sentence-transformers safetensors roberta sentence-similarity feature-extraction loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:distilbert/distilroberta-base base_model:finetune:distilbert/distilroberta-base model-index co2_eq_emissions autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: distilbert/distilroberta-base
language:
- en
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
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: A baby is laughing.
  sentences:
  - The baby laughed in his car seat.
  - A toddler walks down a hallway.
  - Japan falls silent to mark 311 tragedy
- source_sentence: A woman is reading.
  sentences:
  - A woman is writing something.
  - The man is in a deserted field.
  - Obama urges no new sanctions on Iran
- source_sentence: A man is spitting.
  sentences:
  - A man is crying.
  - A girl plays a wind instrument.
  - Kids playing ball in the park.
- source_sentence: A man shoots a man.
  sentences:
  - A man is shooting off guns.
  - A slow loris hanging on a cord.
  - Finance minister promises no new taxes
- source_sentence: A boy is vacuuming.
  sentences:
  - A little boy is vacuuming the floor.
  - A woman is applying eye shadow.
  - Glorious triple-gold night for Britain
co2_eq_emissions:
  emissions: 94.71657156591533
  energy_consumed: 0.2436740010751561
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.923
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SentenceTransformer based on distilbert/distilroberta-base
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.832978199459682
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8449812730792539
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8284059469034439
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8314151253676515
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8291459460248565
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8319080532683886
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7274279213358037
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7358272455513368
      name: Spearman Dot
    - type: pearson_max
      value: 0.832978199459682
      name: Pearson Max
    - type: spearman_max
      value: 0.8449812730792539
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8266436609310417
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.841563547795295
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8250171666597236
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8276544602820737
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8255984422889996
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.828520082690129
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7120095981036954
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7163267085950832
      name: Spearman Dot
    - type: pearson_max
      value: 0.8266436609310417
      name: Pearson Max
    - type: spearman_max
      value: 0.841563547795295
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.817074395539638
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8355573303767316
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8175610864074738
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8212543828500742
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8175058817585
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8216438541895171
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6852246329807953
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6861394760239012
      name: Spearman Dot
    - type: pearson_max
      value: 0.8175610864074738
      name: Pearson Max
    - type: spearman_max
      value: 0.8355573303767316
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 32
      type: sts-dev-32
    metrics:
    - type: pearson_cosine
      value: 0.7963856490231295
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8243820415687734
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7982768947167747
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.804919985023919
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.800259304954162
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8069660671225415
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.6311831976256888
      name: Pearson Dot
    - type: spearman_dot
      value: 0.6277202377535699
      name: Spearman Dot
    - type: pearson_max
      value: 0.800259304954162
      name: Pearson Max
    - type: spearman_max
      value: 0.8243820415687734
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 16
      type: sts-dev-16
    metrics:
    - type: pearson_cosine
      value: 0.7401161630034654
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7871969780219474
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7609788932639057
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7761115272699121
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7645256699036285
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7794348361665424
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5201701018366058
      name: Pearson Dot
    - type: spearman_dot
      value: 0.511537896780009
      name: Spearman Dot
    - type: pearson_max
      value: 0.7645256699036285
      name: Pearson Max
    - type: spearman_max
      value: 0.7871969780219474
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8124139776213125
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8211087618006394
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7835377144525455
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7821679937822867
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.785247473429926
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7839505779526579
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5917356859640799
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5785063907246168
      name: Spearman Dot
    - type: pearson_max
      value: 0.8124139776213125
      name: Pearson Max
    - type: spearman_max
      value: 0.8211087618006394
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8079155052116238
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8190362316108264
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7794841536695422
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7786315620445202
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.781284034387115
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7812532216784576
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5714349767115854
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5601824337480018
      name: Spearman Dot
    - type: pearson_max
      value: 0.8079155052116238
      name: Pearson Max
    - type: spearman_max
      value: 0.8190362316108264
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.7987987273687178
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8128864395227673
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7727564778562619
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7727917251788465
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7734618345058613
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7751195654319647
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.5397052344713898
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5279010425382445
      name: Spearman Dot
    - type: pearson_max
      value: 0.7987987273687178
      name: Pearson Max
    - type: spearman_max
      value: 0.8128864395227673
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 32
      type: sts-test-32
    metrics:
    - type: pearson_cosine
      value: 0.7720012222035324
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7936423982593883
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7561303110063385
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7597271202292094
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7580804607973455
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7628041180101269
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.48898156184384284
      name: Pearson Dot
    - type: spearman_dot
      value: 0.47793665423562026
      name: Spearman Dot
    - type: pearson_max
      value: 0.7720012222035324
      name: Pearson Max
    - type: spearman_max
      value: 0.7936423982593883
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 16
      type: sts-test-16
    metrics:
    - type: pearson_cosine
      value: 0.7137967594997888
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7485767932719462
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7254358927069169
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7339448581065434
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.7274341928076351
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.7382083636772965
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.385573703763858
      name: Pearson Dot
    - type: spearman_dot
      value: 0.3749226996833225
      name: Spearman Dot
    - type: pearson_max
      value: 0.7274341928076351
      name: Pearson Max
    - type: spearman_max
      value: 0.7485767932719462
      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 256-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:** 256 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})
  (reduced_dim): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```

