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large_directFourEpoch_maxPooling_stsModel

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:51741
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
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---
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:51741
- loss:CoSENTLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Starsza para azjatycka pozuje z noworodkiem przy stole obiadowym.
 sentences:
 - Koszykarz ma zamiar zdobyć punkty dla swojej drużyny.
 - Grupa starszych osób pozuje wokół stołu w jadalni.
 - Możliwe, że układ słoneczny taki jak nasz może istnieć poza galaktyką.
- source_sentence: Englisch arbeitet überall mit Menschen, die Dinge kaufen und verkaufen,
 und in der Gastfreundschaft und im Tourismusgeschäft.
 sentences:
 - Ich bin in Maharashtra (einschließlich Mumbai) und Andhra Pradesh herumgereist,
 und ich hatte kein Problem damit, nur mit Englisch auszukommen.
 - 'Ein griechischsprachiger Sklave (δούλος, doulos) würde seinen Herrn, glaube ich,
 κύριος nennen (translit: kurios; Herr, Herr, Herr, Herr; Vokativform: κύριε).'
 - Das Paar lag auf dem Bett.
- source_sentence: Si vous vous comprenez et comprenez votre ennemi, vous aurez beaucoup
 plus de chances de gagner n'importe quelle bataille.
 sentences:
 - 'Outre les probabilités de gagner une bataille théorique, cette citation a une
 autre signification : l''importance de connaître/comprendre les autres.'
 - Une femme et un chien se promènent ensemble.
 - Un homme joue de la guitare.
- source_sentence: Un homme joue de la harpe.
 sentences:
 - Une femme joue de la guitare.
 - une femme a un enfant.
 - Un groupe de personnes est debout et assis sur le sol la nuit.
- source_sentence: Dois cães a lutar na neve.
 sentences:
 - Dois cães brincam na neve.
 - Pode sempre perguntar, então é a escolha do autor a aceitar ou não.
 - Um gato está a caminhar sobre chão de madeira dura.
datasets:
- PhilipMay/stsb_multi_mt
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
 results:
 - task:
 type: semantic-similarity
 name: Semantic Similarity
 dataset:
 name: sts eval
 type: sts-eval
 metrics:
 - type: pearson_cosine
 value: 0.8253368468984889
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.846759233048935
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8299744736557623
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8520861599655403
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8256016069231242
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8492129628424273
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8255197501712864
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8487854416277784
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8260573564720586
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8478611313629719
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8254630619077544
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8478863939561875
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8252961253716439
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8499454503012575
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8239479448794885
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8442906623766797
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8278545449307315
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8527526772189048
 name: Spearman Cosine
 - task:
 type: semantic-similarity
 name: Semantic Similarity
 dataset:
 name: sts test
 type: sts-test
 metrics:
 - type: pearson_cosine
 value: 0.7359219780078147
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.7333779772204967
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.6902858725875743
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.6846586782470231
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.7678591853836273
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.7639074219836824
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8169782930257612
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8177729106856704
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.6833019591754349
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.6919928881318896
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.7631519370867645
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.7647963195113389
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.8258066541444342
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.8190422019612702
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.7539006536391688
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.7554299404462984
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.7696930212849677
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.7667219735306458
 name: Spearman Cosine
 - type: pearson_cosine
 value: 0.7616719432466488
 name: Pearson Cosine
 - type: spearman_cosine
 value: 0.7669487674957793
 name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) and [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 84fccfe766bcfd679e39efefe4ebf45af190ad2d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
 - [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
 - [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- **Languages:** de, en, es, fr, it, nl, pl, pt, ru, zh
