sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:1115700 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Geotrend/bert-base-sw-cased base_model:finetune:Geotrend/bert-base-sw-cased model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Geotrend/bert-base-sw-cased
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 Geotrend/bert-base-sw-cased
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.6937245827269046
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6872564222432196
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6671541268726737
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6578428252987948
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6672292642346008
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6577692881532263
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5234944445417878
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5126395384896926
      name: Spearman Dot
    - type: pearson_max
      value: 0.6937245827269046
      name: Pearson Max
    - type: spearman_max
      value: 0.6872564222432196
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.689885399601221
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6847071916895495
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6678379220949281
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6579957115799916
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6673062843667007
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6573006123381013
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.49533316366864977
      name: Pearson Dot
    - type: spearman_dot
      value: 0.48723679408818543
      name: Spearman Dot
    - type: pearson_max
      value: 0.689885399601221
      name: Pearson Max
    - type: spearman_max
      value: 0.6847071916895495
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.6873377612773459
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6816874105466478
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.667357515297651
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6557727891191705
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6674937201647584
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6560441259953166
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.45660372834373963
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4533070407260065
      name: Spearman Dot
    - type: pearson_max
      value: 0.6873377612773459
      name: Pearson Max
    - type: spearman_max
      value: 0.6816874105466478
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.6836009506667413
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6795423695973911
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6663652896396122
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6534731725514219
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6663726876345561
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6537216014002204
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.43102957451470686
      name: Pearson Dot
    - type: spearman_dot
      value: 0.431538008932168
      name: Spearman Dot
    - type: pearson_max
      value: 0.6836009506667413
      name: Pearson Max
    - type: spearman_max
      value: 0.6795423695973911
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.6715253560367674
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.669070001537953
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6571390159051358
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6456119247619697
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6598587843081631
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6472279949159918
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.36757468941627225
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3678274698380672
      name: Spearman Dot
    - type: pearson_max
      value: 0.6715253560367674
      name: Pearson Max
    - type: spearman_max
      value: 0.669070001537953
      name: Spearman Max
---

# SentenceTransformer based on Geotrend/bert-base-sw-cased

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased) 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:** [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased) <!-- at revision 7d9ca957a81d2449cf1319af0b91f75f11642336 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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-bert-base-sw-cased-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><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      | 0.6937     |
| **spearman_cosine** | **0.6873** |
| pearson_manhattan   | 0.6672     |
| spearman_manhattan  | 0.6578     |
| pearson_euclidean   | 0.6672     |
| spearman_euclidean  | 0.6578     |
| pearson_dot         | 0.5235     |
| spearman_dot        | 0.5126     |
| pearson_max         | 0.6937     |
| spearman_max        | 0.6873     |

#### 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      | 0.6899     |
| **spearman_cosine** | **0.6847** |
| pearson_manhattan   | 0.6678     |
| spearman_manhattan  | 0.658      |
| pearson_euclidean   | 0.6673     |
| spearman_euclidean  | 0.6573     |
| pearson_dot         | 0.4953     |
| spearman_dot        | 0.4872     |
| pearson_max         | 0.6899     |
| spearman_max        | 0.6847     |

#### 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      | 0.6873     |
| **spearman_cosine** | **0.6817** |
| pearson_manhattan   | 0.6674     |
| spearman_manhattan  | 0.6558     |
| pearson_euclidean   | 0.6675     |
| spearman_euclidean  | 0.656      |
| pearson_dot         | 0.4566     |
| spearman_dot        | 0.4533     |
| pearson_max         | 0.6873     |
| spearman_max        | 0.6817     |

#### 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      | 0.6836     |
| **spearman_cosine** | **0.6795** |
| pearson_manhattan   | 0.6664     |
| spearman_manhattan  | 0.6535     |
| pearson_euclidean   | 0.6664     |
| spearman_euclidean  | 0.6537     |
| pearson_dot         | 0.431      |
| spearman_dot        | 0.4315     |
| pearson_max         | 0.6836     |
| spearman_max        | 0.6795     |

#### 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      | 0.6715     |
| **spearman_cosine** | **0.6691** |
| pearson_manhattan   | 0.6571     |
| spearman_manhattan  | 0.6456     |
| pearson_euclidean   | 0.6599     |
| spearman_euclidean  | 0.6472     |
| pearson_dot         | 0.3676     |
| spearman_dot        | 0.3678     |
| pearson_max         | 0.6715     |
| spearman_max        | 0.6691     |

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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: 9 tokens</li><li>mean: 16.73 tokens</li><li>max: 71 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.74 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.0 tokens</li><li>max: 49 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: 7 tokens</li><li>mean: 28.25 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.16 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.55 tokens</li><li>max: 46 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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch  | Step  | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0057 | 100   | 19.9104       | -                            | -                            | -                            | -                           | -                            |
| 0.0115 | 200   | 15.4038       | -                            | -                            | -                            | -                           | -                            |
| 0.0172 | 300   | 12.4565       | -                            | -                            | -                            | -                           | -                            |
| 0.0229 | 400   | 11.8633       | -                            | -                            | -                            | -                           | -                            |