## 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("tomaarsen/distilroberta-base-nli-matryoshka-reduced")
# Run inference
sentences = [
    'A boy is vacuuming.',
    'A little boy is vacuuming the floor.',
    'A woman is applying eye shadow.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]

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

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

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

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

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

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

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.833     |
| **spearman_cosine** | **0.845** |
| pearson_manhattan   | 0.8284    |
| spearman_manhattan  | 0.8314    |
| pearson_euclidean   | 0.8291    |
| spearman_euclidean  | 0.8319    |
| pearson_dot         | 0.7274    |
| spearman_dot        | 0.7358    |
| pearson_max         | 0.833     |
| spearman_max        | 0.845     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8266     |
| **spearman_cosine** | **0.8416** |
| pearson_manhattan   | 0.825      |
| spearman_manhattan  | 0.8277     |
| pearson_euclidean   | 0.8256     |
| spearman_euclidean  | 0.8285     |
| pearson_dot         | 0.712      |
| spearman_dot        | 0.7163     |
| pearson_max         | 0.8266     |
| spearman_max        | 0.8416     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8171     |
| **spearman_cosine** | **0.8356** |
| pearson_manhattan   | 0.8176     |
| spearman_manhattan  | 0.8213     |
| pearson_euclidean   | 0.8175     |
| spearman_euclidean  | 0.8216     |
| pearson_dot         | 0.6852     |
| spearman_dot        | 0.6861     |
| pearson_max         | 0.8176     |
| spearman_max        | 0.8356     |

#### Semantic Similarity
* Dataset: `sts-dev-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7964     |
| **spearman_cosine** | **0.8244** |
| pearson_manhattan   | 0.7983     |
| spearman_manhattan  | 0.8049     |
| pearson_euclidean   | 0.8003     |
| spearman_euclidean  | 0.807      |
| pearson_dot         | 0.6312     |
| spearman_dot        | 0.6277     |
| pearson_max         | 0.8003     |
| spearman_max        | 0.8244     |

#### Semantic Similarity
* Dataset: `sts-dev-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7401     |
| **spearman_cosine** | **0.7872** |
| pearson_manhattan   | 0.761      |
| spearman_manhattan  | 0.7761     |
| pearson_euclidean   | 0.7645     |
| spearman_euclidean  | 0.7794     |
| pearson_dot         | 0.5202     |
| spearman_dot        | 0.5115     |
| pearson_max         | 0.7645     |
| spearman_max        | 0.7872     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8124     |
| **spearman_cosine** | **0.8211** |
| pearson_manhattan   | 0.7835     |
| spearman_manhattan  | 0.7822     |
| pearson_euclidean   | 0.7852     |
| spearman_euclidean  | 0.784      |
| pearson_dot         | 0.5917     |
| spearman_dot        | 0.5785     |
| pearson_max         | 0.8124     |
| spearman_max        | 0.8211     |

