---
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:CosineSimilarityLoss
base_model: aari1995/gbert-large-2-cls-nlisim
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: Ein Mann spricht.
  sentences:
  - Ein Mann spricht in ein Mikrofon.
  - Der Mann spielt auf den Tastaturen.
  - Zwei Mädchen gehen im Ozean spazieren.
- source_sentence: Eine Flagge weht.
  sentences:
  - Die Flagge bewegte sich in der Luft.
  - Ein Hund fährt auf einem Skateboard.
  - Zwei Frauen sitzen in einem Cafe.
- source_sentence: Ein Mann übt Boxen
  sentences:
  - Ein Affe praktiziert Kampfsportarten.
  - Eine Person faltet ein Blatt Papier.
  - Eine Frau geht mit ihrem Hund spazieren.
- source_sentence: Das Tor ist gelb.
  sentences:
  - Das Tor ist blau.
  - Die Frau hält die Hände des Mannes.
  - NATO-Soldat bei afghanischem Angriff getötet
- source_sentence: Zwei Frauen laufen.
  sentences:
  - Frauen laufen.
  - Die Frau prüft die Augen des Mannes.
  - Ein Mann ist auf einem Dach
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on aari1995/gbert-large-2-cls-nlisim
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 1024
      type: sts-dev-1024
    metrics:
    - type: pearson_cosine
      value: 0.8417806877288009
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8452891310343582
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8418749526406495
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8450348906331776
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8422615095001257
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8453390990427703
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8416625079549063
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8450616171323844
      name: Spearman Dot
    - type: pearson_max
      value: 0.8422615095001257
      name: Pearson Max
    - type: spearman_max
      value: 0.8453390990427703
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 768
      type: sts-dev-768
    metrics:
    - type: pearson_cosine
      value: 0.8418107096367227
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8453863409322975
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8418527770289471
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8448328869253576
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8422791953749277
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8451547857394669
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8417682812591724
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8446927200809794
      name: Spearman Dot
    - type: pearson_max
      value: 0.8422791953749277
      name: Pearson Max
    - type: spearman_max
      value: 0.8453863409322975
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 512
      type: sts-dev-512
    metrics:
    - type: pearson_cosine
      value: 0.8394808864309438
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8437551103291275
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8420246416513741
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8447335398769396
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8422722079216611
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8448909261141044
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8358204287638725
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8380004733308642
      name: Spearman Dot
    - type: pearson_max
      value: 0.8422722079216611
      name: Pearson Max
    - type: spearman_max
      value: 0.8448909261141044
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 256
      type: sts-dev-256
    metrics:
    - type: pearson_cosine
      value: 0.833879413726309
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8392439788855341
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8379618268497928
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.839860826315925
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.838931461279174
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8404811150299943
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8230557648139373
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8242532718299653
      name: Spearman Dot
    - type: pearson_max
      value: 0.838931461279174
      name: Pearson Max
    - type: spearman_max
      value: 0.8404811150299943
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 128
      type: sts-dev-128
    metrics:
    - type: pearson_cosine
      value: 0.8253967606033702
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8335750690073012
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8341588626988476
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8343994326050966
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8355263623880292
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8358857095028451
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8035163216908426
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8050271037746011
      name: Spearman Dot
    - type: pearson_max
      value: 0.8355263623880292
      name: Pearson Max
    - type: spearman_max
      value: 0.8358857095028451
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts dev 64
      type: sts-dev-64
    metrics:
    - type: pearson_cosine
      value: 0.8150661334039712
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8265558538619309
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8241988539394505
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8238763145175863
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8274925218859535
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8270778062044848
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7773847317840161
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7790338242936304
      name: Spearman Dot
    - type: pearson_max
      value: 0.8274925218859535
      name: Pearson Max
    - type: spearman_max
      value: 0.8270778062044848
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 1024
      type: sts-test-1024
    metrics:
    - type: pearson_cosine
      value: 0.8130772714952826
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8188901246173036
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8208715312691268
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8195095089412118
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.820344720619671
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8189263018901494
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8127924456922464
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8185815083131535
      name: Spearman Dot
    - type: pearson_max
      value: 0.8208715312691268
      name: Pearson Max
    - type: spearman_max
      value: 0.8195095089412118
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 768
      type: sts-test-768
    metrics:
    - type: pearson_cosine
      value: 0.8121757739236393
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8182913347635533
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.820604714791802
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8190481839997107
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8197462057663948
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8183157116237637
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8106698462984598
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8148932181769889
      name: Spearman Dot
    - type: pearson_max
      value: 0.820604714791802
      name: Pearson Max
    - type: spearman_max
      value: 0.8190481839997107
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 512
      type: sts-test-512
    metrics:
    - type: pearson_cosine
      value: 0.8096452235754106
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.816264314810491
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8180021560255247
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8165486306356095
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8173829404008947
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8158592878546184
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.8059176831913651
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8088972406630007
      name: Spearman Dot
    - type: pearson_max
      value: 0.8180021560255247
      name: Pearson Max
    - type: spearman_max
      value: 0.8165486306356095
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 256
      type: sts-test-256
    metrics:
    - type: pearson_cosine
      value: 0.8070921035712145
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8150266310280979
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.818409081545237
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8167245415653657
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8176811220335696
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8158894222194816
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.795483328805793
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7956062163122977
      name: Spearman Dot
    - type: pearson_max
      value: 0.818409081545237
      name: Pearson Max
    - type: spearman_max
      value: 0.8167245415653657
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 128
      type: sts-test-128
    metrics:
    - type: pearson_cosine
      value: 0.7974039089035316
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8093067652791092
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8125792968401813
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8121486514324944
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8119102513178551
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.811152531425261
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.7739555890021923
      name: Pearson Dot
    - type: spearman_dot
      value: 0.770072655568691
      name: Spearman Dot
    - type: pearson_max
      value: 0.8125792968401813
      name: Pearson Max
    - type: spearman_max
      value: 0.8121486514324944
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: sts test 64
      type: sts-test-64
    metrics:
    - type: pearson_cosine
      value: 0.7873069617689994
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8024994399645912
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8048161563115213
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8031972835914969
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8060416893207731
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8041515980374414
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.747911221220991
      name: Pearson Dot
    - type: spearman_dot
      value: 0.7386011869481828
      name: Spearman Dot
    - type: pearson_max
      value: 0.8060416893207731
      name: Pearson Max
    - type: spearman_max
      value: 0.8041515980374414
      name: Spearman Max
---