| 0.0287 | 500   | 11.0601       | -                            | -                            | -                            | -                           | -                            |
| 0.0344 | 600   | 9.7725        | -                            | -                            | -                            | -                           | -                            |
| 0.0402 | 700   | 8.8549        | -                            | -                            | -                            | -                           | -                            |
| 0.0459 | 800   | 8.0831        | -                            | -                            | -                            | -                           | -                            |
| 0.0516 | 900   | 7.9941        | -                            | -                            | -                            | -                           | -                            |
| 0.0574 | 1000  | 7.6537        | -                            | -                            | -                            | -                           | -                            |
| 0.0631 | 1100  | 7.9303        | -                            | -                            | -                            | -                           | -                            |
| 0.0688 | 1200  | 7.5246        | -                            | -                            | -                            | -                           | -                            |
| 0.0746 | 1300  | 7.7754        | -                            | -                            | -                            | -                           | -                            |
| 0.0803 | 1400  | 7.668         | -                            | -                            | -                            | -                           | -                            |
| 0.0860 | 1500  | 6.7171        | -                            | -                            | -                            | -                           | -                            |
| 0.0918 | 1600  | 6.347         | -                            | -                            | -                            | -                           | -                            |
| 0.0975 | 1700  | 6.0           | -                            | -                            | -                            | -                           | -                            |
| 0.1033 | 1800  | 6.4314        | -                            | -                            | -                            | -                           | -                            |
| 0.1090 | 1900  | 6.7947        | -                            | -                            | -                            | -                           | -                            |
| 0.1147 | 2000  | 6.9316        | -                            | -                            | -                            | -                           | -                            |
| 0.1205 | 2100  | 6.6304        | -                            | -                            | -                            | -                           | -                            |
| 0.1262 | 2200  | 6.132         | -                            | -                            | -                            | -                           | -                            |
| 0.1319 | 2300  | 5.8953        | -                            | -                            | -                            | -                           | -                            |
| 0.1377 | 2400  | 5.6954        | -                            | -                            | -                            | -                           | -                            |
| 0.1434 | 2500  | 5.6832        | -                            | -                            | -                            | -                           | -                            |
| 0.1491 | 2600  | 5.2266        | -                            | -                            | -                            | -                           | -                            |
| 0.1549 | 2700  | 5.0678        | -                            | -                            | -                            | -                           | -                            |
| 0.1606 | 2800  | 5.4733        | -                            | -                            | -                            | -                           | -                            |
| 0.1664 | 2900  | 6.0899        | -                            | -                            | -                            | -                           | -                            |
| 0.1721 | 3000  | 6.332         | -                            | -                            | -                            | -                           | -                            |
| 0.1778 | 3100  | 6.4937        | -                            | -                            | -                            | -                           | -                            |
| 0.1836 | 3200  | 6.2242        | -                            | -                            | -                            | -                           | -                            |
| 0.1893 | 3300  | 5.8023        | -                            | -                            | -                            | -                           | -                            |
| 0.1950 | 3400  | 5.0745        | -                            | -                            | -                            | -                           | -                            |
| 0.2008 | 3500  | 5.5806        | -                            | -                            | -                            | -                           | -                            |
| 0.2065 | 3600  | 5.5191        | -                            | -                            | -                            | -                           | -                            |
| 0.2122 | 3700  | 5.3849        | -                            | -                            | -                            | -                           | -                            |
| 0.2180 | 3800  | 5.4828        | -                            | -                            | -                            | -                           | -                            |
| 0.2237 | 3900  | 5.9982        | -                            | -                            | -                            | -                           | -                            |
| 0.2294 | 4000  | 5.6842        | -                            | -                            | -                            | -                           | -                            |
| 0.2352 | 4100  | 5.1627        | -                            | -                            | -                            | -                           | -                            |
| 0.2409 | 4200  | 5.154         | -                            | -                            | -                            | -                           | -                            |
| 0.2467 | 4300  | 5.7932        | -                            | -                            | -                            | -                           | -                            |
| 0.2524 | 4400  | 5.5758        | -                            | -                            | -                            | -                           | -                            |
| 0.2581 | 4500  | 5.5212        | -                            | -                            | -                            | -                           | -                            |
| 0.2639 | 4600  | 5.5692        | -                            | -                            | -                            | -                           | -                            |
| 0.2696 | 4700  | 5.2699        | -                            | -                            | -                            | -                           | -                            |
