---
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:10K<n<100K
- loss:MatryoshkaLoss
- loss:ContrastiveLoss
base_model: aari1995/gbert-large-nli_mix
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Das Tor ist gelb.
  sentences:
  - Das Tor ist blau.
  - Ein Mann mit seinem Hund am Strand.
  - Die Menschen sitzen auf Bänken.
- source_sentence: Das Tor ist blau.
  sentences:
  - Ein blaues Moped parkt auf dem Bürgersteig.
  - Drei Hunde spielen im weißen Schnee.
  - Bombenanschläge töten 19 Menschen im Irak
- source_sentence: Ein Mann übt Boxen
  sentences:
  - Ein Fußballspieler versucht ein Tackling.
  - 1 Getötet bei Protest in Bangladesch
  - Das Mädchen sang in ein Mikrofon.
- source_sentence: Drei Männer tanzen.
  sentences:
  - Ein Mann tanzt.
  - Ein Mann arbeitet an seinem Laptop.
  - Das Mädchen sang in ein Mikrofon.
- source_sentence: Eine Flagge weht.
  sentences:
  - Die Flagge bewegte sich in der Luft.
  - Zwei Personen beobachten das Wasser.
  - Zwei Frauen sitzen in einem Cafe.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on aari1995/gbert-large-nli_mix
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 1024
      type: sts-dev-1024
    metrics:
    - type: pearson_cosine
      value: 0.873823661552029
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8803520711782152
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.876117767161979
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8820122762561675
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8762079650155435
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8820817487274982
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.838279478558382
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8381052886607077
      name: Spearman Dot
    - type: pearson_max
      value: 0.8762079650155435
      name: Pearson Max
    - type: spearman_max
      value: 0.8820817487274982
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 768
      type: sts-dev-768
    metrics:
    - type: pearson_cosine
      value: 0.8729182431103752
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8798510743177114
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8750916595783815
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8809884317625296
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8754527585231735
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8811764170967997
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8386088963989539
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8387608674072754
      name: Spearman Dot
    - type: pearson_max
      value: 0.8754527585231735
      name: Pearson Max
    - type: spearman_max
      value: 0.8811764170967997
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8710783395197956
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.878639260136433
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8744942112479004
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.880169853184795
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8750968130873006
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8805091146806316
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8320844036361574
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8320098342545608
      name: Spearman Dot
    - type: pearson_max
      value: 0.8750968130873006
      name: Pearson Max
    - type: spearman_max
      value: 0.8805091146806316
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.8648952635235024
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8746516550395731
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8708389858444562
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.876029234462836
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8719490370119019
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.876707897776359
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.814982046736955
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8168481427335235
      name: Spearman Dot
    - type: pearson_max
      value: 0.8719490370119019
      name: Pearson Max
    - type: spearman_max
      value: 0.876707897776359
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8584911759712609
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8704026301204416
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8657220587707122
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.869723396167326
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8680692506297197
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8718542166801199
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8005092818222429
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8021754345558865
      name: Spearman Dot
    - type: pearson_max
      value: 0.8680692506297197
      name: Pearson Max
    - type: spearman_max
      value: 0.8718542166801199
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.8483333803717887
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8652221599413363
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8595603525995048
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.863342194337673
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8635697556624868
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8668222027396277
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7733853267769795
      name: Pearson Dot
    - type: spearman_dot
      value: 0.775678170624028
      name: Spearman Dot
    - type: pearson_max
      value: 0.8635697556624868
      name: Pearson Max
    - type: spearman_max
      value: 0.8668222027396277
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 1024
      type: sts-test-1024
    metrics:
    - type: pearson_cosine
      value: 0.8538749625112824
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8622934726599119
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8554617861095041
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8632850500504865
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8554205957277228
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8630779166725503
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8170146846171837
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8149857685956332
      name: Spearman Dot
    - type: pearson_max
      value: 0.8554617861095041
      name: Pearson Max
    - type: spearman_max
      value: 0.8632850500504865
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.853820621972726
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.863198271488271
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8558709278385018
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8637532036004547
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8558597695346744
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8634247094122574
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8169163431962185
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8156867907361973
      name: Spearman Dot
    - type: pearson_max
      value: 0.8558709278385018
      name: Pearson Max
    - type: spearman_max
      value: 0.8637532036004547
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8502336569709972
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8623838162450902
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8547121881183612
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8628698143219098
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8546114371189246
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8625109910600326
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8108392647310044
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8103261097232485
      name: Spearman Dot
    - type: pearson_max
      value: 0.8547121881183612
      name: Pearson Max
    - type: spearman_max
      value: 0.8628698143219098
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8441242786553879
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8582717489671877
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8517415030362573
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8591688553092182
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8516965854845419
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8591770194196562
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7901870400809775
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7891397281321177
      name: Spearman Dot
    - type: pearson_max
      value: 0.8517415030362573
      name: Pearson Max
    - type: spearman_max
      value: 0.8591770194196562
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.8369352495821198
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8545806562301762
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8474289413580527
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8546935424655524
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8478267316251253
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8550464936365929
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7732663297266509
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7720532782903432
      name: Spearman Dot
    - type: pearson_max
      value: 0.8478267316251253
      name: Pearson Max
    - type: spearman_max
      value: 0.8550464936365929
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.8282288301025145
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8507215646125454
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8404915813802649
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8482910175231816
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8425986040609018
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8498681513437906
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7518854418344252
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7518133373839283
      name: Spearman Dot
    - type: pearson_max
      value: 0.8425986040609018
      name: Pearson Max
    - type: spearman_max
      value: 0.8507215646125454
      name: Spearman Max
---