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

| Metric              | Value     |
|:--------------------|:----------|
| pearson_cosine      | 0.8079    |
| **spearman_cosine** | **0.819** |
| pearson_manhattan   | 0.7795    |
| spearman_manhattan  | 0.7786    |
| pearson_euclidean   | 0.7813    |
| spearman_euclidean  | 0.7813    |
| pearson_dot         | 0.5714    |
| spearman_dot        | 0.5602    |
| pearson_max         | 0.8079    |
| spearman_max        | 0.819     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7988     |
| **spearman_cosine** | **0.8129** |
| pearson_manhattan   | 0.7728     |
| spearman_manhattan  | 0.7728     |
| pearson_euclidean   | 0.7735     |
| spearman_euclidean  | 0.7751     |
| pearson_dot         | 0.5397     |
| spearman_dot        | 0.5279     |
| pearson_max         | 0.7988     |
| spearman_max        | 0.8129     |

#### Semantic Similarity
* Dataset: `sts-test-32`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.772      |
| **spearman_cosine** | **0.7936** |
| pearson_manhattan   | 0.7561     |
| spearman_manhattan  | 0.7597     |
| pearson_euclidean   | 0.7581     |
| spearman_euclidean  | 0.7628     |
| pearson_dot         | 0.489      |
| spearman_dot        | 0.4779     |
| pearson_max         | 0.772      |
| spearman_max        | 0.7936     |

#### Semantic Similarity
* Dataset: `sts-test-16`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7138     |
| **spearman_cosine** | **0.7486** |
| pearson_manhattan   | 0.7254     |
| spearman_manhattan  | 0.7339     |
| pearson_euclidean   | 0.7274     |
| spearman_euclidean  | 0.7382     |
| pearson_dot         | 0.3856     |
| spearman_dot        | 0.3749     |
| pearson_max         | 0.7274     |
| spearman_max        | 0.7486     |

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### Recommendations

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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 [65dd388](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/65dd38867b600f42241d2066ba1a35fbd097fcfe)
* 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/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          256,
          128,
          64,
          32,
          16
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### sentence-transformers/stsb

* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                         | score                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | float                                                          |
  | details | <ul><li>min: 5 tokens</li><li>mean: 15.0 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.99 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                         | sentence2                                             | score             |
  |:--------------------------------------------------|:------------------------------------------------------|:------------------|
  | <code>A man with a hard hat is dancing.</code>    | <code>A man wearing a hard hat is dancing.</code>     | <code>1.0</code>  |
  | <code>A young child is riding a horse.</code>     | <code>A child is riding a horse.</code>               | <code>0.95</code> |
  | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code>  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          256,
          128,
          64,
          32,
          16
      ],
      "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`: 128
- `per_device_eval_batch_size`: 128
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: False
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `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
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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`: None
- `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 | loss    | sts-dev-128_spearman_cosine | sts-dev-16_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-32_spearman_cosine | sts-dev-64_spearman_cosine | sts-test-128_spearman_cosine | sts-test-16_spearman_cosine | sts-test-256_spearman_cosine | sts-test-32_spearman_cosine | sts-test-64_spearman_cosine |
|:------:|:----:|:-------------:|:-------:|:---------------------------:|:--------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|:---------------------------:|:---------------------------:|
| 0.0229 | 100  | 21.0363       | 14.2448 | 0.7856                      | 0.7417                     | 0.7873                      | 0.7751                     | 0.7846                     | -                            | -                           | -                            | -                           | -                           |
| 0.0459 | 200  | 11.1093       | 13.4736 | 0.7877                      | 0.7298                     | 0.7861                      | 0.7687                     | 0.7798                     | -                            | -                           | -                            | -                           | -                           |
| 0.0688 | 300  | 10.1847       | 13.7191 | 0.7877                      | 0.7284                     | 0.7898                      | 0.7617                     | 0.7755                     | -                            | -                           | -                            | -                           | -                           |
| 0.0918 | 400  | 9.356         | 13.2955 | 0.7906                      | 0.7385                     | 0.7914                      | 0.7715                     | 0.7799                     | -                            | -                           | -                            | -                           | -                           |
| 0.1147 | 500  | 8.9318        | 12.8099 | 0.7889                      | 0.7346                     | 0.7910                      | 0.7690                     | 0.7801                     | -                            | -                           | -                            | -                           | -                           |
| 0.1376 | 600  | 8.5293        | 13.7384 | 0.7814                      | 0.7362                     | 0.7866                      | 0.7656                     | 0.7736                     | -                            | -                           | -                            | -                           | -                           |
| 0.1606 | 700  | 8.7589        | 13.4466 | 0.7899                      | 0.7467                     | 0.7945                      | 0.7770                     | 0.7847                     | -                            | -                           | -                            | -                           | -                           |
| 0.1835 | 800  | 7.7941        | 13.6734 | 0.7960                      | 0.7526                     | 0.7986                      | 0.7800                     | 0.7894                     | -                            | -                           | -                            | -                           | -                           |
| 0.2065 | 900  | 7.9183        | 12.9082 | 0.7885                      | 0.7470                     | 0.7966                      | 0.7705                     | 0.7803                     | -                            | -                           | -                            | -                           | -                           |
| 0.2294 | 1000 | 7.3669        | 13.2827 | 0.7751                      | 0.7181                     | 0.7822                      | 0.7557                     | 0.7675                     | -                            | -                           | -                            | -                           | -                           |
| 0.2524 | 1100 | 7.6205        | 13.0227 | 0.7875                      | 0.7373                     | 0.7914                      | 0.7730                     | 0.7828                     | -                            | -                           | -                            | -                           | -                           |
| 0.2753 | 1200 | 7.4308        | 13.4980 | 0.7844                      | 0.7373                     | 0.7890                      | 0.7709                     | 0.7755                     | -                            | -                           | -                            | -                           | -                           |
| 0.2982 | 1300 | 7.3625        | 12.8380 | 0.7984                      | 0.7520                     | 0.8032                      | 0.7824                     | 0.7915                     | -                            | -                           | -                            | -                           | -                           |
| 0.3212 | 1400 | 6.9421        | 12.7016 | 0.7912                      | 0.7358                     | 0.7960                      | 0.7749                     | 0.7850                     | -                            | -                           | -                            | -                           | -                           |
| 0.3441 | 1500 | 7.0635        | 13.2198 | 0.8018                      | 0.7578                     | 0.8070                      | 0.7861                     | 0.7961                     | -                            | -                           | -                            | -                           | -                           |
| 0.3671 | 1600 | 6.6682        | 13.3225 | 0.7906                      | 0.7522                     | 0.7944                      | 0.7763                     | 0.7849                     | -                            | -                           | -                            | -                           | -                           |
| 0.3900 | 1700 | 6.42          | 12.7381 | 0.7984                      | 0.7449                     | 0.8021                      | 0.7806                     | 0.7911                     | -                            | -                           | -                            | -                           | -                           |
| 0.4129 | 1800 | 6.659         | 13.0247 | 0.7947                      | 0.7461                     | 0.8002                      | 0.7808                     | 0.7876                     | -                            | -                           | -                            | -                           | -                           |
| 0.4359 | 1900 | 6.1664        | 12.6814 | 0.7893                      | 0.7312                     | 0.7959                      | 0.7700                     | 0.7807                     | -                            | -                           | -                            | -                           | -                           |