# SentenceTransformer based on aari1995/gbert-large-2-cls-nlisim

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [aari1995/gbert-large-2-cls-nlisim](https://huggingface.co/aari1995/gbert-large-2-cls-nlisim) 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-2-cls-nlisim](https://huggingface.co/aari1995/gbert-large-2-cls-nlisim) <!-- at revision fb515aefe7a575165dcaa62db3f77a09642ebe64 -->
- **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': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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/gbert-large-2-cls-pawsx-nli-sts")
# Run inference
sentences = [
    'Zwei Frauen laufen.',
    'Frauen laufen.',
    'Die Frau prüft die Augen des Mannes.',
]
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      | 0.8418     |
| **spearman_cosine** | **0.8453** |
| pearson_manhattan   | 0.8419     |
| spearman_manhattan  | 0.845      |
| pearson_euclidean   | 0.8423     |
| spearman_euclidean  | 0.8453     |
| pearson_dot         | 0.8417     |
| spearman_dot        | 0.8451     |
| pearson_max         | 0.8423     |
| spearman_max        | 0.8453     |

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

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8418     |
| **spearman_cosine** | **0.8454** |
| pearson_manhattan   | 0.8419     |
| spearman_manhattan  | 0.8448     |
| pearson_euclidean   | 0.8423     |
| spearman_euclidean  | 0.8452     |
| pearson_dot         | 0.8418     |
| spearman_dot        | 0.8447     |
| pearson_max         | 0.8423     |
| spearman_max        | 0.8454     |