# SentenceTransformer based on aari1995/gbert-large-nli_mix

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aari1995/gbert-large-nli_mix](https://huggingface.co/aari1995/gbert-large-nli_mix) on the [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) dataset. It maps sentences & paragraphs to a 1024-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:** [aari1995/gbert-large-nli_mix](https://huggingface.co/aari1995/gbert-large-nli_mix) <!-- at revision 86b82327d5898d81f9b8caafbf228b803f25abc1 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [PhilipMay/stsb_multi_mt](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': 8192, 'do_lower_case': False}) with Transformer model: JinaBertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

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

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

# Download from the 🤗 Hub
model = SentenceTransformer("aari1995/German_Semantic_V3_2_STS_MIX")
# Run inference
sentences = [
    'Eine Flagge weht.',
    'Die Flagge bewegte sich in der Luft.',
    'Zwei Personen beobachten das Wasser.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, 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>
-->

<!--
### Out-of-Scope Use

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

## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `sts-dev-1024`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8738     |
| **spearman_cosine** | **0.8804** |
| pearson_manhattan   | 0.8761     |
| spearman_manhattan  | 0.882      |
| pearson_euclidean   | 0.8762     |
| spearman_euclidean  | 0.8821     |
| pearson_dot         | 0.8383     |
| spearman_dot        | 0.8381     |
| pearson_max         | 0.8762     |
| spearman_max        | 0.8821     |

#### Semantic Similarity
* Dataset: `sts-dev-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8729     |
| **spearman_cosine** | **0.8799** |
| pearson_manhattan   | 0.8751     |
| spearman_manhattan  | 0.881      |
| pearson_euclidean   | 0.8755     |
| spearman_euclidean  | 0.8812     |
| pearson_dot         | 0.8386     |
| spearman_dot        | 0.8388     |
| pearson_max         | 0.8755     |
| spearman_max        | 0.8812     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8711     |
| **spearman_cosine** | **0.8786** |
| pearson_manhattan   | 0.8745     |
| spearman_manhattan  | 0.8802     |
| pearson_euclidean   | 0.8751     |
| spearman_euclidean  | 0.8805     |
| pearson_dot         | 0.8321     |
| spearman_dot        | 0.832      |
| pearson_max         | 0.8751     |
| spearman_max        | 0.8805     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8649     |
| **spearman_cosine** | **0.8747** |
| pearson_manhattan   | 0.8708     |
| spearman_manhattan  | 0.876      |
| pearson_euclidean   | 0.8719     |
| spearman_euclidean  | 0.8767     |
| pearson_dot         | 0.815      |
| spearman_dot        | 0.8168     |
| pearson_max         | 0.8719     |
| spearman_max        | 0.8767     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8585     |
| **spearman_cosine** | **0.8704** |
| pearson_manhattan   | 0.8657     |
| spearman_manhattan  | 0.8697     |
| pearson_euclidean   | 0.8681     |
| spearman_euclidean  | 0.8719     |
| pearson_dot         | 0.8005     |
| spearman_dot        | 0.8022     |
| pearson_max         | 0.8681     |
| spearman_max        | 0.8719     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8483     |
| **spearman_cosine** | **0.8652** |
| pearson_manhattan   | 0.8596     |
| spearman_manhattan  | 0.8633     |
| pearson_euclidean   | 0.8636     |
| spearman_euclidean  | 0.8668     |
| pearson_dot         | 0.7734     |
| spearman_dot        | 0.7757     |
| pearson_max         | 0.8636     |
| spearman_max        | 0.8668     |

