 0.2409 | 5600  | 3.3346        | -                            | -                            | -                            | -                           | -                            |
| 0.2452 | 5700  | 4.0495        | -                            | -                            | -                            | -                           | -                            |
| 0.2495 | 5800  | 3.7869        | -                            | -                            | -                            | -                           | -                            |
| 0.2538 | 5900  | 3.9533        | -                            | -                            | -                            | -                           | -                            |
| 0.2581 | 6000  | 4.1135        | -                            | -                            | -                            | -                           | -                            |
| 0.2624 | 6100  | 3.6655        | -                            | -                            | -                            | -                           | -                            |
| 0.2667 | 6200  | 3.9111        | -                            | -                            | -                            | -                           | -                            |
| 0.2710 | 6300  | 3.8582        | -                            | -                            | -                            | -                           | -                            |
| 0.2753 | 6400  | 3.7712        | -                            | -                            | -                            | -                           | -                            |
| 0.2796 | 6500  | 3.6536        | -                            | -                            | -                            | -                           | -                            |
| 0.2839 | 6600  | 3.4516        | -                            | -                            | -                            | -                           | -                            |
| 0.2882 | 6700  | 3.7151        | -                            | -                            | -                            | -                           | -                            |
| 0.2925 | 6800  | 3.7659        | -                            | -                            | -                            | -                           | -                            |
| 0.2969 | 6900  | 3.3159        | -                            | -                            | -                            | -                           | -                            |
| 0.3012 | 7000  | 3.5753        | -                            | -                            | -                            | -                           | -                            |
| 0.3055 | 7100  | 4.2095        | -                            | -                            | -                            | -                           | -                            |
| 0.3098 | 7200  | 3.718         | -                            | -                            | -                            | -                           | -                            |
| 0.3141 | 7300  | 4.0709        | -                            | -                            | -                            | -                           | -                            |
| 0.3184 | 7400  | 3.8079        | -                            | -                            | -                            | -                           | -                            |
| 0.3227 | 7500  | 3.3735        | -                            | -                            | -                            | -                           | -                            |
| 0.3270 | 7600  | 3.7303        | -                            | -                            | -                            | -                           | -                            |
| 0.3313 | 7700  | 3.2693        | -                            | -                            | -                            | -                           | -                            |
| 0.3356 | 7800  | 3.6564        | -                            | -                            | -                            | -                           | -                            |
| 0.3399 | 7900  | 3.6702        | -                            | -                            | -                            | -                           | -                            |
| 0.3442 | 8000  | 3.7274        | -                            | -                            | -                            | -                           | -                            |
| 0.3485 | 8100  | 3.8536        | -                            | -                            | -                            | -                           | -                            |
| 0.3528 | 8200  | 3.9516        | -                            | -                            | -                            | -                           | -                            |
| 0.3571 | 8300  | 3.7351        | -                            | -                            | -                            | -                           | -                            |
| 0.3614 | 8400  | 3.649         | -                            | -                            | -                            | -                           | -                            |
| 0.3657 | 8500  | 3.5913        | -                            | -                            | -                            | -                           | -                            |
| 0.3700 | 8600  | 3.7733        | -                            | -                            | -                            | -                           | -                            |
| 0.3743 | 8700  | 3.6359        | -                            | -                            | -                            | -                           | -                            |
| 0.3786 | 8800  | 4.2983        | -                            | -                            | -                            | -                           | -                            |
| 0.3829 | 8900  | 3.6692        | -                            | -                            | -                            | -                           | -                            |
| 0.3872 | 9000  | 3.7309        | -                            | -                            | -                            | -                           | -                            |
| 0.3915 | 9100  | 3.8886        | -                            | -                            | -                            | -                           | -                            |
| 0.3958 | 9200  | 3.8999        | -                            | -                            | -                            | -                           | -                            |
| 0.4001 | 9300  | 3.5528        | -                            | -                            | -                            | -                           | -                            |
| 0.4044 | 9400  | 3.6309        | -                            | -                            | -                            | -                           | -                            |
| 0.4087 | 9500  | 4.2475        | -                            | -                            | -                            | -                           | -                            |
| 0.4130 | 9600  | 3.793         | -                            | -                            | -                            | -                           | -                            |
| 0.4173 | 9700  | 3.6575        | -                            | -                            | -                            | -                           | -                            |
| 0.4216 | 9800  | 3.84          | -                            | -                            | -                            | -                           | -                            |
| 0.4259 | 9900  | 3.3721        | -                            | -                            | -                            | -                           | -                            |
| 0.4302 | 10000 | 4.3743        | -                            | -                            | -                            | -                           | -                            |