| 0.2753 | 4800  | 5.4919        | -                            | -                            | -                            | -                           | -                            |
| 0.2811 | 4900  | 5.0754        | -                            | -                            | -                            | -                           | -                            |
| 0.2868 | 5000  | 5.1514        | -                            | -                            | -                            | -                           | -                            |
| 0.2925 | 5100  | 5.0241        | -                            | -                            | -                            | -                           | -                            |
| 0.2983 | 5200  | 5.2679        | -                            | -                            | -                            | -                           | -                            |
| 0.3040 | 5300  | 5.3576        | -                            | -                            | -                            | -                           | -                            |
| 0.3098 | 5400  | 5.3454        | -                            | -                            | -                            | -                           | -                            |
| 0.3155 | 5500  | 5.2142        | -                            | -                            | -                            | -                           | -                            |
| 0.3212 | 5600  | 4.8418        | -                            | -                            | -                            | -                           | -                            |
| 0.3270 | 5700  | 4.9597        | -                            | -                            | -                            | -                           | -                            |
| 0.3327 | 5800  | 5.1989        | -                            | -                            | -                            | -                           | -                            |
| 0.3384 | 5900  | 5.2624        | -                            | -                            | -                            | -                           | -                            |
| 0.3442 | 6000  | 5.0705        | -                            | -                            | -                            | -                           | -                            |
| 0.3499 | 6100  | 5.232         | -                            | -                            | -                            | -                           | -                            |
| 0.3556 | 6200  | 5.2428        | -                            | -                            | -                            | -                           | -                            |
| 0.3614 | 6300  | 4.755         | -                            | -                            | -                            | -                           | -                            |
| 0.3671 | 6400  | 4.7266        | -                            | -                            | -                            | -                           | -                            |
| 0.3729 | 6500  | 4.6452        | -                            | -                            | -                            | -                           | -                            |
| 0.3786 | 6600  | 5.1431        | -                            | -                            | -                            | -                           | -                            |
| 0.3843 | 6700  | 4.5343        | -                            | -                            | -                            | -                           | -                            |
| 0.3901 | 6800  | 4.698         | -                            | -                            | -                            | -                           | -                            |
| 0.3958 | 6900  | 4.6944        | -                            | -                            | -                            | -                           | -                            |
| 0.4015 | 7000  | 4.6255        | -                            | -                            | -                            | -                           | -                            |
| 0.4073 | 7100  | 5.0211        | -                            | -                            | -                            | -                           | -                            |
| 0.4130 | 7200  | 4.6974        | -                            | -                            | -                            | -                           | -                            |
| 0.4187 | 7300  | 4.9182        | -                            | -                            | -                            | -                           | -                            |
| 0.4245 | 7400  | 4.652         | -                            | -                            | -                            | -                           | -                            |
| 0.4302 | 7500  | 5.1015        | -                            | -                            | -                            | -                           | -                            |
| 0.4360 | 7600  | 4.5249        | -                            | -                            | -                            | -                           | -                            |
| 0.4417 | 7700  | 4.455         | -                            | -                            | -                            | -                           | -                            |
| 0.4474 | 7800  | 4.8153        | -                            | -                            | -                            | -                           | -                            |
| 0.4532 | 7900  | 4.7665        | -                            | -                            | -                            | -                           | -                            |
| 0.4589 | 8000  | 4.3413        | -                            | -                            | -                            | -                           | -                            |
| 0.4646 | 8100  | 4.4697        | -                            | -                            | -                            | -                           | -                            |
| 0.4704 | 8200  | 4.6776        | -                            | -                            | -                            | -                           | -                            |
| 0.4761 | 8300  | 4.2868        | -                            | -                            | -                            | -                           | -                            |
| 0.4818 | 8400  | 4.7052        | -                            | -                            | -                            | -                           | -                            |
| 0.4876 | 8500  | 4.4721        | -                            | -                            | -                            | -                           | -                            |
| 0.4933 | 8600  | 4.6926        | -                            | -                            | -                            | -                           | -                            |
| 0.4991 | 8700  | 4.9891        | -                            | -                            | -                            | -                           | -                            |
| 0.5048 | 8800  | 4.4837        | -                            | -                            | -                            | -                           | -                            |
| 0.5105 | 8900  | 4.8127        | -                            | -                            | -                            | -                           | -                            |
| 0.5163 | 9000  | 4.3438        | -                            | -                            | -                            | -                           | -                            |