#### Semantic Similarity
* Dataset: `sts-test-1024`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8539     |
| **spearman_cosine** | **0.8623** |
| pearson_manhattan   | 0.8555     |
| spearman_manhattan  | 0.8633     |
| pearson_euclidean   | 0.8554     |
| spearman_euclidean  | 0.8631     |
| pearson_dot         | 0.817      |
| spearman_dot        | 0.815      |
| pearson_max         | 0.8555     |
| spearman_max        | 0.8633     |

#### Semantic Similarity
* Dataset: `sts-test-768`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8538     |
| **spearman_cosine** | **0.8632** |
| pearson_manhattan   | 0.8559     |
| spearman_manhattan  | 0.8638     |
| pearson_euclidean   | 0.8559     |
| spearman_euclidean  | 0.8634     |
| pearson_dot         | 0.8169     |
| spearman_dot        | 0.8157     |
| pearson_max         | 0.8559     |
| spearman_max        | 0.8638     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8502     |
| **spearman_cosine** | **0.8624** |
| pearson_manhattan   | 0.8547     |
| spearman_manhattan  | 0.8629     |
| pearson_euclidean   | 0.8546     |
| spearman_euclidean  | 0.8625     |
| pearson_dot         | 0.8108     |
| spearman_dot        | 0.8103     |
| pearson_max         | 0.8547     |
| spearman_max        | 0.8629     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8441     |
| **spearman_cosine** | **0.8583** |
| pearson_manhattan   | 0.8517     |
| spearman_manhattan  | 0.8592     |
| pearson_euclidean   | 0.8517     |
| spearman_euclidean  | 0.8592     |
| pearson_dot         | 0.7902     |
| spearman_dot        | 0.7891     |
| pearson_max         | 0.8517     |
| spearman_max        | 0.8592     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8369     |
| **spearman_cosine** | **0.8546** |
| pearson_manhattan   | 0.8474     |
| spearman_manhattan  | 0.8547     |
| pearson_euclidean   | 0.8478     |
| spearman_euclidean  | 0.855      |
| pearson_dot         | 0.7733     |
| spearman_dot        | 0.7721     |
| pearson_max         | 0.8478     |
| spearman_max        | 0.855      |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8282     |
| **spearman_cosine** | **0.8507** |
| pearson_manhattan   | 0.8405     |
| spearman_manhattan  | 0.8483     |
| pearson_euclidean   | 0.8426     |
| spearman_euclidean  | 0.8499     |
| pearson_dot         | 0.7519     |
| spearman_dot        | 0.7518     |
| pearson_max         | 0.8426     |
| spearman_max        | 0.8507     |

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## Training Details

### Training Dataset

#### PhilipMay/stsb_multi_mt

* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 22,996 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: 18.13 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence1                                                                | sentence2                                                                        | score                           |
  |:-------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------|
  | <code>schütze wegen mordes an schwarzem us-jugendlichen angeklagt</code> | <code>gedanken zu den rassenbeziehungen unter einem schwarzen präsidenten</code> | <code>0.1599999964237213</code> |
  | <code>fußballspieler kicken einen fußball in das tor.</code>             | <code>Ein Fußballspieler schießt ein Tor.</code>                                 | <code>0.7599999904632568</code> |
  | <code>obama lockert abschiebungsregeln für junge einwanderer</code>      | <code>usa lockert abschiebebestimmungen für jugendliche: napolitano</code>       | <code>0.800000011920929</code>  |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "ContrastiveLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### PhilipMay/stsb_multi_mt

