---
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:51741
- loss:CoSENTLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Starsza para azjatycka pozuje z noworodkiem przy stole obiadowym.
  sentences:
  - Koszykarz ma zamiar zdobyć punkty dla swojej drużyny.
  - Grupa starszych osób pozuje wokół stołu w jadalni.
  - Możliwe, że układ słoneczny taki jak nasz może istnieć poza galaktyką.
- source_sentence: Englisch arbeitet überall mit Menschen, die Dinge kaufen und verkaufen,
    und in der Gastfreundschaft und im Tourismusgeschäft.
  sentences:
  - Ich bin in Maharashtra (einschließlich Mumbai) und Andhra Pradesh herumgereist,
    und ich hatte kein Problem damit, nur mit Englisch auszukommen.
  - 'Ein griechischsprachiger Sklave (δούλος, doulos) würde seinen Herrn, glaube ich,
    κύριος nennen (translit: kurios; Herr, Herr, Herr, Herr; Vokativform: κύριε).'
  - Das Paar lag auf dem Bett.
- source_sentence: Si vous vous comprenez et comprenez votre ennemi, vous aurez beaucoup
    plus de chances de gagner n'importe quelle bataille.
  sentences:
  - 'Outre les probabilités de gagner une bataille théorique, cette citation a une
    autre signification : l''importance de connaître/comprendre les autres.'
  - Une femme et un chien se promènent ensemble.
  - Un homme joue de la guitare.
- source_sentence: Un homme joue de la harpe.
  sentences:
  - Une femme joue de la guitare.
  - une femme a un enfant.
  - Un groupe de personnes est debout et assis sur le sol la nuit.
- source_sentence: Dois cães a lutar na neve.
  sentences:
  - Dois cães brincam na neve.
  - Pode sempre perguntar, então é a escolha do autor a aceitar ou não.
  - Um gato está a caminhar sobre chão de madeira dura.
datasets:
- PhilipMay/stsb_multi_mt
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test
      type: sts-test
    metrics:
    - type: pearson_cosine
      value: 0.7692321507082782
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7754664383435602
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.744008191409292
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7432706720166963
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.7776852512001898
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.76766489827043
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.8314297687820957
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8277691429963476
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.6929213209527071
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7054612855633249
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.7918682837845359
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7976160149852088
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.8513155698871028
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8484524209199422
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.7935251833509375
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7822969529870586
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.7881663420573638
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7773593792885142
      name: Spearman Cosine
    - type: pearson_cosine
      value: 0.7890643648864227
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.7837157606570725
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) and [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) datasets. 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:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 84fccfe766bcfd679e39efefe4ebf45af190ad2d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
    - [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
    - [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- **Languages:** de, en, es, fr, it, nl, pl, pt, ru, zh
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): MultiHeadGeneralizedPooling()
)
```

## 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("RomainDarous/large_directOneEpoch_maxPooling_stsModel")
# Run inference
sentences = [
    'Dois cães a lutar na neve.',
    'Dois cães brincam na neve.',
    'Pode sempre perguntar, então é a escolha do autor a aceitar ou não.',
]
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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### Direct Usage (Transformers)

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

</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`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7692     |
| **spearman_cosine** | **0.7755** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.744      |
| **spearman_cosine** | **0.7433** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7777     |
| **spearman_cosine** | **0.7677** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8314     |
| **spearman_cosine** | **0.8278** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6929     |
| **spearman_cosine** | **0.7055** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7919     |
| **spearman_cosine** | **0.7976** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8513     |
| **spearman_cosine** | **0.8485** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7935     |
| **spearman_cosine** | **0.7823** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7882     |
| **spearman_cosine** | **0.7774** |

#### Semantic Similarity

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.7891     |
| **spearman_cosine** | **0.7837** |

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## Bias, Risks and Limitations

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### Recommendations

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## Training Details

### Training Datasets
<details><summary>multi_stsb_de</summary>

