---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:311737
- loss:MSELoss
base_model: FacebookAI/xlm-roberta-base
datasets: []
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
widget:
- source_sentence: Taxation charge credit
sentences:
- Paskolu grazinimas
- sumazejimas
- Pelno mokescio sanaudos
- source_sentence: Current tax liabilities
sentences:
- Ecarts de conversion
- aux proprietaires de la societe mere
- Dettes dimpots
- source_sentence: purchase of intangible assets
sentences:
- Ativos intangiveis
- Financiamentos obtidos
- Ativos intangiveis
- source_sentence: Profit and total comprehensive income for the year attributable
to noncontrolling interests
sentences:
- Passivita finanziarie correnti
- Flusso di cassa generato assorbito dallattivita operativa
- Interessenze di pertinenza dei terzi
- source_sentence: Financial asset investments
sentences:
- Prevedbena rezerva
- activities
- Financne nalozbe
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on FacebookAI/xlm-roberta-base
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en fr
type: en-fr
metrics:
- type: negative_mse
value: -18.797919154167175
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en fr
type: en-fr
metrics:
- type: src2trg_accuracy
value: 0.002551963902655522
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.0021821140616909533
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.002367038982173238
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en fi
type: en-fi
metrics:
- type: negative_mse
value: -19.07900720834732
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en fi
type: en-fi
metrics:
- type: src2trg_accuracy
value: 0.005478404892342974
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.004841381067651931
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.005159892979997452
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en pl
type: en-pl
metrics:
- type: negative_mse
value: -18.932442367076874
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en pl
type: en-pl
metrics:
- type: src2trg_accuracy
value: 0.003107520198881293
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.0026931841723637872
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.00290035218562254
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en sv
type: en-sv
metrics:
- type: negative_mse
value: -19.032517075538635
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en sv
type: en-sv
metrics:
- type: src2trg_accuracy
value: 0.003710225128914602
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.003961765815620677
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.003835995472267639
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en de
type: en-de
metrics:
- type: negative_mse
value: -19.20013278722763
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en de
type: en-de
metrics:
- type: src2trg_accuracy
value: 0.002623321845584075
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.002726197212077568
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.0026747595288308217
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en it
type: en-it
metrics:
- type: negative_mse
value: -19.07709091901779
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en it
type: en-it
metrics:
- type: src2trg_accuracy
value: 0.003507843007478986
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.003640214441723476
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.003574028724601231
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en pt
type: en-pt
metrics:
- type: negative_mse
value: -19.00094896554947
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en pt
type: en-pt
metrics:
- type: src2trg_accuracy
value: 0.00842170929507174
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.008109794135995009
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.008265751715533374
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en no
type: en-no
metrics:
- type: negative_mse
value: -20.60515135526657
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en no
type: en-no
metrics:
- type: src2trg_accuracy
value: 0.011031797534068787
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.012329656067488644
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.011680726800778717
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en nb
type: en-nb
metrics:
- type: negative_mse
value: -20.6013485789299
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en nb
type: en-nb
metrics:
- type: src2trg_accuracy
value: 0.01270053475935829
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.01270053475935829
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.01270053475935829
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en de de
type: en-de-de
metrics:
- type: negative_mse
value: -20.8682119846344
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en de de
type: en-de-de
metrics:
- type: src2trg_accuracy
value: 0.028169014084507043
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.028169014084507043
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.028169014084507043
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en es
type: en-es
metrics:
- type: negative_mse
value: -18.843790888786316
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en es
type: en-es
metrics:
- type: src2trg_accuracy
value: 0.005086136177194421
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.004675963904840033
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.0048810500410172274
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en cs
type: en-cs
metrics:
- type: negative_mse
value: -19.128620624542236
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en cs
type: en-cs
metrics:
- type: src2trg_accuracy
value: 0.011185682326621925
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.0145413870246085
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.012863534675615212
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en nl
type: en-nl
metrics:
- type: negative_mse