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
 (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): MultiHeadGeneralizedPooling()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RomainDarous/large_directFourEpoch_maxPooling_stsModel")
# Run inference
sentences = [
 'Dois cães a lutar na neve.',
 'Dois cães brincam na neve.',
 'Pode sempre perguntar, então é a escolha do autor a aceitar ou não.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### 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
* Datasets: `sts-eval`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-eval | sts-test |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.8253 | 0.7617 |
| **spearman_cosine** | **0.8468** | **0.7669** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.83 |
| **spearman_cosine** | **0.8521** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8256 |
| **spearman_cosine** | **0.8492** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8255 |
| **spearman_cosine** | **0.8488** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8261 |
| **spearman_cosine** | **0.8479** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8255 |
| **spearman_cosine** | **0.8479** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8253 |
| **spearman_cosine** | **0.8499** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8239 |
| **spearman_cosine** | **0.8443** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8279 |
| **spearman_cosine** | **0.8528** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Datasets
<details><summary>multi_stsb_de</summary>
#### multi_stsb_de
* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 5 tokens</li><li>mean: 11.58 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.53 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:---------------------------------------------------------------|:--------------------------------------------------------------------------|:--------------------------------|
 | <code>Ein Flugzeug hebt gerade ab.</code> | <code>Ein Flugzeug hebt gerade ab.</code> | <code>1.0</code> |
 | <code>Ein Mann spielt eine große Flöte.</code> | <code>Ein Mann spielt eine Flöte.</code> | <code>0.7599999904632568</code> |
 | <code>Ein Mann streicht geriebenen Käse auf eine Pizza.</code> | <code>Ein Mann streicht geriebenen Käse auf eine ungekochte Pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_es</summary>
#### multi_stsb_es
* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 7 tokens</li><li>mean: 12.21 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.07 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:----------------------------------------------------------------|:----------------------------------------------------------------------|:--------------------------------|
 | <code>Un avión está despegando.</code> | <code>Un avión está despegando.</code> | <code>1.0</code> |
 | <code>Un hombre está tocando una gran flauta.</code> | <code>Un hombre está tocando una flauta.</code> | <code>0.7599999904632568</code> |
 | <code>Un hombre está untando queso rallado en una pizza.</code> | <code>Un hombre está untando queso rallado en una pizza cruda.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_fr</summary>
#### multi_stsb_fr
* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 12.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.49 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:-----------------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
 | <code>Un avion est en train de décoller.</code> | <code>Un avion est en train de décoller.</code> | <code>1.0</code> |
 | <code>Un homme joue d'une grande flûte.</code> | <code>Un homme joue de la flûte.</code> | <code>0.7599999904632568</code> |
 | <code>Un homme étale du fromage râpé sur une pizza.</code> | <code>Un homme étale du fromage râpé sur une pizza non cuite.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_it</summary>
#### multi_stsb_it
* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 7 tokens</li><li>mean: 12.77 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.69 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:--------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------|
 | <code>Un aereo sta decollando.</code> | <code>Un aereo sta decollando.</code> | <code>1.0</code> |
 | <code>Un uomo sta suonando un grande flauto.</code> | <code>Un uomo sta suonando un flauto.</code> | <code>0.7599999904632568</code> |
 | <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza.</code> | <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza non cotta.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_nl</summary>
#### multi_stsb_nl
* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 11.67 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.55 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:--------------------------------------------------------|:--------------------------------------------------------------------|:--------------------------------|
 | <code>Er gaat een vliegtuig opstijgen.</code> | <code>Er gaat een vliegtuig opstijgen.</code> | <code>1.0</code> |
 | <code>Een man speelt een grote fluit.</code> | <code>Een man speelt fluit.</code> | <code>0.7599999904632568</code> |