#### multi_stsb_de

* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 11.58 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.53 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                      | sentence2                                                                 | score                           |
  |:---------------------------------------------------------------|:--------------------------------------------------------------------------|:--------------------------------|
  | <code>Ein Flugzeug hebt gerade ab.</code>                      | <code>Ein Flugzeug hebt gerade ab.</code>                                 | <code>1.0</code>                |
  | <code>Ein Mann spielt eine große Flöte.</code>                 | <code>Ein Mann spielt eine Flöte.</code>                                  | <code>0.7599999904632568</code> |
  | <code>Ein Mann streicht geriebenen Käse auf eine Pizza.</code> | <code>Ein Mann streicht geriebenen Käse auf eine ungekochte Pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_es</summary>

#### multi_stsb_es

* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 7 tokens</li><li>mean: 12.21 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.07 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                       | sentence2                                                             | score                           |
  |:----------------------------------------------------------------|:----------------------------------------------------------------------|:--------------------------------|
  | <code>Un avión está despegando.</code>                          | <code>Un avión está despegando.</code>                                | <code>1.0</code>                |
  | <code>Un hombre está tocando una gran flauta.</code>            | <code>Un hombre está tocando una flauta.</code>                       | <code>0.7599999904632568</code> |
  | <code>Un hombre está untando queso rallado en una pizza.</code> | <code>Un hombre está untando queso rallado en una pizza cruda.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_fr</summary>

#### multi_stsb_fr

* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 6 tokens</li><li>mean: 12.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.49 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                  | sentence2                                                            | score                           |
  |:-----------------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
  | <code>Un avion est en train de décoller.</code>            | <code>Un avion est en train de décoller.</code>                      | <code>1.0</code>                |
  | <code>Un homme joue d'une grande flûte.</code>             | <code>Un homme joue de la flûte.</code>                              | <code>0.7599999904632568</code> |
  | <code>Un homme étale du fromage râpé sur une pizza.</code> | <code>Un homme étale du fromage râpé sur une pizza non cuite.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_it</summary>

#### multi_stsb_it

* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 7 tokens</li><li>mean: 12.77 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.69 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                 | sentence2                                                                           | score                           |
  |:--------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------|
  | <code>Un aereo sta decollando.</code>                                     | <code>Un aereo sta decollando.</code>                                               | <code>1.0</code>                |
  | <code>Un uomo sta suonando un grande flauto.</code>                       | <code>Un uomo sta suonando un flauto.</code>                                        | <code>0.7599999904632568</code> |
  | <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza.</code> | <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza non cotta.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_nl</summary>

#### multi_stsb_nl

* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 6 tokens</li><li>mean: 11.67 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.55 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                               | sentence2                                                           | score                           |
  |:--------------------------------------------------------|:--------------------------------------------------------------------|:--------------------------------|
  | <code>Er gaat een vliegtuig opstijgen.</code>           | <code>Er gaat een vliegtuig opstijgen.</code>                       | <code>1.0</code>                |
  | <code>Een man speelt een grote fluit.</code>            | <code>Een man speelt fluit.</code>                                  | <code>0.7599999904632568</code> |
  | <code>Een man smeert geraspte kaas op een pizza.</code> | <code>Een man strooit geraspte kaas op een ongekookte pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_pl</summary>

#### multi_stsb_pl

* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 12.2 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.11 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                  | sentence2                                                               | score                           |
  |:-----------------------------------------------------------|:------------------------------------------------------------------------|:--------------------------------|
  | <code>Samolot wystartował.</code>                          | <code>Samolot wystartował.</code>                                       | <code>1.0</code>                |
  | <code>Człowiek gra na dużym flecie.</code>                 | <code>Człowiek gra na flecie.</code>                                    | <code>0.7599999904632568</code> |