value: -19.84833925962448
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en nl
type: en-nl
metrics:
- type: src2trg_accuracy
value: 0.00699912510936133
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.008165645960921552
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.00758238553514144
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en da
type: en-da
metrics:
- type: negative_mse
value: -19.38561350107193
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en da
type: en-da
metrics:
- type: src2trg_accuracy
value: 0.011572856391372961
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.01262493424513414
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.01209889531825355
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en lt
type: en-lt
metrics:
- type: negative_mse
value: -20.48500031232834
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en lt
type: en-lt
metrics:
- type: src2trg_accuracy
value: 0.010893246187363835
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.010893246187363835
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.010893246187363835
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en is
type: en-is
metrics:
- type: negative_mse
value: -19.216923415660858
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en is
type: en-is
metrics:
- type: src2trg_accuracy
value: 0.007246376811594203
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.009316770186335404
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.008281573498964804
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en sl
type: en-sl
metrics:
- type: negative_mse
value: -18.1530699133873
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en sl
type: en-sl
metrics:
- type: src2trg_accuracy
value: 0.011204481792717087
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.014005602240896359
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.012605042016806723
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en sv se
type: en-sv-se
metrics:
- type: negative_mse
value: -17.647552490234375
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en sv se
type: en-sv-se
metrics:
- type: src2trg_accuracy
value: 0.023376623376623377
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.02077922077922078
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.02207792207792208
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en fi fi
type: en-fi-fi
metrics:
- type: negative_mse
value: -19.282042980194092
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en fi fi
type: en-fi-fi
metrics:
- type: src2trg_accuracy
value: 0.017994858611825194
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.017994858611825194
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.017994858611825194
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en en gb
type: en-en-gb
metrics:
- type: negative_mse
value: -23.508824408054352
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en en gb
type: en-en-gb
metrics:
- type: src2trg_accuracy
value: 0.012552301255230125
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.016736401673640166
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.014644351464435146
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en lv
type: en-lv
metrics:
- type: negative_mse
value: -18.03768277168274
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en lv
type: en-lv
metrics:
- type: src2trg_accuracy
value: 0.004761904761904762
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.009523809523809525
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.0071428571428571435
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en el
type: en-el
metrics:
- type: negative_mse
value: -23.520667850971222
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en el
type: en-el
metrics:
- type: src2trg_accuracy
value: 0.05128205128205128
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.05128205128205128
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.05128205128205128
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en et
type: en-et
metrics:
- type: negative_mse
value: -17.514553666114807
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en et
type: en-et
metrics:
- type: src2trg_accuracy
value: 0.019230769230769232
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.019230769230769232
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.019230769230769232
name: Mean Accuracy
---
# SentenceTransformer based on FacebookAI/xlm-roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the en-fr, en-fi, en-pl, en-sv, en-de, en-it, en-pt, en-no, en-nb, en-de-de, en-es, en-cs, en-nl, en-da, en-lt, en-is, en-sl, en-sv-se, en-fi-fi, en-en-gb, en-lv, en-el and en-et 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:** [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- en-fr
- en-fi
- en-pl
- en-sv
- en-de
- en-it
- en-pt
- en-no
- en-nb
- en-de-de
- en-es
- en-cs
- en-nl
- en-da
- en-lt
- en-is
- en-sl
- en-sv-se
- en-fi-fi
- en-en-gb
- en-lv
- en-el
- en-et
### 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): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("slimaneMakh/student-multilang-XLMR-14jun")
# Run inference
sentences = [
'Financial asset investments',
'Financne nalozbe',
'activities',
]
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]
```
## Evaluation
### Metrics
#### Knowledge Distillation
* Dataset: `en-fr`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-18.7979** |
#### Translation
* Dataset: `en-fr`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0026 |
| trg2src_accuracy | 0.0022 |
| **mean_accuracy** | **0.0024** |
#### Knowledge Distillation
* Dataset: `en-fi`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-19.079** |
#### Translation
* Dataset: `en-fi`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0055 |
| trg2src_accuracy | 0.0048 |
| **mean_accuracy** | **0.0052** |
#### Knowledge Distillation
* Dataset: `en-pl`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-18.9324** |