* Dataset: [PhilipMay/stsb_multi_mt](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: 16.54 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.53 tokens</li><li>max: 47 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>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>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "ContrastiveLoss",
      "matryoshka_dims": [
          1024,
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `learning_rate`: 5e-06
- `num_train_epochs`: 4
- `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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

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

| Epoch  | Step  | Training Loss | loss   | sts-dev-1024_spearman_cosine | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-1024_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:----------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0348 | 100   | 0.2334        | 0.2530 | 0.8329                       | 0.8219                      | 0.8274                      | 0.8292                      | 0.8148                     | 0.8317                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.0696 | 200   | 0.1959        | 0.1921 | 0.8285                       | 0.8183                      | 0.8234                      | 0.8250                      | 0.8121                     | 0.8275                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1043 | 300   | 0.1468        | 0.1592 | 0.8346                       | 0.8267                      | 0.8305                      | 0.8319                      | 0.8227                     | 0.8334                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1391 | 400   | 0.1346        | 0.1511 | 0.8513                       | 0.8451                      | 0.8486                      | 0.8497                      | 0.8418                     | 0.8505                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1739 | 500   | 0.1333        | 0.1480 | 0.8590                       | 0.8526                      | 0.8563                      | 0.8576                      | 0.8502                     | 0.8583                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2087 | 600   | 0.1328        | 0.1478 | 0.8626                       | 0.8557                      | 0.8595                      | 0.8612                      | 0.8530                     | 0.8620                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2435 | 700   | 0.1345        | 0.1451 | 0.8631                       | 0.8563                      | 0.8599                      | 0.8618                      | 0.8548                     | 0.8626                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2783 | 800   | 0.1282        | 0.1423 | 0.8705                       | 0.8625                      | 0.8671                      | 0.8692                      | 0.8601                     | 0.8698                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3130 | 900   | 0.1317        | 0.1416 | 0.8724                       | 0.8639                      | 0.8690                      | 0.8714                      | 0.8619                     | 0.8716                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3478 | 1000  | 0.1295        | 0.1422 | 0.8641                       | 0.8577                      | 0.8617                      | 0.8637                      | 0.8556                     | 0.8639                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3826 | 1100  | 0.1267        | 0.1427 | 0.8675                       | 0.8603                      | 0.8644                      | 0.8666                      | 0.8581                     | 0.8671                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4174 | 1200  | 0.127         | 0.1417 | 0.8674                       | 0.8589                      | 0.8635                      | 0.8664                      | 0.8570                     | 0.8671                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4522 | 1300  | 0.1292        | 0.1419 | 0.8756                       | 0.8663                      | 0.8711                      | 0.8739                      | 0.8641                     | 0.8748                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4870 | 1400  | 0.1281        | 0.1411 | 0.8726                       | 0.8646                      | 0.8686                      | 0.8713                      | 0.8616                     | 0.8721                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5217 | 1500  | 0.1292        | 0.1407 | 0.8738                       | 0.8654                      | 0.8698                      | 0.8727                      | 0.8617                     | 0.8739                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5565 | 1600  | 0.1251        | 0.1419 | 0.8732                       | 0.8643                      | 0.8686                      | 0.8720                      | 0.8605                     | 0.8731                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5913 | 1700  | 0.1288        | 0.1412 | 0.8782                       | 0.8682                      | 0.8731                      | 0.8769                      | 0.8652                     | 0.8779                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6261 | 1800  | 0.1306        | 0.1405 | 0.8755                       | 0.8664                      | 0.8710                      | 0.8744                      | 0.8632                     | 0.8751                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6609 | 1900  | 0.1289        | 0.1410 | 0.8739                       | 0.8647                      | 0.8691                      | 0.8727                      | 0.8614                     | 0.8736                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6957 | 2000  | 0.1287        | 0.1403 | 0.8773                       | 0.8669                      | 0.8719                      | 0.8758                      | 0.8637                     | 0.8769                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.7304 | 2100  | 0.126         | 0.1402 | 0.8773                       | 0.8675                      | 0.8722                      | 