 | <code>Een man smeert geraspte kaas op een pizza.</code> | <code>Een man strooit geraspte kaas op een ongekookte pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_pl</summary>
#### multi_stsb_pl
* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 5 tokens</li><li>mean: 12.2 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.11 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:-----------------------------------------------------------|:------------------------------------------------------------------------|:--------------------------------|
 | <code>Samolot wystartował.</code> | <code>Samolot wystartował.</code> | <code>1.0</code> |
 | <code>Człowiek gra na dużym flecie.</code> | <code>Człowiek gra na flecie.</code> | <code>0.7599999904632568</code> |
 | <code>Mężczyzna rozsiewa na pizzy rozdrobniony ser.</code> | <code>Mężczyzna rozsiewa rozdrobniony ser na niegotowanej pizzy.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_pt</summary>
#### multi_stsb_pt
* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 7 tokens</li><li>mean: 12.33 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.29 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------|
 | <code>Um avião está a descolar.</code> | <code>Um avião aéreo está a descolar.</code> | <code>1.0</code> |
 | <code>Um homem está a tocar uma grande flauta.</code> | <code>Um homem está a tocar uma flauta.</code> | <code>0.7599999904632568</code> |
 | <code>Um homem está a espalhar queijo desfiado numa pizza.</code> | <code>Um homem está a espalhar queijo desfiado sobre uma pizza não cozida.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_ru</summary>
#### multi_stsb_ru
* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 5 tokens</li><li>mean: 11.19 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.17 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
 | <code>Самолет взлетает.</code> | <code>Взлетает самолет.</code> | <code>1.0</code> |
 | <code>Человек играет на большой флейте.</code> | <code>Человек играет на флейте.</code> | <code>0.7599999904632568</code> |
 | <code>Мужчина разбрасывает сыр на пиццу.</code> | <code>Мужчина разбрасывает измельченный сыр на вареную пиццу.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_zh</summary>
#### multi_stsb_zh
* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 10.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.79 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:------------------------------|:----------------------------------|:--------------------------------|
 | <code>一架飞机正在起飞。</code> | <code>一架飞机正在起飞。</code> | <code>1.0</code> |
 | <code>一个男人正在吹一支大笛子。</code> | <code>一个人在吹笛子。</code> | <code>0.7599999904632568</code> |
 | <code>一名男子正在比萨饼上涂抹奶酪丝。</code> | <code>一名男子正在将奶酪丝涂抹在未熟的披萨上。</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
### Evaluation Datasets
<details><summary>multi_stsb_de</summary>
#### multi_stsb_de
* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 5 tokens</li><li>mean: 18.25 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:-------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
 | <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>1.0</code> |
 | <code>Ein kleines Kind reitet auf einem Pferd.</code> | <code>Ein Kind reitet auf einem Pferd.</code> | <code>0.949999988079071</code> |
 | <code>Ein Mann verfüttert eine Maus an eine Schlange.</code> | <code>Der Mann füttert die Schlange mit einer Maus.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_es</summary>
#### multi_stsb_es
* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 7 tokens</li><li>mean: 17.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.86 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:----------------------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------|
 | <code>Un hombre con un casco está bailando.</code> | <code>Un hombre con un casco está bailando.</code> | <code>1.0</code> |
 | <code>Un niño pequeño está montando a caballo.</code> | <code>Un niño está montando a caballo.</code> | <code>0.949999988079071</code> |
 | <code>Un hombre está alimentando a una serpiente con un ratón.</code> | <code>El hombre está alimentando a la serpiente con un ratón.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_fr</summary>
#### multi_stsb_fr
* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 19.7 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.65 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-------------------------------|
 | <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>1.0</code> |
 | <code>Un jeune enfant monte à cheval.</code> | <code>Un enfant monte à cheval.</code> | <code>0.949999988079071</code> |
 | <code>Un homme donne une souris à un serpent.</code> | <code>L'homme donne une souris au serpent.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_it</summary>
#### multi_stsb_it
* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 18.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.43 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:------------------------------------------------------------------|:---------------------------------------------------------------|:-------------------------------|
 | <code>Un uomo con l'elmetto sta ballando.</code> | <code>Un uomo che indossa un elmetto sta ballando.</code> | <code>1.0</code> |
