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large_directOneEpoch_additivePooling_noisedInit_stsModel

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:51741
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
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large_directOneEpoch_additivePooling_noisedInit_stsModel / README.md
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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 test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.770265917448893
name: Pearson Cosine
- type: spearman_cosine
value: 0.7695753611202234
name: Spearman Cosine
- type: pearson_cosine
value: 0.743314147313189
name: Pearson Cosine
- type: spearman_cosine
value: 0.7439229969799834
name: Spearman Cosine
- type: pearson_cosine
value: 0.7899000778817052
name: Pearson Cosine
- type: spearman_cosine
value: 0.7919879744361831
name: Spearman Cosine
- type: pearson_cosine
value: 0.8421382425963325
name: Pearson Cosine
- type: spearman_cosine
value: 0.8446545302953714
name: Spearman Cosine
- type: pearson_cosine
value: 0.7276779508630199
name: Pearson Cosine
- type: spearman_cosine
value: 0.7341312749002586
name: Spearman Cosine
- type: pearson_cosine
value: 0.8028078127170786
name: Pearson Cosine
- type: spearman_cosine
value: 0.8128598816467064
name: Spearman Cosine
- type: pearson_cosine
value: 0.8526436146515345
name: Pearson Cosine
- type: spearman_cosine
value: 0.8537073081826633
name: Spearman Cosine
- type: pearson_cosine
value: 0.7808414032955799
name: Pearson Cosine
- type: spearman_cosine
value: 0.7762953696351416
name: Spearman Cosine
- type: pearson_cosine
value: 0.7980009468506776
name: Pearson Cosine
- type: spearman_cosine
value: 0.8013833949375317
name: Spearman Cosine
- type: pearson_cosine
value: 0.7984564144977082
name: Pearson Cosine
- type: spearman_cosine
value: 0.7930873485722544
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(
(P): ModuleList(
(0-7): 8 x Linear(in_features=768, out_features=96, bias=True)
)
(W1): ModuleList(
(0-7): 8 x Linear(in_features=96, out_features=384, bias=True)
)
(W2): ModuleList(
(0-7): 8 x Linear(in_features=384, out_features=96, bias=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("RomainDarous/large_directOneEpoch_additivePooling_noisedInit_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
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7703 |
| **spearman_cosine** | **0.7696** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7433 |
| **spearman_cosine** | **0.7439** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.7899 |
| **spearman_cosine** | **0.792** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8421 |
| **spearman_cosine** | **0.8447** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7277 |
| **spearman_cosine** | **0.7341** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8028 |
| **spearman_cosine** | **0.8129** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8526 |
| **spearman_cosine** | **0.8537** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7808 |
| **spearman_cosine** | **0.7763** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.798 |
| **spearman_cosine** | **0.8014** |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.7985 |
| **spearman_cosine** | **0.7931** |
<!--
## 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.*
-->
## Training Details
### Training Datasets
<details><summary>multi_stsb_de</summary>
#### multi_stsb_de
* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.58 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.53 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------|:--------------------------------------------------------------------------|:--------------------------------|
| <code>Ein Flugzeug hebt gerade ab.</code> | <code>Ein Flugzeug hebt gerade ab.</code> | <code>1.0</code> |
| <code>Ein Mann spielt eine große Flöte.</code> | <code>Ein Mann spielt eine Flöte.</code> | <code>0.7599999904632568</code> |
| <code>Ein Mann streicht geriebenen Käse auf eine Pizza.</code> | <code>Ein Mann streicht geriebenen Käse auf eine ungekochte Pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_es</summary>
#### multi_stsb_es
* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 12.21 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.07 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------|:----------------------------------------------------------------------|:--------------------------------|
| <code>Un avión está despegando.</code> | <code>Un avión está despegando.</code> | <code>1.0</code> |
| <code>Un hombre está tocando una gran flauta.</code> | <code>Un hombre está tocando una flauta.</code> | <code>0.7599999904632568</code> |
| <code>Un hombre está untando queso rallado en una pizza.</code> | <code>Un hombre está untando queso rallado en una pizza cruda.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_fr</summary>
#### multi_stsb_fr
* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.49 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