#### 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      | 0.8395     |
| **spearman_cosine** | **0.8438** |
| pearson_manhattan   | 0.842      |
| spearman_manhattan  | 0.8447     |
| pearson_euclidean   | 0.8423     |
| spearman_euclidean  | 0.8449     |
| pearson_dot         | 0.8358     |
| spearman_dot        | 0.838      |
| pearson_max         | 0.8423     |
| spearman_max        | 0.8449     |

#### 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      | 0.8339     |
| **spearman_cosine** | **0.8392** |
| pearson_manhattan   | 0.838      |
| spearman_manhattan  | 0.8399     |
| pearson_euclidean   | 0.8389     |
| spearman_euclidean  | 0.8405     |
| pearson_dot         | 0.8231     |
| spearman_dot        | 0.8243     |
| pearson_max         | 0.8389     |
| spearman_max        | 0.8405     |

#### 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      | 0.8254     |
| **spearman_cosine** | **0.8336** |
| pearson_manhattan   | 0.8342     |
| spearman_manhattan  | 0.8344     |
| pearson_euclidean   | 0.8355     |
| spearman_euclidean  | 0.8359     |
| pearson_dot         | 0.8035     |
| spearman_dot        | 0.805      |
| pearson_max         | 0.8355     |
| spearman_max        | 0.8359     |

#### 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      | 0.8151     |
| **spearman_cosine** | **0.8266** |
| pearson_manhattan   | 0.8242     |
| spearman_manhattan  | 0.8239     |
| pearson_euclidean   | 0.8275     |
| spearman_euclidean  | 0.8271     |
| pearson_dot         | 0.7774     |
| spearman_dot        | 0.779      |
| pearson_max         | 0.8275     |
| spearman_max        | 0.8271     |

#### 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      | 0.8131     |
| **spearman_cosine** | **0.8189** |
| pearson_manhattan   | 0.8209     |
| spearman_manhattan  | 0.8195     |
| pearson_euclidean   | 0.8203     |
| spearman_euclidean  | 0.8189     |
| pearson_dot         | 0.8128     |
| spearman_dot        | 0.8186     |
| pearson_max         | 0.8209     |
| spearman_max        | 0.8195     |

#### 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      | 0.8122     |
| **spearman_cosine** | **0.8183** |
| pearson_manhattan   | 0.8206     |
| spearman_manhattan  | 0.819      |
| pearson_euclidean   | 0.8197     |
| spearman_euclidean  | 0.8183     |
| pearson_dot         | 0.8107     |
| spearman_dot        | 0.8149     |
| pearson_max         | 0.8206     |
| spearman_max        | 0.819      |

#### 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      | 0.8096     |
| **spearman_cosine** | **0.8163** |
| pearson_manhattan   | 0.818      |
| spearman_manhattan  | 0.8165     |
| pearson_euclidean   | 0.8174     |
| spearman_euclidean  | 0.8159     |
| pearson_dot         | 0.8059     |
| spearman_dot        | 0.8089     |
| pearson_max         | 0.818      |
| spearman_max        | 0.8165     |

#### 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      | 0.8071    |
| **spearman_cosine** | **0.815** |
| pearson_manhattan   | 0.8184    |
| spearman_manhattan  | 0.8167    |
| pearson_euclidean   | 0.8177    |
| spearman_euclidean  | 0.8159    |
| pearson_dot         | 0.7955    |
| spearman_dot        | 0.7956    |
| pearson_max         | 0.8184    |
| spearman_max        | 0.8167    |

#### 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      | 0.7974     |
| **spearman_cosine** | **0.8093** |
| pearson_manhattan   | 0.8126     |
| spearman_manhattan  | 0.8121     |
| pearson_euclidean   | 0.8119     |
| spearman_euclidean  | 0.8112     |
| pearson_dot         | 0.774      |
| spearman_dot        | 0.7701     |
| pearson_max         | 0.8126     |
| spearman_max        | 0.8121     |