#### Translation
* Dataset: `en-pl`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0031 |
| trg2src_accuracy | 0.0027 |
| **mean_accuracy** | **0.0029** |
#### Knowledge Distillation
* Dataset: `en-sv`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.0325** |
#### Translation
* Dataset: `en-sv`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0037 |
| trg2src_accuracy | 0.004 |
| **mean_accuracy** | **0.0038** |
#### Knowledge Distillation
* Dataset: `en-de`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.2001** |
#### Translation
* Dataset: `en-de`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0026 |
| trg2src_accuracy | 0.0027 |
| **mean_accuracy** | **0.0027** |
#### Knowledge Distillation
* Dataset: `en-it`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.0771** |
#### Translation
* Dataset: `en-it`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0035 |
| trg2src_accuracy | 0.0036 |
| **mean_accuracy** | **0.0036** |
#### Knowledge Distillation
* Dataset: `en-pt`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.0009** |
#### Translation
* Dataset: `en-pt`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0084 |
| trg2src_accuracy | 0.0081 |
| **mean_accuracy** | **0.0083** |
#### Knowledge Distillation
* Dataset: `en-no`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-20.6052** |
#### Translation
* Dataset: `en-no`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.011 |
| trg2src_accuracy | 0.0123 |
| **mean_accuracy** | **0.0117** |
#### Knowledge Distillation
* Dataset: `en-nb`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-20.6013** |
#### Translation
* Dataset: `en-nb`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0127 |
| trg2src_accuracy | 0.0127 |
| **mean_accuracy** | **0.0127** |
#### Knowledge Distillation
* Dataset: `en-de-de`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-20.8682** |
#### Translation
* Dataset: `en-de-de`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0282 |
| trg2src_accuracy | 0.0282 |
| **mean_accuracy** | **0.0282** |
#### Knowledge Distillation
* Dataset: `en-es`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-18.8438** |
#### Translation
* Dataset: `en-es`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0051 |
| trg2src_accuracy | 0.0047 |
| **mean_accuracy** | **0.0049** |
#### Knowledge Distillation
* Dataset: `en-cs`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.1286** |
#### Translation
* Dataset: `en-cs`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0112 |
| trg2src_accuracy | 0.0145 |
| **mean_accuracy** | **0.0129** |
#### Knowledge Distillation
* Dataset: `en-nl`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.8483** |
#### Translation
* Dataset: `en-nl`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.007 |
| trg2src_accuracy | 0.0082 |
| **mean_accuracy** | **0.0076** |
#### Knowledge Distillation
* Dataset: `en-da`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.3856** |
#### Translation
* Dataset: `en-da`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0116 |
| trg2src_accuracy | 0.0126 |
| **mean_accuracy** | **0.0121** |
#### Knowledge Distillation
* Dataset: `en-lt`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-20.485** |
#### Translation
* Dataset: `en-lt`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0109 |
| trg2src_accuracy | 0.0109 |
| **mean_accuracy** | **0.0109** |
#### Knowledge Distillation
* Dataset: `en-is`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-19.2169** |
#### Translation
* Dataset: `en-is`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0072 |
| trg2src_accuracy | 0.0093 |
| **mean_accuracy** | **0.0083** |
#### Knowledge Distillation
* Dataset: `en-sl`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-18.1531** |
#### Translation
* Dataset: `en-sl`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0112 |
| trg2src_accuracy | 0.014 |
| **mean_accuracy** | **0.0126** |
#### Knowledge Distillation
* Dataset: `en-sv-se`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-17.6476** |
#### Translation
* Dataset: `en-sv-se`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0234 |
| trg2src_accuracy | 0.0208 |
| **mean_accuracy** | **0.0221** |
#### Knowledge Distillation
* Dataset: `en-fi-fi`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-19.282** |
#### Translation
* Dataset: `en-fi-fi`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:----------|
| src2trg_accuracy | 0.018 |
| trg2src_accuracy | 0.018 |
| **mean_accuracy** | **0.018** |
#### Knowledge Distillation
* Dataset: `en-en-gb`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-23.5088** |
#### Translation
* Dataset: `en-en-gb`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0126 |
| trg2src_accuracy | 0.0167 |
| **mean_accuracy** | **0.0146** |
#### Knowledge Distillation
* Dataset: `en-lv`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-18.0377** |
#### Translation
* Dataset: `en-lv`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0048 |
| trg2src_accuracy | 0.0095 |
| **mean_accuracy** | **0.0071** |
#### Knowledge Distillation
* Dataset: `en-el`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-23.5207** |
#### Translation
* Dataset: `en-el`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0513 |
| trg2src_accuracy | 0.0513 |
| **mean_accuracy** | **0.0513** |
#### Knowledge Distillation
* Dataset: `en-et`
* Evaluated with [MSEEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:-------------|
| **negative_mse** | **-17.5146** |
#### Translation
* Dataset: `en-et`
* Evaluated with [TranslationEvaluator
](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.0192 |
| trg2src_accuracy | 0.0192 |
| **mean_accuracy** | **0.0192** |
## Training Details
### Training Datasets
#### en-fr
* Dataset: en-fr
* Size: 63,449 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details |
[-0.0459553524851799, 0.36456549167633057, 0.36365264654159546, 0.6452828645706177, -0.4019026756286621, ...]