| <code>Un avion est en train de décoller.</code> | <code>Un avion est en train de décoller.</code> | <code>1.0</code> |
| <code>Un homme joue d'une grande flûte.</code> | <code>Un homme joue de la flûte.</code> | <code>0.7599999904632568</code> |
| <code>Un homme étale du fromage râpé sur une pizza.</code> | <code>Un homme étale du fromage râpé sur une pizza non cuite.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_it</summary>
#### multi_stsb_it
* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 12.77 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.69 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------|
| <code>Un aereo sta decollando.</code> | <code>Un aereo sta decollando.</code> | <code>1.0</code> |
| <code>Un uomo sta suonando un grande flauto.</code> | <code>Un uomo sta suonando un flauto.</code> | <code>0.7599999904632568</code> |
| <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza.</code> | <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza non cotta.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_nl</summary>
#### multi_stsb_nl
* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 11.67 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.55 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------|:--------------------------------------------------------------------|:--------------------------------|
| <code>Er gaat een vliegtuig opstijgen.</code> | <code>Er gaat een vliegtuig opstijgen.</code> | <code>1.0</code> |
| <code>Een man speelt een grote fluit.</code> | <code>Een man speelt fluit.</code> | <code>0.7599999904632568</code> |
| <code>Een man smeert geraspte kaas op een pizza.</code> | <code>Een man strooit geraspte kaas op een ongekookte pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pl</summary>
#### multi_stsb_pl
* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 12.2 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.11 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:------------------------------------------------------------------------|:--------------------------------|
| <code>Samolot wystartował.</code> | <code>Samolot wystartował.</code> | <code>1.0</code> |
| <code>Człowiek gra na dużym flecie.</code> | <code>Człowiek gra na flecie.</code> | <code>0.7599999904632568</code> |
| <code>Mężczyzna rozsiewa na pizzy rozdrobniony ser.</code> | <code>Mężczyzna rozsiewa rozdrobniony ser na niegotowanej pizzy.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pt</summary>
#### multi_stsb_pt
* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 12.33 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.29 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------|
| <code>Um avião está a descolar.</code> | <code>Um avião aéreo está a descolar.</code> | <code>1.0</code> |
| <code>Um homem está a tocar uma grande flauta.</code> | <code>Um homem está a tocar uma flauta.</code> | <code>0.7599999904632568</code> |
| <code>Um homem está a espalhar queijo desfiado numa pizza.</code> | <code>Um homem está a espalhar queijo desfiado sobre uma pizza não cozida.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_ru</summary>
#### multi_stsb_ru
* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.19 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.17 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
| <code>Самолет взлетает.</code> | <code>Взлетает самолет.</code> | <code>1.0</code> |
| <code>Человек играет на большой флейте.</code> | <code>Человек играет на флейте.</code> | <code>0.7599999904632568</code> |
| <code>Мужчина разбрасывает сыр на пиццу.</code> | <code>Мужчина разбрасывает измельченный сыр на вареную пиццу.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_zh</summary>
#### multi_stsb_zh
* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.79 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------|:----------------------------------|:--------------------------------|
| <code>一架飞机正在起飞。</code> | <code>一架飞机正在起飞。</code> | <code>1.0</code> |
| <code>一个男人正在吹一支大笛子。</code> | <code>一个人在吹笛子。</code> | <code>0.7599999904632568</code> |
| <code>一名男子正在比萨饼上涂抹奶酪丝。</code> | <code>一名男子正在将奶酪丝涂抹在未熟的披萨上。</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
### Evaluation Datasets
<details><summary>multi_stsb_de</summary>
#### multi_stsb_de
* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 18.25 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
| <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>1.0</code> |
| <code>Ein kleines Kind reitet auf einem Pferd.</code> | <code>Ein Kind reitet auf einem Pferd.</code> | <code>0.949999988079071</code> |
| <code>Ein Mann verfüttert eine Maus an eine Schlange.</code> | <code>Der Mann füttert die Schlange mit einer Maus.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_es</summary>
#### multi_stsb_es
* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 17.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.86 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------|
