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:BAAI/bge-small-en-v1.5 base_model:finetune:BAAI/bge-small-en-v1.5 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-small-en-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 BAAI/bge-small-en-v1.5
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.6831671531193453
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.677143022633225
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6891948944875336
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6892226446007472
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6916897298195501
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6916850273924392
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6418376172951465
      name: Pearson Dot
    - type: spearman_dot
      value: 0.628581703082033
      name: Spearman Dot
    - type: pearson_max
      value: 0.6916897298195501
      name: Pearson Max
    - type: spearman_max
      value: 0.6916850273924392
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.6753009254241098
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6731049071307844
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6906782473185179
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6927883369656496
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6933649652149252
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.694111832507592
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.600449101550258
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5857671058687308
      name: Spearman Dot
    - type: pearson_max
      value: 0.6933649652149252
      name: Pearson Max
    - type: spearman_max
      value: 0.694111832507592
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.6546200020168988
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6523958945855459
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6837289470688535
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6796775815725002
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6861328219241016
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6815842202083926
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5120576666695955
      name: Pearson Dot
    - type: spearman_dot
      value: 0.49141347385563683
      name: Spearman Dot
    - type: pearson_max
      value: 0.6861328219241016
      name: Pearson Max
    - type: spearman_max
      value: 0.6815842202083926
      name: Spearman Max
---

# SentenceTransformer based on BAAI/bge-small-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the Mollel/swahili-n_li-triplet-swh-eng dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("Mollel/MultiLinguSwahili-bge-small-en-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, 384]

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

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

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</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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

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## Evaluation

### Metrics

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6832     |
| **spearman_cosine** | **0.6771** |
| pearson_manhattan   | 0.6892     |
| spearman_manhattan  | 0.6892     |
| pearson_euclidean   | 0.6917     |
| spearman_euclidean  | 0.6917     |
| pearson_dot         | 0.6418     |
| spearman_dot        | 0.6286     |
| pearson_max         | 0.6917     |
| spearman_max        | 0.6917     |

#### 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      | 0.6753     |
| **spearman_cosine** | **0.6731** |
| pearson_manhattan   | 0.6907     |
| spearman_manhattan  | 0.6928     |
| pearson_euclidean   | 0.6934     |
| spearman_euclidean  | 0.6941     |
| pearson_dot         | 0.6004     |
| spearman_dot        | 0.5858     |
| pearson_max         | 0.6934     |
| spearman_max        | 0.6941     |

#### 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      | 0.6546     |
| **spearman_cosine** | **0.6524** |
| pearson_manhattan   | 0.6837     |
| spearman_manhattan  | 0.6797     |
| pearson_euclidean   | 0.6861     |
| spearman_euclidean  | 0.6816     |
| pearson_dot         | 0.5121     |
| spearman_dot        | 0.4914     |
| pearson_max         | 0.6861     |
| spearman_max        | 0.6816     |