| Net income for the period attributable to shareholders
| Resultat de lexercice
|
| [0.44971197843551636, 0.9621334075927734, -0.0879441499710083, -0.08917804807424545, 0.002839124295860529, ...]
| Podatek dochodowy
| Impots
|
| [0.3880807161331177, 0.19511738419532776, -0.13357722759246826, 0.25993096828460693, 0.0716109424829483, ...]
| AttributabletotheshareholdersofKvikabankihf
| aux actionnaires de la Societe
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-fi
* Dataset: en-fi
* Size: 18,428 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.24573877453804016, 0.5694760680198669, 0.45771917700767517, -0.13942377269268036, -0.2597014904022217, ...]
| Shareholders of Copenhagen Airports AS
| Emoyhtion osakkeenomistajille
|
| [0.5077632665634155, 0.8774086236953735, -0.3499397933483124, -0.6389203667640686, 0.026370976120233536, ...]
| Income tax benefit expense
| Income taxes
|
| [0.9414718747138977, -0.24161840975284576, 0.41289815306663513, 0.10003143548965454, -1.092337965965271, ...]
| Result
| Emoyrityksen osakkeenomistajille kuuluvasta tuloksesta laskettu
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-pl
* Dataset: en-pl
* Size: 45,054 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.09482160955667496, 0.7886450886726379, 0.23035818338394165, 0.21230120956897736, 0.33353161811828613, ...]
| Changes in deferred taxes directly recognized in other comprehensive income
| Podatek dochodowy dotyczacy innych calkowitych dochodow
|
| [-0.15856720507144928, 0.6147034168243408, -0.25085723400115967, -0.5494844913482666, -0.526219367980957, ...]
| Diluted from continuing operations
| Rozwodniony zysk strata na jedna akcje
|
| [-0.1696387380361557, -0.23339493572711945, -0.7045446038246155, -0.3721548914909363, -0.36909934878349304, ...]
| CASH FLOW RESULTING FROM OPERATING ACTIVITIES
| Srodki pieniezne netto z dzialalnosci operacyjnej
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-sv
* Dataset: en-sv
* Size: 37,354 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.2742433547973633, -0.4345971345901489, -0.28529638051986694, -0.06954757869243622, -1.822569489479065, ...]
| grupe
| moderbolagets aktieagare
|
| [0.04750566929578781, 0.2545453608036041, 0.3464582860469818, 0.22448834776878357, -0.0583755262196064, ...]
| Total comprehensive income for the year attributable to owners of the parent Company
| Moderbolagets aktieagare
|
| [0.045431576669216156, 0.3078455924987793, -0.06083355098962784, -0.5454118847846985, 0.5727013349533081, ...]
| Repayment of obligations under lease arrangements
| Amortering av skuld
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-de
* Dataset: en-de
* Size: 45,253 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.086859792470932, 0.7745860815048218, -0.08605925738811493, 0.37508440017700195, -0.9738988876342773, ...]
| adjustments of investments in subsidiaries
| Wahrungsumrechnungsdifferenzen
|
| [-0.05315065383911133, 0.0072781918570399284, -0.2516656517982483, -0.4747457504272461, -1.1008282899856567, ...]
| LOSS FROM CONTINUING OPERATIONS
| Ergebnis nach Ertragsteuern
|
| [0.14867287874221802, 1.0406593084335327, -0.17914682626724243, -0.6161922812461853, 0.14850790798664093, ...]