#### 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      | 0.7873     |
| **spearman_cosine** | **0.8025** |
| pearson_manhattan   | 0.8048     |
| spearman_manhattan  | 0.8032     |
| pearson_euclidean   | 0.806      |
| spearman_euclidean  | 0.8042     |
| pearson_dot         | 0.7479     |
| spearman_dot        | 0.7386     |
| pearson_max         | 0.806      |
| spearman_max        | 0.8042     |

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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": "CosineSimilarityLoss",
      "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": "CosineSimilarityLoss",
      "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
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 16
- `learning_rate`: 5e-06
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True

#### 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`: 4
- `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
- `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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| 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 |
|:------:|:----:|:-------------:|:------:|:----------------------------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:|
| 0.0174 | 100  | 0.2958        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.0348 | 200  | 0.2914        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.0522 | 300  | 0.2691        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.0696 | 400  | 0.253         | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.0870 | 500  | 0.2458        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1044 | 600  | 0.2594        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1218 | 700  | 0.2339        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1392 | 800  | 0.2245        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1565 | 900  | 0.2122        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1739 | 1000 | 0.2369        | 0.2394 | 0.8402                       | 0.8277                      | 0.8352                      | 0.8393                      | 0.8164                     | 0.8404                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.1913 | 1100 | 0.2308        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2087 | 1200 | 0.2292        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2261 | 1300 | 0.2232        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2435 | 1400 | 0.2001        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2609 | 1500 | 0.2139        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2783 | 1600 | 0.1906        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.2957 | 1700 | 0.1895        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3131 | 1800 | 0.2011        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3305 | 1900 | 0.1723        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3479 | 2000 | 0.1886        | 0.2340 | 0.8448                       | 0.8321                      | 0.8385                      | 0.8435                      | 0.8233                     | 0.8449                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3653 | 2100 | 0.1719        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.3827 | 2200 | 0.1879        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4001 | 2300 | 0.187         | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4175 | 2400 | 0.1487        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4349 | 2500 | 0.1752        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4523 | 2600 | 0.1475        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4696 | 2700 | 0.1695        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.4870 | 2800 | 0.1615        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5044 | 2900 | 0.1558        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5218 | 3000 | 0.1713        | 0.2357 | 0.8457                       | 0.8344                      | 0.8406                      | 0.8447                      | 0.8266                     | 0.8461                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5392 | 3100 | 0.1556        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5566 | 3200 | 0.1743        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5740 | 3300 | 0.1426        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.5914 | 3400 | 0.1519        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6088 | 3500 | 0.1763        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6262 | 3600 | 0.1456        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6436 | 3700 | 0.1649        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6610 | 3800 | 0.1427        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6784 | 3900 | 0.1284        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.6958 | 4000 | 0.1533        | 0.2344 | 0.8417                       | 0.8291                      | 0.8357                      | 0.8402                      | 0.8225                     | 0.8421                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.7132 | 4100 | 0.1397        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.7306 | 4200 | 0.1505        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.7480 | 4300 | 0.1355        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.7654 | 4400 | 0.1275        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.7827 | 4500 | 0.1599        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8001 | 4600 | 0.1493        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8175 | 4700 | 0.1497        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8349 | 4800 | 0.1492        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8523 | 4900 | 0.1378        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8697 | 5000 | 0.1391        | 0.2362 | 0.8453                       | 0.8336                      | 0.8392                      | 0.8438                      | 0.8266                     | 0.8454                      | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.8871 | 5100 | 0.1622        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9045 | 5200 | 0.1456        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9219 | 5300 | 0.1367        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9393 | 5400 | 0.1243        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9567 | 5500 | 0.1389        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9741 | 5600 | 0.1338        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 0.9915 | 5700 | 0.1146        | -      | -                            | -                           | -                           | -                           | -                          | -                           | -                             | -                            | -                            | -                            | -                           | -                            |
| 1.0    | 5749 | -             | -      | -                            | -                           | -                           | -                           | -                          | -                           | 0.8189                        | 0.8093                       | 0.8150                       | 0.8163                       | 0.8025                      | 0.8183                       |


### 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}
}
```

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