0.8758                      | 0.8635                     | 0.8772                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.7652 | 2200  | 0.1274        | 0.1401 | 0.8799                       | 0.8693                      | 0.8743                      | 0.8784                      | 0.8652                     | 0.8797                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8    | 2300  | 0.1234        | 0.1399 | 0.8777                       | 0.8686                      | 0.8729                      | 0.8767                      | 0.8650                     | 0.8778                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8348 | 2400  | 0.128         | 0.1401 | 0.8769                       | 0.8660                      | 0.8712                      | 0.8759                      | 0.8621                     | 0.8768                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8696 | 2500  | 0.1269        | 0.1403 | 0.8756                       | 0.8648                      | 0.8698                      | 0.8742                      | 0.8605                     | 0.8750                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9043 | 2600  | 0.1243        | 0.1401 | 0.8762                       | 0.8665                      | 0.8711                      | 0.8751                      | 0.8622                     | 0.8760                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9391 | 2700  | 0.1277        | 0.1406 | 0.8742                       | 0.8649                      | 0.8693                      | 0.8725                      | 0.8613                     | 0.8738                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9739 | 2800  | 0.1287        | 0.1394 | 0.8789                       | 0.8689                      | 0.8738                      | 0.8773                      | 0.8648                     | 0.8785                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.0087 | 2900  | 0.1274        | 0.1397 | 0.8784                       | 0.8682                      | 0.8731                      | 0.8769                      | 0.8632                     | 0.8782                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.0435 | 3000  | 0.129         | 0.1401 | 0.8800                       | 0.8693                      | 0.8743                      | 0.8782                      | 0.8653                     | 0.8795                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.0783 | 3100  | 0.121         | 0.1408 | 0.8785                       | 0.8682                      | 0.8731                      | 0.8769                      | 0.8638                     | 0.8782                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.1130 | 3200  | 0.1249        | 0.1399 | 0.8773                       | 0.8668                      | 0.8722                      | 0.8759                      | 0.8625                     | 0.8771                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.1478 | 3300  | 0.1252        | 0.1404 | 0.8740                       | 0.8643                      | 0.8688                      | 0.8724                      | 0.8593                     | 0.8737                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.1826 | 3400  | 0.126         | 0.1398 | 0.8761                       | 0.8657                      | 0.8707                      | 0.8745                      | 0.8610                     | 0.8758                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.2174 | 3500  | 0.1279        | 0.1400 | 0.8760                       | 0.8661                      | 0.8708                      | 0.8745                      | 0.8617                     | 0.8759                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.2522 | 3600  | 0.1264        | 0.1399 | 0.8786                       | 0.8684                      | 0.8734                      | 0.8768                      | 0.8633                     | 0.8783                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.2870 | 3700  | 0.126         | 0.1395 | 0.8789                       | 0.8690                      | 0.8734                      | 0.8773                      | 0.8643                     | 0.8786                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.3217 | 3800  | 0.1234        | 0.1399 | 0.8777                       | 0.8669                      | 0.8723                      | 0.8760                      | 0.8625                     | 0.8775                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.3565 | 3900  | 0.1269        | 0.1397 | 0.8777                       | 0.8671                      | 0.8725                      | 0.8760                      | 0.8630                     | 0.8773                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.3913 | 4000  | 0.1223        | 0.1393 | 0.8806                       | 0.8694                      | 0.8751                      | 0.8789                      | 0.8654                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.4261 | 4100  | 0.1227        | 0.1399 | 0.8775                       | 0.8671                      | 0.8728                      | 0.8764                      | 0.8622                     | 0.8774                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.4609 | 4200  | 0.1263        | 0.1402 | 0.8771                       | 0.8669                      | 0.8724                      | 0.8756                      | 0.8619                     | 0.8769                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.4957 | 4300  | 0.1263        | 0.1400 | 0.8781                       | 0.8674                      | 0.8730                      | 0.8766                      | 0.8627                     | 0.8778                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.5304 | 4400  | 0.1302        | 0.1396 | 0.8778                       | 0.8675                      | 0.8728                      | 0.8761                      | 0.8628                     | 0.8775                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.5652 | 4500  | 0.1274        | 0.1393 | 0.8789                       | 0.8685                      | 0.8736                      | 