| <code>Un hombre con un casco está bailando.</code> | <code>Un hombre con un casco está bailando.</code> | <code>1.0</code> |
| <code>Un niño pequeño está montando a caballo.</code> | <code>Un niño está montando a caballo.</code> | <code>0.949999988079071</code> |
| <code>Un hombre está alimentando a una serpiente con un ratón.</code> | <code>El hombre está alimentando a la serpiente con un ratón.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_fr</summary>
#### multi_stsb_fr
* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 19.7 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.65 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-------------------------------|
| <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>1.0</code> |
| <code>Un jeune enfant monte à cheval.</code> | <code>Un enfant monte à cheval.</code> | <code>0.949999988079071</code> |
| <code>Un homme donne une souris à un serpent.</code> | <code>L'homme donne une souris au serpent.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_it</summary>
#### multi_stsb_it
* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.43 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------|:---------------------------------------------------------------|:-------------------------------|
| <code>Un uomo con l'elmetto sta ballando.</code> | <code>Un uomo che indossa un elmetto sta ballando.</code> | <code>1.0</code> |
| <code>Un bambino piccolo sta cavalcando un cavallo.</code> | <code>Un bambino sta cavalcando un cavallo.</code> | <code>0.949999988079071</code> |
| <code>Un uomo sta dando da mangiare un topo a un serpente.</code> | <code>L'uomo sta dando da mangiare un topo al serpente.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_nl</summary>
#### multi_stsb_nl
* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 17.88 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.71 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------|:-----------------------------------------------------|:-------------------------------|
| <code>Een man met een helm is aan het dansen.</code> | <code>Een man met een helm is aan het dansen.</code> | <code>1.0</code> |
| <code>Een jong kind rijdt op een paard.</code> | <code>Een kind rijdt op een paard.</code> | <code>0.949999988079071</code> |
| <code>Een man voedt een muis aan een slang.</code> | <code>De man voert een muis aan de slang.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pl</summary>
#### multi_stsb_pl
* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.43 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------|:---------------------------------------------------|:-------------------------------|
| <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>1.0</code> |
| <code>Małe dziecko jedzie na koniu.</code> | <code>Dziecko jedzie na koniu.</code> | <code>0.949999988079071</code> |
| <code>Człowiek karmi węża myszką.</code> | <code>Ten człowiek karmi węża myszką.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pt</summary>
#### multi_stsb_pt
* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 18.22 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.11 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
| <code>Um homem de chapéu duro está a dançar.</code> | <code>Um homem com um capacete está a dançar.</code> | <code>1.0</code> |
| <code>Uma criança pequena está a montar a cavalo.</code> | <code>Uma criança está a montar a cavalo.</code> | <code>0.949999988079071</code> |
| <code>Um homem está a alimentar um rato a uma cobra.</code> | <code>O homem está a alimentar a cobra com um rato.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_ru</summary>
#### multi_stsb_ru
* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.92 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.75 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------|:----------------------------------------------|:-------------------------------|
| <code>Человек в твердой шляпе танцует.</code> | <code>Мужчина в твердой шляпе танцует.</code> | <code>1.0</code> |
| <code>Маленький ребенок едет верхом на лошади.</code> | <code>Ребенок едет на лошади.</code> | <code>0.949999988079071</code> |
| <code>Мужчина кормит мышь змее.</code> | <code>Человек кормит змею мышью.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_zh</summary>
#### multi_stsb_zh
* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.37 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.24 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------|:--------------------------|:-------------------------------|
| <code>一个戴着硬帽子的人在跳舞。</code> | <code>一个戴着硬帽的人在跳舞。</code> | <code>1.0</code> |
| <code>一个小孩子在骑马。</code> | <code>一个孩子在骑马。</code> | <code>0.949999988079071</code> |
| <code>一个人正在用老鼠喂蛇。</code> | <code>那人正在给蛇喂老鼠。</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | sts-test_spearman_cosine |
|:-----:|:----:|:------------------------:|
| -1 | -1 | 0.7931 |
### 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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