<!--
## Bias, Risks and Limitations

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

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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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": [
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          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": [
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `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`: 64
- `per_device_eval_batch_size`: 64
- `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
| Epoch  | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-64_spearman_cosine |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:---------------------------:|
| 0.0115 | 100  | 9.6847        | -                            | -                            | -                           |
| 0.0229 | 200  | 8.5336        | -                            | -                            | -                           |
| 0.0344 | 300  | 7.768         | -                            | -                            | -                           |
| 0.0459 | 400  | 7.2049        | -                            | -                            | -                           |
| 0.0574 | 500  | 6.9425        | -                            | -                            | -                           |
| 0.0688 | 600  | 7.029         | -                            | -                            | -                           |
| 0.0803 | 700  | 6.259         | -                            | -                            | -                           |
| 0.0918 | 800  | 6.0939        | -                            | -                            | -                           |
| 0.1032 | 900  | 5.991         | -                            | -                            | -                           |
| 0.1147 | 1000 | 5.39          | -                            | -                            | -                           |
| 0.1262 | 1100 | 5.3214        | -                            | -                            | -                           |
| 0.1377 | 1200 | 5.1469        | -                            | -                            | -                           |
| 0.1491 | 1300 | 4.901         | -                            | -                            | -                           |
| 0.1606 | 1400 | 5.2725        | -                            | -                            | -                           |
| 0.1721 | 1500 | 5.077         | -                            | -                            | -                           |
| 0.1835 | 1600 | 4.8006        | -                            | -                            | -                           |
| 0.1950 | 1700 | 4.5318        | -                            | -                            | -                           |
| 0.2065 | 1800 | 4.48          | -                            | -                            | -                           |
| 0.2180 | 1900 | 4.5752        | -                            | -                            | -                           |
| 0.2294 | 2000 | 4.427         | -                            | -                            | -                           |
| 0.2409 | 2100 | 4.4021        | -                            | -                            | -                           |
| 0.2524 | 2200 | 4.5903        | -                            | -                            | -                           |
| 0.2639 | 2300 | 4.4561        | -                            | -                            | -                           |
| 0.2753 | 2400 | 4.372         | -                            | -                            | -                           |
| 0.2868 | 2500 | 4.2698        | -                            | -                            | -                           |
| 0.2983 | 2600 | 4.3954        | -                            | -                            | -                           |
| 0.3097 | 2700 | 4.2697        | -                            | -                            | -                           |
| 0.3212 | 2800 | 4.125         | -                            | -                            | -                           |
| 0.3327 | 2900 | 4.3611        | -                            | -                            | -                           |
| 0.3442 | 3000 | 4.2527        | -                            | -                            | -                           |
| 0.3556 | 3100 | 4.1892        | -                            | -                            | -                           |
| 0.3671 | 3200 | 4.0417        | -                            | -                            | -                           |
| 0.3786 | 3300 | 3.9434        | -                            | -                            | -                           |
| 0.3900 | 3400 | 3.9797        | -                            | -                            | -                           |
| 0.4015 | 3500 | 3.9611        | -                            | -                            | -                           |
| 0.4130 | 3600 | 4.04          | -                            | -                            | -                           |
| 0.4245 | 3700 | 3.965         | -                            | -                            | -                           |
| 0.4359 | 3800 | 3.778         | -                            | -                            | -                           |
| 0.4474 | 3900 | 4.0624        | -                            | -                            | -                           |
| 0.4589 | 4000 | 3.8972        | -                            | -                            | -                           |
| 0.4703 | 4100 | 3.7882        | -                            | -                            | -                           |
| 0.4818 | 4200 | 3.8048        | -                            | -                            | -                           |
| 0.4933 | 4300 | 3.9253        | -                            | -                            | -                           |
| 0.5048 | 4400 | 3.9832        | -                            | -                            | -                           |
| 0.5162 | 4500 | 3.6644        | -                            | -                            | -                           |
| 0.5277 | 4600 | 3.7353        | -                            | -                            | -                           |
| 0.5392 | 4700 | 3.7768        | -                            | -                            | -                           |
| 0.5506 | 4800 | 3.796         | -                            | -                            | -                           |
| 0.5621 | 4900 | 3.875         | -                            | -                            | -                           |
| 0.5736 | 5000 | 3.7856        | -                            | -                            | -                           |
| 0.5851 | 5100 | 3.8898        | -                            | -                            | -                           |
| 0.5965 | 5200 | 3.6327        | -                            | -                            | -                           |
| 0.6080 | 5300 | 3.7727        | -                            | -                            | -                           |
| 0.6195 | 5400 | 3.8582        | -                            | -                            | -                           |
| 0.6310 | 5500 | 3.729         | -                            | -                            | -                           |
| 0.6424 | 5600 | 3.7088        | -                            | -                            | -                           |
| 0.6539 | 5700 | 3.8414        | -                            | -                            | -                           |
| 0.6654 | 5800 | 3.7624        | -                            | -                            | -                           |
| 0.6768 | 5900 | 3.8816        | -                            | -                            | -                           |
| 0.6883 | 6000 | 3.7483        | -                            | -                            | -                           |
| 0.6998 | 6100 | 3.7759        | -                            | -                            | -                           |
| 0.7113 | 6200 | 3.6674        | -                            | -                            | -                           |
| 0.7227 | 6300 | 3.6441        | -                            | -                            | -                           |
| 0.7342 | 6400 | 3.7779        | -                            | -                            | -                           |
| 0.7457 | 6500 | 3.6691        | -                            | -                            | -                           |
| 0.7571 | 6600 | 3.7636        | -                            | -                            | -                           |
| 0.7686 | 6700 | 3.7424        | -                            | -                            | -                           |
| 0.7801 | 6800 | 3.4943        | -                            | -                            | -                           |
| 0.7916 | 6900 | 3.5399        | -                            | -                            | -                           |
| 0.8030 | 7000 | 3.3658        | -                            | -                            | -                           |
| 0.8145 | 7100 | 3.2856        | -                            | -                            | -                           |
| 0.8260 | 7200 | 3.3702        | -                            | -                            | -                           |
| 0.8374 | 7300 | 3.3121        | -                            | -                            | -                           |
| 0.8489 | 7400 | 3.2322        | -                            | -                            | -                           |
| 0.8604 | 7500 | 3.1577        | -                            | -                            | -                           |
| 0.8719 | 7600 | 3.1873        | -                            | -                            | -                           |
| 0.8833 | 7700 | 3.1492        | -                            | -                            | -                           |
| 0.8948 | 7800 | 3.2035        | -                            | -                            | -                           |
| 0.9063 | 7900 | 3.1607        | -                            | -                            | -                           |
| 0.9177 | 8000 | 3.1557        | -                            | -                            | -                           |
| 0.9292 | 8100 | 3.0915        | -                            | -                            | -                           |
| 0.9407 | 8200 | 3.1335        | -                            | -                            | -                           |
| 0.9522 | 8300 | 3.14          | -                            | -                            | -                           |
| 0.9636 | 8400 | 3.1422        | -                            | -                            | -                           |
| 0.9751 | 8500 | 3.1923        | -                            | -                            | -                           |
| 0.9866 | 8600 | 3.1085        | -                            | -                            | -                           |
| 0.9980 | 8700 | 3.089         | -                            | -                            | -                           |
| 1.0    | 8717 | -             | 0.6731                       | 0.6771                       | 0.6524                      |


### 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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