 | <code>Un bambino piccolo sta cavalcando un cavallo.</code> | <code>Un bambino sta cavalcando un cavallo.</code> | <code>0.949999988079071</code> |
 | <code>Un uomo sta dando da mangiare un topo a un serpente.</code> | <code>L'uomo sta dando da mangiare un topo al serpente.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_nl</summary>
#### multi_stsb_nl
* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 5 tokens</li><li>mean: 17.88 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.71 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:-----------------------------------------------------|:-----------------------------------------------------|:-------------------------------|
 | <code>Een man met een helm is aan het dansen.</code> | <code>Een man met een helm is aan het dansen.</code> | <code>1.0</code> |
 | <code>Een jong kind rijdt op een paard.</code> | <code>Een kind rijdt op een paard.</code> | <code>0.949999988079071</code> |
 | <code>Een man voedt een muis aan een slang.</code> | <code>De man voert een muis aan de slang.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_pl</summary>
#### multi_stsb_pl
* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.43 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:---------------------------------------------------|:---------------------------------------------------|:-------------------------------|
 | <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>1.0</code> |
 | <code>Małe dziecko jedzie na koniu.</code> | <code>Dziecko jedzie na koniu.</code> | <code>0.949999988079071</code> |
 | <code>Człowiek karmi węża myszką.</code> | <code>Ten człowiek karmi węża myszką.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_pt</summary>
#### multi_stsb_pt
* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 7 tokens</li><li>mean: 18.22 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.11 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
 | <code>Um homem de chapéu duro está a dançar.</code> | <code>Um homem com um capacete está a dançar.</code> | <code>1.0</code> |
 | <code>Uma criança pequena está a montar a cavalo.</code> | <code>Uma criança está a montar a cavalo.</code> | <code>0.949999988079071</code> |
 | <code>Um homem está a alimentar um rato a uma cobra.</code> | <code>O homem está a alimentar a cobra com um rato.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_ru</summary>
#### multi_stsb_ru
* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 17.92 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.75 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:------------------------------------------------------|:----------------------------------------------|:-------------------------------|
 | <code>Человек в твердой шляпе танцует.</code> | <code>Мужчина в твердой шляпе танцует.</code> | <code>1.0</code> |
 | <code>Маленький ребенок едет верхом на лошади.</code> | <code>Ребенок едет на лошади.</code> | <code>0.949999988079071</code> |
 | <code>Мужчина кормит мышь змее.</code> | <code>Человек кормит змею мышью.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
<details><summary>multi_stsb_zh</summary>
#### multi_stsb_zh
* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
 | | sentence1 | sentence2 | score |
 |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
 | type | string | string | float |
 | details | <ul><li>min: 6 tokens</li><li>mean: 15.37 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.24 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
 | sentence1 | sentence2 | score |
 |:---------------------------|:--------------------------|:-------------------------------|
 | <code>一个戴着硬帽子的人在跳舞。</code> | <code>一个戴着硬帽的人在跳舞。</code> | <code>1.0</code> |
 | <code>一个小孩子在骑马。</code> | <code>一个孩子在骑马。</code> | <code>0.949999988079071</code> |
 | <code>一个人正在用老鼠喂蛇。</code> | <code>那人正在给蛇喂老鼠。</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
 ```json
 {
 "scale": 20.0,
 "similarity_fct": "pairwise_cos_sim"
 }
 ```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 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`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | multi stsb de loss | multi stsb es loss | multi stsb fr loss | multi stsb it loss | multi stsb nl loss | multi stsb pl loss | multi stsb pt loss | multi stsb ru loss | multi stsb zh loss | sts-eval_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:-----:|:-------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------------:|:------------------------:|
| 4.0 | 12960 | 3.6699 | 6.7790 | 6.7773 | 6.8239 | 6.9079 | 6.9186 | 6.7028 | 6.7280 | 6.7424 | 6.4329 | 0.8528 | - |
| -1 | -1 | - | - | - | - | - | - | - | - | - | - | - | 0.7669 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.3.0
- Datasets: 2.16.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
 author = "Reimers, Nils and Gurevych, Iryna",
 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
 month = "11",
 year = "2019",
 publisher = "Association for Computational Linguistics",
 url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
 title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
 author={Su Jianlin},
 year={2022},
 month={Jan},
 url={https://kexue.fm/archives/8847},
}
```
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