---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: distilbert/distilroberta-base
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: He shrugged.
  sentences:
  - Then he shrugged.
  - Two people are dancing.
  - The people are Indian.
- source_sentence: a young girl
  sentences:
  - A girl is playing.
  - A dog playing outside.
  - The men are moving.
- source_sentence: girl sleeps
  sentences:
  - A little girl is sleep.
  - Two women are walking.
  - three men are pictured
- source_sentence: He walked.
  sentences:
  - A man is moving around.
  - A young man is running.
  - What idiots girls are!
- source_sentence: '''Go now.'''
  sentences:
  - Now go.
  - The door did not budge.
  - I never knew the man.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 768
      type: sts-dev-768
    metrics:
    - type: pearson_cosine
      value: 0.8418367310465795
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8485984004433933
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8356556933767024
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8341402433895243
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8378021883964464
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8364904078404392
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7476524989991268
      name: Pearson Dot
    - type: spearman_dot
      value: 0.744450587024694
      name: Spearman Dot
    - type: pearson_max
      value: 0.8418367310465795
      name: Pearson Max
    - type: spearman_max
      value: 0.8485984004433933
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8416891989714739
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8490082509626217
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8348187780435371
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8332638443518806
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.837008948364763
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8356608810942396
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7426437744526075
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7393063147821313
      name: Spearman Dot
    - type: pearson_max
      value: 0.8416891989714739
      name: Pearson Max
    - type: spearman_max
      value: 0.8490082509626217
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.8368212220308662
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8458532859579723
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8282949195581827
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8279757292284411
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8304309516656533
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8301347336633305
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7158283880571648
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7114038350641958
      name: Spearman Dot
    - type: pearson_max
      value: 0.8368212220308662
      name: Pearson Max
    - type: spearman_max
      value: 0.8458532859579723
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8291552182220155
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8410315378567165
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8205197124842151
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8211956528048456
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8218377581296912
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8223376697977559
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6736747525126793
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6704632728499174
      name: Spearman Dot
    - type: pearson_max
      value: 0.8291552182220155
      name: Pearson Max
    - type: spearman_max
      value: 0.8410315378567165
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.8201110050860942
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.835036509147006
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8028297556674707
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8048509047037822
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8046682420071583
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8063788129340022
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6171580093307325
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6176751811391049
      name: Spearman Dot
    - type: pearson_max
      value: 0.8201110050860942
      name: Pearson Max
    - type: spearman_max
      value: 0.835036509147006
      name: Spearman Max
---

# SentenceTransformer based on distilbert/distilroberta-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
<!-- - **License:** Unknown -->

### Model Sources

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

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    "'Go now.'",
    'Now go.',
    'The door did not budge.',
]
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-dev-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8418     |
| **spearman_cosine** | **0.8486** |
| pearson_manhattan   | 0.8357     |
| spearman_manhattan  | 0.8341     |
| pearson_euclidean   | 0.8378     |
| spearman_euclidean  | 0.8365     |
| pearson_dot         | 0.7477     |
| spearman_dot        | 0.7445     |
| pearson_max         | 0.8418     |
| spearman_max        | 0.8486     |

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

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.8417    |
| **spearman_cosine** | **0.849** |
| pearson_manhattan   | 0.8348    |
| spearman_manhattan  | 0.8333    |
| pearson_euclidean   | 0.837     |
| spearman_euclidean  | 0.8357    |
| pearson_dot         | 0.7426    |
| spearman_dot        | 0.7393    |
| pearson_max         | 0.8417    |
| spearman_max        | 0.849     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8368     |
| **spearman_cosine** | **0.8459** |
| pearson_manhattan   | 0.8283     |
| spearman_manhattan  | 0.828      |
| pearson_euclidean   | 0.8304     |
| spearman_euclidean  | 0.8301     |
| pearson_dot         | 0.7158     |
| spearman_dot        | 0.7114     |
| pearson_max         | 0.8368     |
| spearman_max        | 0.8459     |

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

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.8292    |
| **spearman_cosine** | **0.841** |
| pearson_manhattan   | 0.8205    |
| spearman_manhattan  | 0.8212    |
| pearson_euclidean   | 0.8218    |
| spearman_euclidean  | 0.8223    |
| pearson_dot         | 0.6737    |
| spearman_dot        | 0.6705    |
| pearson_max         | 0.8292    |
| spearman_max        | 0.841     |