| 0.4588 | 2000 | 6.392         | 13.0238 | 0.7935                      | 0.7354                     | 0.7987                      | 0.7758                     | 0.7860                     | -                            | -                           | -                            | -                           | -                           |
| 0.4818 | 2100 | 6.177         | 12.8833 | 0.7891                      | 0.7428                     | 0.7924                      | 0.7723                     | 0.7801                     | -                            | -                           | -                            | -                           | -                           |
| 0.5047 | 2200 | 6.0411        | 12.5269 | 0.7836                      | 0.7400                     | 0.7875                      | 0.7664                     | 0.7765                     | -                            | -                           | -                            | -                           | -                           |
| 0.5276 | 2300 | 6.1506        | 13.4349 | 0.7741                      | 0.7350                     | 0.7803                      | 0.7556                     | 0.7634                     | -                            | -                           | -                            | -                           | -                           |
| 0.5506 | 2400 | 6.109         | 12.6996 | 0.7808                      | 0.7326                     | 0.7860                      | 0.7663                     | 0.7735                     | -                            | -                           | -                            | -                           | -                           |
| 0.5735 | 2500 | 6.2849        | 13.2831 | 0.7874                      | 0.7365                     | 0.7932                      | 0.7727                     | 0.7794                     | -                            | -                           | -                            | -                           | -                           |
| 0.5965 | 2600 | 6.0658        | 12.9425 | 0.7988                      | 0.7481                     | 0.8042                      | 0.7818                     | 0.7889                     | -                            | -                           | -                            | -                           | -                           |
| 0.6194 | 2700 | 6.0646        | 13.0144 | 0.7965                      | 0.7509                     | 0.8010                      | 0.7800                     | 0.7875                     | -                            | -                           | -                            | -                           | -                           |
| 0.6423 | 2800 | 6.0795        | 12.7602 | 0.7912                      | 0.7472                     | 0.7937                      | 0.7778                     | 0.7818                     | -                            | -                           | -                            | -                           | -                           |
| 0.6653 | 2900 | 6.2407        | 13.2381 | 0.7829                      | 0.7381                     | 0.7873                      | 0.7664                     | 0.7765                     | -                            | -                           | -                            | -                           | -                           |
| 0.6882 | 3000 | 6.1872        | 12.9064 | 0.7942                      | 0.7516                     | 0.7965                      | 0.7793                     | 0.7857                     | -                            | -                           | -                            | -                           | -                           |
| 0.7112 | 3100 | 5.8987        | 12.9323 | 0.8065                      | 0.7585                     | 0.8087                      | 0.7909                     | 0.7989                     | -                            | -                           | -                            | -                           | -                           |
| 0.7341 | 3200 | 5.996         | 13.1017 | 0.7971                      | 0.7566                     | 0.8005                      | 0.7811                     | 0.7889                     | -                            | -                           | -                            | -                           | -                           |
| 0.7571 | 3300 | 5.3748        | 12.7601 | 0.8398                      | 0.7881                     | 0.8441                      | 0.8232                     | 0.8337                     | -                            | -                           | -                            | -                           | -                           |
| 0.7800 | 3400 | 4.0798        | 12.7221 | 0.8400                      | 0.7908                     | 0.8440                      | 0.8255                     | 0.8342                     | -                            | -                           | -                            | -                           | -                           |
| 0.8029 | 3500 | 3.6024        | 12.5445 | 0.8408                      | 0.7892                     | 0.8447                      | 0.8247                     | 0.8347                     | -                            | -                           | -                            | -                           | -                           |
| 0.8259 | 3600 | 3.4619        | 12.6025 | 0.8405                      | 0.7883                     | 0.8442                      | 0.8255                     | 0.8347                     | -                            | -                           | -                            | -                           | -                           |
| 0.8488 | 3700 | 3.2288        | 12.6636 | 0.8388                      | 0.7872                     | 0.8433                      | 0.8226                     | 0.8330                     | -                            | -                           | -                            | -                           | -                           |
| 0.8718 | 3800 | 3.0543        | 12.6475 | 0.8386                      | 0.7834                     | 0.8427                      | 0.8229                     | 0.8330                     | -                            | -                           | -                            | -                           | -                           |
| 0.8947 | 3900 | 3.0368        | 12.5390 | 0.8407                      | 0.7845                     | 0.8444                      | 0.8227                     | 0.8346                     | -                            | -                           | -                            | -                           | -                           |
| 0.9176 | 4000 | 2.9591        | 12.5709 | 0.8419                      | 0.7864                     | 0.8456                      | 0.8245                     | 0.8359                     | -                            | -                           | -                            | -                           | -                           |
| 0.9406 | 4100 | 2.944         | 12.6029 | 0.8415                      | 0.7868                     | 0.8452                      | 0.8245                     | 0.8359                     | -                            | -                           | -                            | -                           | -                           |
| 0.9635 | 4200 | 2.9032        | 12.5514 | 0.8423                      | 0.7888                     | 0.8455                      | 0.8254                     | 0.8363                     | -                            | -                           | -                            | -                           | -                           |
| 0.9865 | 4300 | 2.838         | 12.6054 | 0.8416                      | 0.7872                     | 0.8450                      | 0.8244                     | 0.8356                     | -                            | -                           | -                            | -                           | -                           |
| 1.0    | 4359 | -             | -       | -                           | -                          | -                           | -                          | -                          | 0.8190                       | 0.7486                      | 0.8211                       | 0.7936                      | 0.8129                      |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.244 kWh
- **Carbon Emitted**: 0.095 kg of CO2
- **Hours Used**: 0.923 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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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