0.8770                      | 0.8637                     | 0.8784                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.6    | 4600  | 0.1273        | 0.1394 | 0.8794                       | 0.8683                      | 0.8737                      | 0.8773                      | 0.8637                     | 0.8789                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.6348 | 4700  | 0.1297        | 0.1391 | 0.8822                       | 0.8712                      | 0.8764                      | 0.8800                      | 0.8666                     | 0.8817                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.6696 | 4800  | 0.1249        | 0.1392 | 0.8804                       | 0.8694                      | 0.8748                      | 0.8785                      | 0.8643                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.7043 | 4900  | 0.1286        | 0.1390 | 0.8803                       | 0.8693                      | 0.8746                      | 0.8784                      | 0.8643                     | 0.8800                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.7391 | 5000  | 0.1271        | 0.1392 | 0.8799                       | 0.8697                      | 0.8745                      | 0.8780                      | 0.8645                     | 0.8795                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.7739 | 5100  | 0.1293        | 0.1391 | 0.8803                       | 0.8702                      | 0.8748                      | 0.8790                      | 0.8648                     | 0.8803                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.8087 | 5200  | 0.1233        | 0.1391 | 0.8793                       | 0.8692                      | 0.8739                      | 0.8777                      | 0.8639                     | 0.8791                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.8435 | 5300  | 0.1239        | 0.1394 | 0.8805                       | 0.8705                      | 0.8748                      | 0.8788                      | 0.8656                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.8783 | 5400  | 0.124         | 0.1392 | 0.8795                       | 0.8692                      | 0.8742                      | 0.8780                      | 0.8640                     | 0.8792                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.9130 | 5500  | 0.1245        | 0.1390 | 0.8797                       | 0.8697                      | 0.8744                      | 0.8782                      | 0.8645                     | 0.8794                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.9478 | 5600  | 0.1257        | 0.1391 | 0.8794                       | 0.8689                      | 0.8741                      | 0.8778                      | 0.8637                     | 0.8791                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.9826 | 5700  | 0.1231        | 0.1389 | 0.8807                       | 0.8708                      | 0.8756                      | 0.8793                      | 0.8663                     | 0.8804                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.0174 | 5800  | 0.1216        | 0.1390 | 0.8781                       | 0.8678                      | 0.8733                      | 0.8768                      | 0.8630                     | 0.8779                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.0522 | 5900  | 0.1252        | 0.1387 | 0.8795                       | 0.8695                      | 0.8745                      | 0.8784                      | 0.8639                     | 0.8794                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.0870 | 6000  | 0.1242        | 0.1387 | 0.8799                       | 0.8703                      | 0.8749                      | 0.8787                      | 0.8652                     | 0.8798                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.1217 | 6100  | 0.1231        | 0.1392 | 0.8796                       | 0.8702                      | 0.8748                      | 0.8784                      | 0.8653                     | 0.8795                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.1565 | 6200  | 0.1217        | 0.1391 | 0.8797                       | 0.8704                      | 0.8746                      | 0.8784                      | 0.8655                     | 0.8794                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.1913 | 6300  | 0.1259        | 0.1389 | 0.8803                       | 0.8710                      | 0.8756                      | 0.8789                      | 0.8664                     | 0.8800                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.2261 | 6400  | 0.1262        | 0.1386 | 0.8813                       | 0.8714                      | 0.8762                      | 0.8796                      | 0.8667                     | 0.8809                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.2609 | 6500  | 0.127         | 0.1392 | 0.8793                       | 0.8701                      | 0.8743                      | 0.8778                      | 0.8652                     | 0.8792                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.2957 | 6600  | 0.1275        | 0.1391 | 0.8806                       | 0.8710                      | 0.8755                      | 0.8788                      | 0.8663                     | 0.8803                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.3304 | 6700  | 0.1228        | 0.1394 | 0.8795                       | 0.8693                      | 0.8741                      | 0.8774                      | 0.8646                     | 0.8791                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.3652 | 6800  | 0.1243        | 0.1390 | 0.8803                       | 0.8700                      | 0.8747                      | 0.8783                      | 0.8655                     | 0.8797                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.4    | 6900  | 0.1292        | 0.1389 | 0.8795                       | 0.8697                      | 0.8743                      | 0.8778                      | 0.8650                     | 0.8791                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.4348 | 7000  | 0.1238        | 0.1390 | 0.8799                       | 0.8697                      | 0.8744                      | 0.8782                      | 0.8648                     | 0.8795                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.4696 | 7100  | 0.1246        | 0.1389 | 0.8800                       | 0.8695                      | 0.8743                      | 0.8780                      | 0.8649                     | 0.8795                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.5043 | 7200  | 0.1265        | 0.1396 | 0.8802                       | 0.8695                      | 0.8743                      | 0.8781                      | 0.8647                     | 0.8796                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.5391 | 7300  | 0.1229        | 0.1390 | 0.8813                       | 0.8708                      | 0.8753                      | 0.8796                      | 0.8665                     | 0.8809                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.5739 | 7400  | 0.1244        | 0.1389 | 0.8808                       | 0.8706                      | 0.8749                      | 0.8790                      | 0.8665                     | 0.8803                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.6087 | 7500  | 0.1223        | 0.1389 | 0.8813                       | 0.8709                      | 0.8753                      | 0.8797                      | 0.8662                     | 0.8807                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.6435 | 7600  | 0.1268        | 0.1387 | 0.8810                       | 0.8704                      | 0.8752                      | 0.8793                      | 0.8659                     | 0.8805                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.6783 | 7700  | 0.1218        | 0.1387 | 0.8817                       | 0.8710                      | 0.8755                      | 0.8798                      | 0.8665                     | 0.8809                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.7130 | 7800  | 0.1225        | 0.1388 | 0.8804                       | 0.8700                      | 0.8745                      | 0.8787                      | 0.8653                     | 0.8799                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.7478 | 7900  | 0.1263        | 0.1391 | 0.8807                       | 0.8703                      | 0.8745                      | 0.8788                      | 0.8654                     | 0.8801                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.7826 | 8000  | 0.1261        | 0.1388 | 0.8804                       | 0.8698                      | 0.8743                      | 0.8787                      | 0.8652                     | 0.8799                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.8174 | 8100  | 0.1267        | 0.1386 | 0.8814                       | 0.8707                      | 0.8750                      | 0.8795                      | 0.8658                     | 0.8807                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.8522 | 8200  | 0.1236        | 0.1387 | 0.8809                       | 0.8703                      | 0.8747                      | 0.8792                      | 0.8659                     | 0.8803                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.8870 | 8300  | 0.1222        | 0.1390 | 0.8802                       | 0.8696                      | 0.8741                      | 0.8786                      | 0.8649                     | 0.8799                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.9217 | 8400  | 0.1236        | 0.1388 | 0.8807                       | 0.8700                      | 0.8747                      | 0.8790                      | 0.8653                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.9565 | 8500  | 0.1233        | 0.1389 | 0.8808                       | 0.8705                      | 0.8752                      | 0.8791                      | 0.8659                     | 0.8806                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 2.9913 | 8600  | 0.1262        | 0.1388 | 0.8808                       | 0.8704                      | 0.8750                      | 0.8792                      | 0.8658                     | 0.8805                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.0261 | 8700  | 0.1277        | 0.1388 | 0.8795                       | 0.8690                      | 0.8737                      | 0.8778                      | 0.8640                     | 0.8791                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.0609 | 8800  | 0.1243        | 0.1387 | 0.8809                       | 0.8705                      | 0.8751                      | 0.8791                      | 0.8656                     | 0.8803                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.0957 | 8900  | 0.1206        | 0.1387 | 0.8813                       | 0.8709                      | 0.8754                      | 0.8796                      | 0.8661                     | 0.8807                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.1304 | 9000  | 0.1217        | 0.1388 | 0.8815                       | 0.8716                      | 0.8758                      | 0.8797                      | 0.8670                     | 0.8810                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.1652 | 9100  | 0.1236        | 0.1390 | 0.8803                       | 0.8702                      | 0.8744                      | 0.8785                      | 0.8653                     | 0.8798                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.2    | 9200  | 0.1244        | 0.1389 | 0.8799                       | 0.8697                      | 0.8741                      | 0.8783                      | 0.8647                     | 0.8795                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.2348 | 9300  | 0.1247        | 0.1388 | 0.8802                       | 0.8698                      | 0.8743                      | 0.8785                      | 0.8650                     | 0.8798                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.2696 | 9400  | 0.1214        | 0.1388 | 0.8810                       | 0.8710                      | 0.8751                      | 0.8793                      | 0.8663                     | 0.8806                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.3043 | 9500  | 0.121         | 0.1386 | 0.8808                       | 0.8709                      | 0.8749                      | 0.8791                      | 0.8662                     | 0.8803                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.3391 | 9600  | 0.1205        | 0.1387 | 0.8804                       | 0.8705                      | 0.8746                      | 0.8789                      | 0.8655                     | 0.8800                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.3739 | 9700  | 0.1203        | 0.1387 | 0.8807                       | 0.8708                      | 0.8750                      | 0.8790                      | 0.8661                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.4087 | 9800  | 0.1239        | 0.1386 | 0.8811                       | 0.8711                      | 0.8752                      | 0.8794                      | 0.8663                     | 0.8805                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.4435 | 9900  | 0.1197        | 0.1387 | 0.8808                       | 0.8709                      | 0.8750                      | 0.8792                      | 0.8662                     | 0.8804                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.4783 | 10000 | 0.1252        | 0.1388 | 0.8805                       | 0.8704                      | 0.8746                      | 0.8787                      | 0.8657                     | 0.8800                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.5130 | 10100 | 0.1229        | 0.1388 | 0.8803                       | 0.8703                      | 0.8745                      | 0.8786                      | 0.8654                     | 0.8799                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.5478 | 10200 | 0.1258        | 0.1387 | 0.8805                       | 0.8704                      | 0.8747                      | 0.8787                      | 0.8653                     | 0.8801                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.5826 | 10300 | 0.1232        | 0.1387 | 0.8806                       | 0.8706                      | 0.8750                      | 0.8790                      | 0.8656                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.6174 | 10400 | 0.1286        | 0.1388 | 0.8807                       | 0.8706                      | 0.8749                      | 0.8790                      | 0.8656                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.6522 | 10500 | 0.1248        | 0.1387 | 0.8806                       | 0.8706                      | 0.8748                      | 0.8789                      | 0.8653                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.6870 | 10600 | 0.1277        | 0.1389 | 0.8800                       | 0.8699                      | 0.8742                      | 0.8782                      | 0.8647                     | 0.8796                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.7217 | 10700 | 0.1219        | 0.1388 | 0.8799                       | 0.8697                      | 0.8740                      | 0.8780                      | 0.8645                     | 0.8794                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.7565 | 10800 | 0.1269        | 0.1388 | 0.8803                       | 0.8702                      | 0.8745                      | 0.8785                      | 0.8649                     | 0.8798                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.7913 | 10900 | 0.1289        | 0.1387 | 0.8805                       | 0.8703                      | 0.8746                      | 0.8787                      | 0.8651                     | 0.8800                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.8261 | 11000 | 0.1234        | 0.1387 | 0.8806                       | 0.8704                      | 0.8749                      | 0.8789                      | 0.8653                     | 0.8801                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.8609 | 11100 | 0.1229        | 0.1387 | 0.8806                       | 0.8706                      | 0.8749                      | 0.8788                      | 0.8654                     | 0.8802                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.8957 | 11200 | 0.1266        | 0.1387 | 0.8806                       | 0.8706                      | 0.8749                      | 0.8789                      | 0.8655                     | 0.8801                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.9304 | 11300 | 0.1253        | 0.1387 | 0.8804                       | 0.8704                      | 0.8747                      | 0.8787                      | 0.8653                     | 0.8800                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 3.9652 | 11400 | 0.1279        | 0.1388 | 0.8804                       | 0.8704                      | 0.8747                      | 0.8787                      | 0.8653                     | 0.8799                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 4.0    | 11500 | 0.1195        | 0.1388 | 0.8804                       | 0.8704                      | 0.8747                      | 0.8786                      | 0.8652                     | 0.8799                      | 0.8623                        | 0.8546                       | 0.8583                       | 0.8624                       | 0.8507                      | 0.8632                       |

</details>

### Framework Versions
- Python: 3.9.16
- Sentence Transformers: 3.0.0
- Transformers: 4.42.0.dev0
- PyTorch: 2.2.2+cu118
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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}
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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