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

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.8201    |
| **spearman_cosine** | **0.835** |
| pearson_manhattan   | 0.8028    |
| spearman_manhattan  | 0.8049    |
| pearson_euclidean   | 0.8047    |
| spearman_euclidean  | 0.8064    |
| pearson_dot         | 0.6172    |
| spearman_dot        | 0.6177    |
| pearson_max         | 0.8201    |
| spearman_max        | 0.835     |

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## Training Details

### Training Dataset

#### sentence-transformers/all-nli

* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 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: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 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>Children smiling and waving at camera</code>                         | <code>There are children present</code>          | <code>The kids are frowning</code>                         |
  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</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

#### sentence-transformers/all-nli

* Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 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: 18.02 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 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>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>        |
  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</code>            | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-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
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|
| 0.0459 | 100  | 19.459        | 8.2665 | 0.7796                      | 0.8046                      | 0.8114                      | 0.8082                     | 0.7996                      |
| 0.0917 | 200  | 11.0035       | 7.6606 | 0.7696                      | 0.7971                      | 0.8083                      | 0.7987                     | 0.7933                      |
| 0.1376 | 300  | 9.7634        | 6.4912 | 0.7992                      | 0.8126                      | 0.8190                      | 0.8062                     | 0.8127                      |
| 0.1835 | 400  | 9.1103        | 5.9960 | 0.8081                      | 0.8229                      | 0.8263                      | 0.8136                     | 0.8224                      |
| 0.2294 | 500  | 8.7099        | 5.9388 | 0.7984                      | 0.8138                      | 0.8189                      | 0.8021                     | 0.8166                      |
| 0.2752 | 600  | 8.1215        | 5.6457 | 0.7963                      | 0.8104                      | 0.8149                      | 0.8057                     | 0.8121                      |
| 0.3211 | 700  | 7.7441        | 5.4632 | 0.7937                      | 0.8153                      | 0.8199                      | 0.8119                     | 0.8150                      |
| 0.3670 | 800  | 7.4849        | 5.1815 | 0.8076                      | 0.8208                      | 0.8238                      | 0.8152                     | 0.8172                      |
| 0.4128 | 900  | 7.1386        | 5.1419 | 0.8035                      | 0.8181                      | 0.8235                      | 0.8139                     | 0.8189                      |
| 0.4587 | 1000 | 6.839         | 5.1548 | 0.7943                      | 0.8118                      | 0.8172                      | 0.8054                     | 0.8153                      |
| 0.5046 | 1100 | 6.6597        | 5.1015 | 0.7895                      | 0.8066                      | 0.8119                      | 0.8059                     | 0.8063                      |
| 0.5505 | 1200 | 6.7172        | 5.3707 | 0.7753                      | 0.7987                      | 0.8068                      | 0.7989                     | 0.8014                      |
| 0.5963 | 1300 | 6.6514        | 4.9368 | 0.7904                      | 0.8086                      | 0.8139                      | 0.8051                     | 0.8083                      |
| 0.6422 | 1400 | 6.5573        | 5.0196 | 0.7882                      | 0.8066                      | 0.8128                      | 0.8035                     | 0.8091                      |
| 0.6881 | 1500 | 6.7596        | 4.9381 | 0.7960                      | 0.8120                      | 0.8169                      | 0.8058                     | 0.8140                      |
| 0.7339 | 1600 | 6.2686        | 4.4018 | 0.8136                      | 0.8245                      | 0.8268                      | 0.8160                     | 0.8244                      |
| 0.7798 | 1700 | 3.4607        | 3.8397 | 0.8415                      | 0.8466                      | 0.8502                      | 0.8345                     | 0.8503                      |
| 0.8257 | 1800 | 2.6912        | 3.7914 | 0.8415                      | 0.8459                      | 0.8493                      | 0.8350                     | 0.8488                      |
| 0.8716 | 1900 | 2.4958        | 3.7752 | 0.8402                      | 0.8450                      | 0.8484                      | 0.8340                     | 0.8478                      |
| 0.9174 | 2000 | 2.3413        | 3.7997 | 0.8410                      | 0.8459                      | 0.8490                      | 0.8350                     | 0.8486                      |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- Transformers: 4.41.1
- PyTorch: 2.3.0+cu121
- Accelerate: 0.30.1
- Datasets: 2.19.2
- 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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