  | <code>Mężczyzna rozsiewa na pizzy rozdrobniony ser.</code> | <code>Mężczyzna rozsiewa rozdrobniony ser na niegotowanej pizzy.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_pt</summary>

#### multi_stsb_pt

* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 7 tokens</li><li>mean: 12.33 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.29 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                         | sentence2                                                                         | score                           |
  |:------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------|
  | <code>Um avião está a descolar.</code>                            | <code>Um avião aéreo está a descolar.</code>                                      | <code>1.0</code>                |
  | <code>Um homem está a tocar uma grande flauta.</code>             | <code>Um homem está a tocar uma flauta.</code>                                    | <code>0.7599999904632568</code> |
  | <code>Um homem está a espalhar queijo desfiado numa pizza.</code> | <code>Um homem está a espalhar queijo desfiado sobre uma pizza não cozida.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_ru</summary>

#### multi_stsb_ru

* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 11.19 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.17 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                       | sentence2                                                            | score                           |
  |:------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
  | <code>Самолет взлетает.</code>                  | <code>Взлетает самолет.</code>                                       | <code>1.0</code>                |
  | <code>Человек играет на большой флейте.</code>  | <code>Человек играет на флейте.</code>                               | <code>0.7599999904632568</code> |
  | <code>Мужчина разбрасывает сыр на пиццу.</code> | <code>Мужчина разбрасывает измельченный сыр на вареную пиццу.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_zh</summary>

#### multi_stsb_zh

* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training 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: 6 tokens</li><li>mean: 10.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.79 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                     | sentence2                         | score                           |
  |:------------------------------|:----------------------------------|:--------------------------------|
  | <code>一架飞机正在起飞。</code>        | <code>一架飞机正在起飞。</code>            | <code>1.0</code>                |
  | <code>一个男人正在吹一支大笛子。</code>    | <code>一个人在吹笛子。</code>             | <code>0.7599999904632568</code> |
  | <code>一名男子正在比萨饼上涂抹奶酪丝。</code> | <code>一名男子正在将奶酪丝涂抹在未熟的披萨上。</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>

### Evaluation Datasets
<details><summary>multi_stsb_de</summary>

#### multi_stsb_de

* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 18.25 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                    | sentence2                                                  | score                          |
  |:-------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
  | <code>Ein Mann mit einem Schutzhelm tanzt.</code>            | <code>Ein Mann mit einem Schutzhelm tanzt.</code>          | <code>1.0</code>               |
  | <code>Ein kleines Kind reitet auf einem Pferd.</code>        | <code>Ein Kind reitet auf einem Pferd.</code>              | <code>0.949999988079071</code> |
  | <code>Ein Mann verfüttert eine Maus an eine Schlange.</code> | <code>Der Mann füttert die Schlange mit einer Maus.</code> | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_es</summary>

#### multi_stsb_es

* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 7 tokens</li><li>mean: 17.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.86 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                             | sentence2                                                            | score                          |
  |:----------------------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------|
  | <code>Un hombre con un casco está bailando.</code>                    | <code>Un hombre con un casco está bailando.</code>                   | <code>1.0</code>               |
  | <code>Un niño pequeño está montando a caballo.</code>                 | <code>Un niño está montando a caballo.</code>                        | <code>0.949999988079071</code> |
  | <code>Un hombre está alimentando a una serpiente con un ratón.</code> | <code>El hombre está alimentando a la serpiente con un ratón.</code> | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_fr</summary>

#### multi_stsb_fr

* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 6 tokens</li><li>mean: 19.7 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.65 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                | sentence2                                                                   | score                          |
  |:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-------------------------------|
  | <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>1.0</code>               |
  | <code>Un jeune enfant monte à cheval.</code>                             | <code>Un enfant monte à cheval.</code>                                      | <code>0.949999988079071</code> |
  | <code>Un homme donne une souris à un serpent.</code>                     | <code>L'homme donne une souris au serpent.</code>                           | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_it</summary>