| Taxation paid received
| Ertragsteueraufwand ertrag
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-it
* Dataset: en-it
* Size: 34,682 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.5695832371711731, 0.02826128527522087, 0.1920386552810669, 0.40783414244651794, -1.2495031356811523, ...]
| Current financial receivables
| Titoli in portafoglio
|
| [0.662227988243103, 0.6725629568099976, 0.22833657264709473, 0.054810211062431335, -0.40215858817100525, ...]
| Proceeds from sale of assets
| Attivita destinate alla vendita
|
| [0.1357184797525406, 0.7814697623252869, 0.3390173614025116, -0.10204766690731049, -0.3055779039859772, ...]
| Profit before income tax
| Risultato netto
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-pt
* Dataset: en-pt
* Size: 7,300 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.008626206777989864, 0.6093286275863647, 0.08171450346708298, 1.162959337234497, 0.6411553025245667, ...]
| Interest received by the Barclays Bank Group was m
| Juros recebidos
|
| [-0.27057403326034546, 0.2500847578048706, -0.07353457063436508, 0.5000247955322266, -0.07040926814079285, ...]
| Other liabilities
| Outros passivos
|
| [-0.03809820115566254, 0.1842460036277771, -0.08849599212408066, -0.844947338104248, 0.7437804341316223, ...]
| Payment of obligations under leases
| Passivos de locacao
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-no
* Dataset: en-no
* Size: 3,602 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.19592446088790894, 0.5323967337608337, 0.21345381438732147, -0.4241628348827362, -0.0008733272552490234, ...]
| of the parent company
| income
|
| [-0.05730602145195007, 0.16925856471061707, -0.16081246733665466, -1.6013731956481934, 0.6432715654373169, ...]
| Employee charges and benefits expenses
| Personalkostnader
|
| [0.053435444831848145, -0.08411762863397598, 0.7841566801071167, 0.822182834148407, -0.3946605324745178, ...]
| in expected credit losses net
| totalresultat
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-nb
* Dataset: en-nb
* Size: 3,446 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.6152929663658142, 1.0328565835952759, -0.48867374658584595, 0.6196318864822388, -1.0412869453430176, ...]
| Note b
| Andre driftskostnader
|
| [-0.08955559879541397, 0.07031169533729553, -0.4530458450317383, 0.6429653763771057, -0.17220227420330048, ...]
| Profitloss for the period
| Resultat
|
| [-0.2092481404542923, 0.8907342553138733, -0.2213028073310852, 0.19046330451965332, 0.36781418323516846, ...]
| Tax on profitloss
| Skattekostnad
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-de-de
* Dataset: en-de-de
* Size: 623 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.15285512804985046, 0.24292221665382385, -0.21986141800880432, -0.12183597683906555, -0.8729998469352722, ...]
| Ikkekontrollerende eierinteresse
| davon den nicht beherrschenden Anteilen zuzurechnen
|
| [0.7105820178985596, 0.6940978765487671, 0.29005366563796997, 0.33401334285736084, 0.05582822486758232, ...]
| Total net revenue
| Umsatzerlose
|
| [-0.20316101610660553, 0.9045584797859192, -0.2203243523836136, -1.074849247932434, -0.4881342351436615, ...]
| Caixa e equivalentes de caixa
| Zahlungsmittel und Zahlungsmittelaquivalente
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-es
* Dataset: en-es
* Size: 28,719 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.172838494181633, 0.43473777174949646, 0.3958137333393097, 0.1424863040447235, -0.8349866271018982, ...]
| Increase in trade receivables and other assets
| Clientes y otras cuentas a cobrar
|
| [0.5418481826782227, 0.5917099714279175, 0.1668325960636139, 0.3066450357437134, -1.260878324508667, ...]
| Increase in trade and other receivables and advances paid
| Clientes y otras cuentas a cobrar
|
| [-0.2715812921524048, 0.05829544737935066, -0.4542696177959442, -0.029009468853473663, -0.7529364824295044, ...]
| Total Comprehensive Loss for the year wholly attributable to Equity Holders of the Parent Company
| Atribuible a la sociedad dominante
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-cs
* Dataset: en-cs
* Size: 2,203 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.06290890276432037, 0.5762706398963928, -0.024871770292520523, 0.22431252896785736, -0.6742631196975708, ...]