| 0.4345 | 10100 | 3.5054        | -                            | -                            | -                            | -                           | -                            |
| 0.4388 | 10200 | 3.54          | -                            | -                            | -                            | -                           | -                            |
| 0.4431 | 10300 | 3.6197        | -                            | -                            | -                            | -                           | -                            |
| 0.4474 | 10400 | 3.7567        | -                            | -                            | -                            | -                           | -                            |
| 0.4517 | 10500 | 3.9814        | -                            | -                            | -                            | -                           | -                            |
| 0.4560 | 10600 | 3.6277        | -                            | -                            | -                            | -                           | -                            |
| 0.4603 | 10700 | 3.5071        | -                            | -                            | -                            | -                           | -                            |
| 0.4646 | 10800 | 3.8348        | -                            | -                            | -                            | -                           | -                            |
| 0.4689 | 10900 | 3.8674        | -                            | -                            | -                            | -                           | -                            |
| 0.4732 | 11000 | 3.0325        | -                            | -                            | -                            | -                           | -                            |
| 0.4775 | 11100 | 3.7262        | -                            | -                            | -                            | -                           | -                            |
| 0.4818 | 11200 | 3.6921        | -                            | -                            | -                            | -                           | -                            |
| 0.4861 | 11300 | 3.4946        | -                            | -                            | -                            | -                           | -                            |
| 0.4904 | 11400 | 3.7541        | -                            | -                            | -                            | -                           | -                            |
| 0.4948 | 11500 | 3.6751        | -                            | -                            | -                            | -                           | -                            |
| 0.4991 | 11600 | 3.8765        | -                            | -                            | -                            | -                           | -                            |
| 0.5034 | 11700 | 3.5058        | -                            | -                            | -                            | -                           | -                            |
| 0.5077 | 11800 | 3.5135        | -                            | -                            | -                            | -                           | -                            |
| 0.5120 | 11900 | 3.8052        | -                            | -                            | -                            | -                           | -                            |
| 0.5163 | 12000 | 3.3015        | -                            | -                            | -                            | -                           | -                            |
| 0.5206 | 12100 | 3.5389        | -                            | -                            | -                            | -                           | -                            |
| 0.5249 | 12200 | 3.5226        | -                            | -                            | -                            | -                           | -                            |
| 0.5292 | 12300 | 3.6715        | -                            | -                            | -                            | -                           | -                            |
| 0.5335 | 12400 | 3.2256        | -                            | -                            | -                            | -                           | -                            |
| 0.5378 | 12500 | 3.3447        | -                            | -                            | -                            | -                           | -                            |
| 0.5421 | 12600 | 3.6315        | -                            | -                            | -                            | -                           | -                            |
| 0.5464 | 12700 | 3.8674        | -                            | -                            | -                            | -                           | -                            |
| 0.5507 | 12800 | 3.4066        | -                            | -                            | -                            | -                           | -                            |
| 0.5550 | 12900 | 3.7356        | -                            | -                            | -                            | -                           | -                            |
| 0.5593 | 13000 | 3.5742        | -                            | -                            | -                            | -                           | -                            |
| 0.5636 | 13100 | 3.7676        | -                            | -                            | -                            | -                           | -                            |
| 0.5679 | 13200 | 3.7907        | -                            | -                            | -                            | -                           | -                            |
| 0.5722 | 13300 | 3.8089        | -                            | -                            | -                            | -                           | -                            |
| 0.5765 | 13400 | 3.4742        | -                            | -                            | -                            | -                           | -                            |
| 0.5808 | 13500 | 3.6536        | -                            | -                            | -                            | -                           | -                            |
| 0.5851 | 13600 | 3.7736        | -                            | -                            | -                            | -                           | -                            |
| 0.5894 | 13700 | 3.9072        | -                            | -                            | -                            | -                           | -                            |
| 0.5937 | 13800 | 3.7386        | -                            | -                            | -                            | -                           | -                            |
| 0.5980 | 13900 | 3.3387        | -                            | -                            | -                            | -                           | -                            |
| 0.6023 | 14000 | 3.5509        | -                            | -                            | -                            | -                           | -                            |
| 0.6066 | 14100 | 3.7056        | -                            | -                            | -                            | -                           | -                            |
| 0.6109 | 14200 | 3.7283        | -                            | -                            | -                            | -                           | -                            |
| 0.6152 | 14300 | 3.7301        | -                            | -                            | -                            | -                           | -                            |
| 0.6195 | 14400 | 3.8027        | -                            | -                            | -                            | -                           | -                            |