| 0.5220 | 9100  | 4.4743        | -                            | -                            | -                            | -                           | -                            |
| 0.5277 | 9200  | 4.6879        | -                            | -                            | -                            | -                           | -                            |
| 0.5335 | 9300  | 4.3593        | -                            | -                            | -                            | -                           | -                            |
| 0.5392 | 9400  | 4.3023        | -                            | -                            | -                            | -                           | -                            |
| 0.5449 | 9500  | 4.8188        | -                            | -                            | -                            | -                           | -                            |
| 0.5507 | 9600  | 4.6142        | -                            | -                            | -                            | -                           | -                            |
| 0.5564 | 9700  | 4.7679        | -                            | -                            | -                            | -                           | -                            |
| 0.5622 | 9800  | 4.6224        | -                            | -                            | -                            | -                           | -                            |
| 0.5679 | 9900  | 4.9154        | -                            | -                            | -                            | -                           | -                            |
| 0.5736 | 10000 | 4.7557        | -                            | -                            | -                            | -                           | -                            |
| 0.5794 | 10100 | 4.6395        | -                            | -                            | -                            | -                           | -                            |
| 0.5851 | 10200 | 4.7977        | -                            | -                            | -                            | -                           | -                            |
| 0.5908 | 10300 | 4.915         | -                            | -                            | -                            | -                           | -                            |
| 0.5966 | 10400 | 4.4854        | -                            | -                            | -                            | -                           | -                            |
| 0.6023 | 10500 | 4.3973        | -                            | -                            | -                            | -                           | -                            |
| 0.6080 | 10600 | 4.6964        | -                            | -                            | -                            | -                           | -                            |
| 0.6138 | 10700 | 4.8853        | -                            | -                            | -                            | -                           | -                            |
| 0.6195 | 10800 | 4.786         | -                            | -                            | -                            | -                           | -                            |
| 0.6253 | 10900 | 4.5482        | -                            | -                            | -                            | -                           | -                            |
| 0.6310 | 11000 | 4.4857        | -                            | -                            | -                            | -                           | -                            |
| 0.6367 | 11100 | 4.7415        | -                            | -                            | -                            | -                           | -                            |
| 0.6425 | 11200 | 4.2596        | -                            | -                            | -                            | -                           | -                            |
| 0.6482 | 11300 | 4.8578        | -                            | -                            | -                            | -                           | -                            |
| 0.6539 | 11400 | 4.5471        | -                            | -                            | -                            | -                           | -                            |
| 0.6597 | 11500 | 4.8337        | -                            | -                            | -                            | -                           | -                            |
| 0.6654 | 11600 | 4.2244        | -                            | -                            | -                            | -                           | -                            |
| 0.6711 | 11700 | 4.9619        | -                            | -                            | -                            | -                           | -                            |
| 0.6769 | 11800 | 4.9369        | -                            | -                            | -                            | -                           | -                            |
| 0.6826 | 11900 | 4.2697        | -                            | -                            | -                            | -                           | -                            |
| 0.6883 | 12000 | 4.2711        | -                            | -                            | -                            | -                           | -                            |
| 0.6941 | 12100 | 4.6396        | -                            | -                            | -                            | -                           | -                            |
| 0.6998 | 12200 | 4.5626        | -                            | -                            | -                            | -                           | -                            |
| 0.7056 | 12300 | 4.5767        | -                            | -                            | -                            | -                           | -                            |
| 0.7113 | 12400 | 4.6449        | -                            | -                            | -                            | -                           | -                            |
| 0.7170 | 12500 | 4.4217        | -                            | -                            | -                            | -                           | -                            |
| 0.7228 | 12600 | 4.0203        | -                            | -                            | -                            | -                           | -                            |
| 0.7285 | 12700 | 4.5381        | -                            | -                            | -                            | -                           | -                            |
| 0.7342 | 12800 | 4.5865        | -                            | -                            | -                            | -                           | -                            |
| 0.7400 | 12900 | 4.4203        | -                            | -                            | -                            | -                           | -                            |
| 0.7457 | 13000 | 4.3761        | -                            | -                            | -                            | -                           | -                            |
| 0.7514 | 13100 | 4.093         | -                            | -                            | -                            | -                           | -                            |
| 0.7572 | 13200 | 5.9235        | -                            | -                            | -                            | -                           | -                            |