| Udzialy niekontrolujace
| Nekontrolnim podilum
|
| [0.39093080163002014, -0.009997962974011898, 0.24490250647068024, 0.9013416171073914, -0.796424388885498, ...]
| Profit for the year attributable to ordinary Shareholders
| Akcionarum materske spolecnosti
|
| [-0.23978163301944733, 0.484517902135849, -0.3151543438434601, 0.1443774700164795, -0.16455821692943573, ...]
| Avsetning for forpliktelser
| Rezervy
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-nl
* Dataset: en-nl
* Size: 8,101 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.43313074111938477, -0.23663929104804993, -0.0008638567524030805, 0.21914006769657135, -1.1042245626449585, ...]
| Shareholders of FGC UES
| Aandeelhouders van de moedermaatschappij
|
| [0.5194972157478333, 0.45368078351020813, 0.5302746295928955, 0.2755521535873413, -0.3021118640899658, ...]
| Noncontrolling interest
| Belang van derden
|
| [0.9302910566329956, 0.7344815731048584, 0.6589862108230591, 0.1774829477071762, 0.528937578201294, ...]
| Debt
| Leningen
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-da
* Dataset: en-da
* Size: 4,554 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.016798147931694984, 0.7280638813972473, 0.1259734034538269, -0.07660696655511856, -0.20033679902553558, ...]
| Provisions current portion
| Hensatte forpligtelser
|
| [-0.07381738722324371, -0.07786396145820618, -0.21328210830688477, 0.18608279526233673, -0.3095148205757141, ...]
| or loss
| Kursreguleringer
|
| [-0.4245157241821289, 0.4695541262626648, 0.05997037887573242, 0.2986871004104614, 0.011750679463148117, ...]
| assets depreciation
| Af og nedskrivninger
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-lt
* Dataset: en-lt
* Size: 2,998 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.2119722217321396, 0.5226094722747803, -0.3225395679473877, 0.6458964347839355, -0.22873802483081818, ...]
| NOTE
| Atsargos
|
| [0.5478602647781372, 0.3326689302921295, -0.14589856564998627, 0.5814526677131653, 0.5692975521087646, ...]
| Repayment of loan
| Paskolu grazinimas
|
| [0.2744126319885254, 0.5255246162414551, 0.05724802985787392, 0.25815054774284363, -0.766740620136261, ...]
| Attributable to the owners of the Company
| Bendroves akcininkams
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-is
* Dataset: en-is
* Size: 2,138 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.037829890847206116, 1.1669130325317383, 0.2974126636981964, 0.16161930561065674, 0.022792719304561615, ...]
| Tax expenses
| Tekjuskattur
|
| [0.11290981620550156, 0.3291318714618683, -0.6060066819190979, 0.029671549797058105, -0.4738736152648926, ...]
| Share of profit from Hyundai Glovis
| Ahrif hlutdeildarfelaga
|
| [-0.1636863499879837, -0.4239570200443268, 0.2055961787700653, -1.1946961879730225, 0.13549365103244781, ...]
| Changes in working capital requirements
| Veltufe fra rekstri
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-sl
* Dataset: en-sl
* Size: 834 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.020984871312975883, -0.31524133682250977, 0.10546927899122238, 1.0089449882507324, -0.592142641544342, ...]
| Net cash flows tofrom investing activities
| activities
|
| [0.1349133551120758, -0.2043939232826233, 0.2521047592163086, -0.04384709894657135, -0.5578309893608093, ...]
| Net cash ows from investing activities
| activities
|
| [-0.16783905029296875, 1.331608533859253, 0.9504968523979187, 0.402763694524765, -0.8187195658683777, ...]
| Foreign currency translations
| Prevedbena rezerva
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-sv-se
* Dataset: en-sv-se
* Size: 847 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.14358974993228912, 0.12112939357757568, 0.152898907661438, 0.2965115010738373, -0.6465349197387695, ...]
| Cash flow from investing activities
| Kassaflode fran investeringsverksamheten
|
| [-0.3012215495109558, -0.6284143924713135, 0.952661395072937, 0.6150138974189758, 1.3908427953720093, ...]