| 0.6238 | 14500 | 3.5606        | -                            | -                            | -                            | -                           | -                            |
| 0.6281 | 14600 | 3.9467        | -                            | -                            | -                            | -                           | -                            |
| 0.6324 | 14700 | 3.3394        | -                            | -                            | -                            | -                           | -                            |
| 0.6367 | 14800 | 4.1254        | -                            | -                            | -                            | -                           | -                            |
| 0.6410 | 14900 | 3.7121        | -                            | -                            | -                            | -                           | -                            |
| 0.6453 | 15000 | 3.9167        | -                            | -                            | -                            | -                           | -                            |
| 0.6496 | 15100 | 3.8084        | -                            | -                            | -                            | -                           | -                            |
| 0.6539 | 15200 | 3.7794        | -                            | -                            | -                            | -                           | -                            |
| 0.6582 | 15300 | 3.7664        | -                            | -                            | -                            | -                           | -                            |
| 0.6625 | 15400 | 3.4378        | -                            | -                            | -                            | -                           | -                            |
| 0.6668 | 15500 | 3.6632        | -                            | -                            | -                            | -                           | -                            |
| 0.6711 | 15600 | 3.8493        | -                            | -                            | -                            | -                           | -                            |
| 0.6754 | 15700 | 4.1475        | -                            | -                            | -                            | -                           | -                            |
| 0.6797 | 15800 | 3.5782        | -                            | -                            | -                            | -                           | -                            |
| 0.6840 | 15900 | 3.4341        | -                            | -                            | -                            | -                           | -                            |
| 0.6883 | 16000 | 3.3295        | -                            | -                            | -                            | -                           | -                            |
| 0.6927 | 16100 | 3.8165        | -                            | -                            | -                            | -                           | -                            |
| 0.6970 | 16200 | 3.9702        | -                            | -                            | -                            | -                           | -                            |
| 0.7013 | 16300 | 3.6555        | -                            | -                            | -                            | -                           | -                            |
| 0.7056 | 16400 | 3.6946        | -                            | -                            | -                            | -                           | -                            |
| 0.7099 | 16500 | 3.8027        | -                            | -                            | -                            | -                           | -                            |
| 0.7142 | 16600 | 3.4523        | -                            | -                            | -                            | -                           | -                            |
| 0.7185 | 16700 | 3.461         | -                            | -                            | -                            | -                           | -                            |
| 0.7228 | 16800 | 3.4403        | -                            | -                            | -                            | -                           | -                            |
| 0.7271 | 16900 | 3.6398        | -                            | -                            | -                            | -                           | -                            |
| 0.7314 | 17000 | 3.8443        | -                            | -                            | -                            | -                           | -                            |
| 0.7357 | 17100 | 3.6012        | -                            | -                            | -                            | -                           | -                            |
| 0.7400 | 17200 | 3.6645        | -                            | -                            | -                            | -                           | -                            |
| 0.7443 | 17300 | 3.4899        | -                            | -                            | -                            | -                           | -                            |
| 0.7486 | 17400 | 3.7186        | -                            | -                            | -                            | -                           | -                            |
| 0.7529 | 17500 | 3.6199        | -                            | -                            | -                            | -                           | -                            |
| 0.7572 | 17600 | 4.4274        | -                            | -                            | -                            | -                           | -                            |
| 0.7615 | 17700 | 4.0262        | -                            | -                            | -                            | -                           | -                            |
| 0.7658 | 17800 | 3.9325        | -                            | -                            | -                            | -                           | -                            |
| 0.7701 | 17900 | 3.6338        | -                            | -                            | -                            | -                           | -                            |
| 0.7744 | 18000 | 3.6136        | -                            | -                            | -                            | -                           | -                            |
| 0.7787 | 18100 | 3.4514        | -                            | -                            | -                            | -                           | -                            |
| 0.7830 | 18200 | 3.4427        | -                            | -                            | -                            | -                           | -                            |
| 0.7873 | 18300 | 3.3601        | -                            | -                            | -                            | -                           | -                            |
| 0.7916 | 18400 | 3.313         | -                            | -                            | -                            | -                           | -                            |
| 0.7959 | 18500 | 3.4062        | -                            | -                            | -                            | -                           | -                            |
| 0.8002 | 18600 | 3.098         | -                            | -                            | -                            | -                           | -                            |
| 0.8045 | 18700 | 3.183         | -                            | -                            | -                            | -                           | -                            |
| 0.8088 | 18800 | 3.1482        | -                            | -                            | -                            | -                           | -                            |
| 0.8131 | 18900 | 3.0122        | -                            | -                            | -                            | -                           | -                            |