#### multi_stsb_it

* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 6 tokens</li><li>mean: 18.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.43 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                         | sentence2                                                      | score                          |
  |:------------------------------------------------------------------|:---------------------------------------------------------------|:-------------------------------|
  | <code>Un uomo con l'elmetto sta ballando.</code>                  | <code>Un uomo che indossa un elmetto sta ballando.</code>      | <code>1.0</code>               |
  | <code>Un bambino piccolo sta cavalcando un cavallo.</code>        | <code>Un bambino sta cavalcando un cavallo.</code>             | <code>0.949999988079071</code> |
  | <code>Un uomo sta dando da mangiare un topo a un serpente.</code> | <code>L'uomo sta dando da mangiare un topo al serpente.</code> | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_nl</summary>

#### multi_stsb_nl

* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 17.88 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.71 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                            | sentence2                                            | score                          |
  |:-----------------------------------------------------|:-----------------------------------------------------|:-------------------------------|
  | <code>Een man met een helm is aan het dansen.</code> | <code>Een man met een helm is aan het dansen.</code> | <code>1.0</code>               |
  | <code>Een jong kind rijdt op een paard.</code>       | <code>Een kind rijdt op een paard.</code>            | <code>0.949999988079071</code> |
  | <code>Een man voedt een muis aan een slang.</code>   | <code>De man voert een muis aan de slang.</code>     | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_pl</summary>

#### multi_stsb_pl

* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.43 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                          | sentence2                                          | score                          |
  |:---------------------------------------------------|:---------------------------------------------------|:-------------------------------|
  | <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>1.0</code>               |
  | <code>Małe dziecko jedzie na koniu.</code>         | <code>Dziecko jedzie na koniu.</code>              | <code>0.949999988079071</code> |
  | <code>Człowiek karmi węża myszką.</code>           | <code>Ten człowiek karmi węża myszką.</code>       | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_pt</summary>

#### multi_stsb_pt

* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 7 tokens</li><li>mean: 18.22 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.11 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                   | sentence2                                                  | score                          |
  |:------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
  | <code>Um homem de chapéu duro está a dançar.</code>         | <code>Um homem com um capacete está a dançar.</code>       | <code>1.0</code>               |
  | <code>Uma criança pequena está a montar a cavalo.</code>    | <code>Uma criança está a montar a cavalo.</code>           | <code>0.949999988079071</code> |
  | <code>Um homem está a alimentar um rato a uma cobra.</code> | <code>O homem está a alimentar a cobra com um rato.</code> | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_ru</summary>

#### multi_stsb_ru

* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 6 tokens</li><li>mean: 17.92 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.75 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                             | sentence2                                     | score                          |
  |:------------------------------------------------------|:----------------------------------------------|:-------------------------------|
  | <code>Человек в твердой шляпе танцует.</code>         | <code>Мужчина в твердой шляпе танцует.</code> | <code>1.0</code>               |
  | <code>Маленький ребенок едет верхом на лошади.</code> | <code>Ребенок едет на лошади.</code>          | <code>0.949999988079071</code> |
  | <code>Мужчина кормит мышь змее.</code>                | <code>Человек кормит змею мышью.</code>       | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>
<details><summary>multi_stsb_zh</summary>

#### multi_stsb_zh

* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* 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: 6 tokens</li><li>mean: 15.37 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.24 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                  | sentence2                 | score                          |
  |:---------------------------|:--------------------------|:-------------------------------|
  | <code>一个戴着硬帽子的人在跳舞。</code> | <code>一个戴着硬帽的人在跳舞。</code> | <code>1.0</code>               |
  | <code>一个小孩子在骑马。</code>     | <code>一个孩子在骑马。</code>     | <code>0.949999988079071</code> |
  | <code>一个人正在用老鼠喂蛇。</code>   | <code>那人正在给蛇喂老鼠。</code>   | <code>1.0</code>               |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```
</details>

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: False
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | sts-test_spearman_cosine |
|:-----:|:----:|:------------------------:|
| -1    | -1   | 0.7837                   |


### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.3.0
- Datasets: 2.16.1
- Tokenizers: 0.21.0

## 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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