| reporting year
| Likvida medel
|
| [0.7741854190826416, 0.9692693948745728, -0.48180654644966125, -0.3358636796474457, -1.0314745903015137, ...]
| Note c
| Personalkostnader
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-fi-fi
* Dataset: en-fi-fi
* Size: 874 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.0959925726056099, -0.0646059587597847, -0.5595968961715698, 0.40048298239707947, -0.0345945879817009, ...]
| Soci della controllante
| Emoyhtion osakkeenomistajille
|
| [0.07576075196266174, 0.13357341289520264, 0.2546372711658478, 0.0818142369389534, -0.08272691816091537, ...]
| ordinary shareholders of the parent company
| Emoyhtion osakkeenomistajille
|
| [-0.1580277979373932, 0.6337043642997742, 0.21239566802978516, 0.5370602011680603, -1.064493179321289, ...]
| Net gains losses on investments in foreign operations
| Muuntoerot
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-en-gb
* Dataset: en-en-gb
* Size: 551 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.18707695603370667, 0.7752551436424255, 0.12487845122814178, 0.7609840631484985, 0.21821437776088715, ...]
| Shortterm and current portion of longterm debt
| Borrowings
|
| [-0.24947500228881836, 1.0999057292938232, 0.3973265290260315, 0.551521897315979, -0.20870772004127502, ...]
| Trade and other
| Trade and other payables
|
| [0.16158847510814667, 0.9547826647758484, 0.5619722604751587, 1.3562628030776978, -0.42042723298072815, ...]
| Interest rate derivatives
| Derivative financial instruments
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-lv
* Dataset: en-lv
* Size: 487 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.5849851369857788, 0.12363594025373459, -0.019146278500556946, 0.223326176404953, 0.3553294241428375, ...]
| Noncurrent interestbearing loans
| Aiznemumi no kreditiestadem
|
| [-0.4405641555786133, 0.6129574179649353, 0.3001856207847595, 0.2243034392595291, 0.3611409366130829, ...]
| Loans long term
| Aiznemumi no kreditiestadem
|
| [0.4723680913448334, 0.5573369860649109, -0.02968907356262207, -0.17952217161655426, -0.6545169949531555, ...]
| Proceeds from dividends
| No meitassabiedribam sanemtas dividendes
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-el
* Dataset: en-el
* Size: 104 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.4922516345977783, -0.07638876140117645, 0.27244681119918823, -0.03274909406900406, -0.44587045907974243, ...]
| other reserves
| Reserves
|
| [0.02690565586090088, 0.5322003960609436, -0.22316685318946838, 1.4094343185424805, -1.2200299501419067, ...]
| Derivativesliabilities
| Derivative financial instruments
|
| [-0.4285869002342224, -1.2929456233978271, -0.05507340282201767, -0.9150614142417908, -1.67551589012146, ...]
| Invested unrestricted equity fund
| Reserves
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-et
* Dataset: en-et
* Size: 136 training samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.23401905596256256, 0.947270393371582, -0.3706150949001312, 0.32394295930862427, -0.10204663872718811, ...]
| Depreciation and amortisation including impairment charges
| Pohivara kulum
|
| [0.5078503489494324, 0.9610038995742798, 0.028378624469041824, 0.5917476415634155, -1.4292068481445312, ...]
| vii
| Pohivara kulum
|
| [-0.39173853397369385, 0.42254066467285156, -0.6972977519035339, 0.13764289021492004, 0.11351882666349411, ...]
| Total depreciation
| Pohivara kulum
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Datasets
#### en-fr
* Dataset: en-fr
* Size: 27,038 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]
| Revenue
| Ventes
|
| [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]
| Revenue
| Produits des activites ordinaires
|
| [-0.7187896966934204, 0.300822377204895, -0.038356583565473557, 1.0221939086914062, -0.07130642980337143, ...]
| Distribution costs
| Frais commerciaux
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-fi
* Dataset: en-fi
* Size: 7,849 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.044488366693258286, 0.4498324394226074, 0.35706791281700134, 0.5602209568023682, -0.1801929622888565, ...]
| Tax on profit for the year
| Tuloverot
|
| [-0.044488366693258286, 0.4498324394226074, 0.35706791281700134, 0.5602209568023682, -0.1801929622888565, ...]