| 0.8174 | 19000 | 3.0828        | -                            | -                            | -                            | -                           | -                            |
| 0.8217 | 19100 | 3.063         | -                            | -                            | -                            | -                           | -                            |
| 0.8260 | 19200 | 2.9688        | -                            | -                            | -                            | -                           | -                            |
| 0.8303 | 19300 | 3.0425        | -                            | -                            | -                            | -                           | -                            |
| 0.8346 | 19400 | 3.2018        | -                            | -                            | -                            | -                           | -                            |
| 0.8389 | 19500 | 2.9111        | -                            | -                            | -                            | -                           | -                            |
| 0.8432 | 19600 | 2.9516        | -                            | -                            | -                            | -                           | -                            |
| 0.8475 | 19700 | 2.9115        | -                            | -                            | -                            | -                           | -                            |
| 0.8518 | 19800 | 2.9323        | -                            | -                            | -                            | -                           | -                            |
| 0.8561 | 19900 | 2.8753        | -                            | -                            | -                            | -                           | -                            |
| 0.8604 | 20000 | 2.8344        | -                            | -                            | -                            | -                           | -                            |
| 0.8647 | 20100 | 2.7665        | -                            | -                            | -                            | -                           | -                            |
| 0.8690 | 20200 | 2.7732        | -                            | -                            | -                            | -                           | -                            |
| 0.8733 | 20300 | 2.8622        | -                            | -                            | -                            | -                           | -                            |
| 0.8776 | 20400 | 2.8749        | -                            | -                            | -                            | -                           | -                            |
| 0.8819 | 20500 | 2.8534        | -                            | -                            | -                            | -                           | -                            |
| 0.8863 | 20600 | 2.9254        | -                            | -                            | -                            | -                           | -                            |
| 0.8906 | 20700 | 2.7366        | -                            | -                            | -                            | -                           | -                            |
| 0.8949 | 20800 | 2.7287        | -                            | -                            | -                            | -                           | -                            |
| 0.8992 | 20900 | 2.9469        | -                            | -                            | -                            | -                           | -                            |
| 0.9035 | 21000 | 2.9052        | -                            | -                            | -                            | -                           | -                            |
| 0.9078 | 21100 | 2.7256        | -                            | -                            | -                            | -                           | -                            |
| 0.9121 | 21200 | 2.8469        | -                            | -                            | -                            | -                           | -                            |
| 0.9164 | 21300 | 2.6626        | -                            | -                            | -                            | -                           | -                            |
| 0.9207 | 21400 | 2.6796        | -                            | -                            | -                            | -                           | -                            |
| 0.9250 | 21500 | 2.6927        | -                            | -                            | -                            | -                           | -                            |
| 0.9293 | 21600 | 2.7125        | -                            | -                            | -                            | -                           | -                            |
| 0.9336 | 21700 | 2.6734        | -                            | -                            | -                            | -                           | -                            |
| 0.9379 | 21800 | 2.7199        | -                            | -                            | -                            | -                           | -                            |
| 0.9422 | 21900 | 2.6635        | -                            | -                            | -                            | -                           | -                            |
| 0.9465 | 22000 | 2.5218        | -                            | -                            | -                            | -                           | -                            |
| 0.9508 | 22100 | 2.7595        | -                            | -                            | -                            | -                           | -                            |
| 0.9551 | 22200 | 2.6821        | -                            | -                            | -                            | -                           | -                            |
| 0.9594 | 22300 | 2.6578        | -                            | -                            | -                            | -                           | -                            |
| 0.9637 | 22400 | 2.568         | -                            | -                            | -                            | -                           | -                            |
| 0.9680 | 22500 | 2.5527        | -                            | -                            | -                            | -                           | -                            |
| 0.9723 | 22600 | 2.6857        | -                            | -                            | -                            | -                           | -                            |
| 0.9766 | 22700 | 2.6637        | -                            | -                            | -                            | -                           | -                            |
| 0.9809 | 22800 | 2.6311        | -                            | -                            | -                            | -                           | -                            |
| 0.9852 | 22900 | 2.4635        | -                            | -                            | -                            | -                           | -                            |
| 0.9895 | 23000 | 2.6239        | -                            | -                            | -                            | -                           | -                            |
| 0.9938 | 23100 | 2.6873        | -                            | -                            | -                            | -                           | -                            |
| 0.9981 | 23200 | 2.5138        | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 23244 | -             | 0.6670                       | 0.6815                       | 0.6859                       | 0.6524                      | 0.6872                       |

</details>

### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.0.1
- Transformers: 4.40.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.29.3
- Datasets: 2.19.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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