| 0.7629 | 13300 | 5.4098        | -                            | -                            | -                            | -                           | -                            |
| 0.7687 | 13400 | 5.3079        | -                            | -                            | -                            | -                           | -                            |
| 0.7744 | 13500 | 5.0946        | -                            | -                            | -                            | -                           | -                            |
| 0.7801 | 13600 | 4.7098        | -                            | -                            | -                            | -                           | -                            |
| 0.7859 | 13700 | 4.9471        | -                            | -                            | -                            | -                           | -                            |
| 0.7916 | 13800 | 4.5742        | -                            | -                            | -                            | -                           | -                            |
| 0.7973 | 13900 | 4.6178        | -                            | -                            | -                            | -                           | -                            |
| 0.8031 | 14000 | 4.4516        | -                            | -                            | -                            | -                           | -                            |
| 0.8088 | 14100 | 4.429         | -                            | -                            | -                            | -                           | -                            |
| 0.8145 | 14200 | 4.3812        | -                            | -                            | -                            | -                           | -                            |
| 0.8203 | 14300 | 4.3739        | -                            | -                            | -                            | -                           | -                            |
| 0.8260 | 14400 | 4.3821        | -                            | -                            | -                            | -                           | -                            |
| 0.8318 | 14500 | 4.4396        | -                            | -                            | -                            | -                           | -                            |
| 0.8375 | 14600 | 4.2667        | -                            | -                            | -                            | -                           | -                            |
| 0.8432 | 14700 | 4.1963        | -                            | -                            | -                            | -                           | -                            |
| 0.8490 | 14800 | 4.1298        | -                            | -                            | -                            | -                           | -                            |
| 0.8547 | 14900 | 4.1843        | -                            | -                            | -                            | -                           | -                            |
| 0.8604 | 15000 | 4.0735        | -                            | -                            | -                            | -                           | -                            |
| 0.8662 | 15100 | 3.9319        | -                            | -                            | -                            | -                           | -                            |
| 0.8719 | 15200 | 4.1544        | -                            | -                            | -                            | -                           | -                            |
| 0.8776 | 15300 | 4.105         | -                            | -                            | -                            | -                           | -                            |
| 0.8834 | 15400 | 4.014         | -                            | -                            | -                            | -                           | -                            |
| 0.8891 | 15500 | 4.0345        | -                            | -                            | -                            | -                           | -                            |
| 0.8949 | 15600 | 3.9127        | -                            | -                            | -                            | -                           | -                            |
| 0.9006 | 15700 | 4.1002        | -                            | -                            | -                            | -                           | -                            |
| 0.9063 | 15800 | 3.8564        | -                            | -                            | -                            | -                           | -                            |
| 0.9121 | 15900 | 3.9297        | -                            | -                            | -                            | -                           | -                            |
| 0.9178 | 16000 | 3.8487        | -                            | -                            | -                            | -                           | -                            |
| 0.9235 | 16100 | 3.7099        | -                            | -                            | -                            | -                           | -                            |
| 0.9293 | 16200 | 3.8545        | -                            | -                            | -                            | -                           | -                            |
| 0.9350 | 16300 | 3.8122        | -                            | -                            | -                            | -                           | -                            |
| 0.9407 | 16400 | 3.8951        | -                            | -                            | -                            | -                           | -                            |
| 0.9465 | 16500 | 3.6996        | -                            | -                            | -                            | -                           | -                            |
| 0.9522 | 16600 | 3.9081        | -                            | -                            | -                            | -                           | -                            |
| 0.9580 | 16700 | 3.8603        | -                            | -                            | -                            | -                           | -                            |
| 0.9637 | 16800 | 3.8534        | -                            | -                            | -                            | -                           | -                            |
| 0.9694 | 16900 | 3.8145        | -                            | -                            | -                            | -                           | -                            |
| 0.9752 | 17000 | 3.9858        | -                            | -                            | -                            | -                           | -                            |
| 0.9809 | 17100 | 3.8224        | -                            | -                            | -                            | -                           | -                            |
| 0.9866 | 17200 | 3.7469        | -                            | -                            | -                            | -                           | -                            |
| 0.9924 | 17300 | 3.9066        | -                            | -                            | -                            | -                           | -                            |
| 0.9981 | 17400 | 3.6754        | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 17433 | -             | 0.6795                       | 0.6817                       | 0.6847                       | 0.6691                      | 0.6873                       |

</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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