| Tax on profit for the year
| Income taxes
|
| [-0.10370840132236481, 0.5262670516967773, -0.1583852767944336, 0.05357339233160019, 0.7700905799865723, ...]
| Remeasurements of defined benefit plans
| Etuuspohjaisen nettovelan uudelleen maarittamisesta johtuvat erat
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-pl
* Dataset: en-pl
* Size: 19,308 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.012203109450638294, 0.6782587766647339, 0.11951778084039688, -0.30175572633743286, -0.6870222091674805, ...]
| Administrative expenses
| Ogolne koszty administracyjne
|
| [0.11572737991809845, 1.1026246547698975, 0.1337483674287796, 0.13492430746555328, -0.2561548352241516, ...]
| Other operating income
| Pozostale przychody
|
| [-0.012237715534865856, 0.7524855136871338, 0.0722682923078537, -0.1759086549282074, -0.8265506625175476, ...]
| Other operating expenses
| Pozostale koszty operacyjne
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-sv
* Dataset: en-sv
* Size: 15,902 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]
| Revenue
| Nettoomsattning
|
| [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]
| Revenue
| Summa rorelsens intakter
|
| [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]
| Revenue
| Summa intakter
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-de
* Dataset: en-de
* Size: 19,441 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]
| Financial income
| Finanzertrage
|
| [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]
| Financial income
| IIIB
|
| [0.10624096542596817, 0.2766471207141876, 0.6653332114219666, 0.09570542722940445, -0.5832860469818115, ...]
| Financial expenses
| Finanzaufwendungen
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-it
* Dataset: en-it
* Size: 15,109 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.0050657871179282665, 0.7755593061447144, -0.4470928907394409, -0.18634264171123505, 0.390926718711853, ...]
| Revenue
| Ricavi
|
| [0.11572737991809845, 1.1026246547698975, 0.1337483674287796, 0.13492430746555328, -0.2561548352241516, ...]
| Other operating income
| Altri proventi
|
| [-0.012237218208611012, 0.7524856925010681, 0.0722685381770134, -0.17590798437595367, -0.8265498876571655, ...]
| Other operating expenses
| Altri oneri
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-pt
* Dataset: en-pt
* Size: 3,206 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.0850653275847435, 0.5872150659561157, 0.3560439944267273, -0.4916071593761444, -0.5272688269615173, ...]
| Investments in intangible assets
| Ativos intangiveis
|
| [-0.29471272230148315, 0.912581205368042, -0.22577235102653503, 0.051218513399362564, -0.2710682451725006, ...]
| Other provisions
| Provisoes
|
| [0.03657735511660576, 0.3423381447792053, -0.249881774187088, -0.22646693885326385, 0.7550634145736694, ...]
| Remeasurements of defined benefit schemes
| Ganhos perdas atuariais
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-no
* Dataset: en-no
* Size: 1,541 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.11572737991809845, 1.1026246547698975, 0.1337483674287796, 0.13492430746555328, -0.2561548352241516, ...]
| Other operating income
| Andre driftsinntekter
|
| [0.6171316504478455, 0.09544796496629715, 0.3045019507408142, 1.3532874584197998, -0.5360710024833679, ...]
| Net profit for the year
| Arets resultat
|
| [0.31753233075141907, 0.9272720813751221, -0.13628403842449188, -0.618966817855835, -0.11626463383436203, ...]
| Income tax paid
| Betalte skatter
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-nb
* Dataset: en-nb
* Size: 1,496 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.7072435021400452, 0.33462974429130554, -0.25377699732780457, 0.554284393787384, -0.9292709231376648, ...]
| Operating profit EBIT
| Resultat etter skatt
|
| [0.6171316504478455, 0.09544817358255386, 0.3045021593570709, 1.3532869815826416, -0.5360713601112366, ...]
| Net profit for the year
| Resultat etter skatt
|
| [0.6171316504478455, 0.09544817358255386, 0.3045021593570709, 1.3532869815826416, -0.5360713601112366, ...]
| Net profit for the year
| Resultat
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-de-de
* Dataset: en-de-de
* Size: 284 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.005065362900495529, 0.7755594253540039, -0.4470923840999603, -0.1863422989845276, 0.39092710614204407, ...]
| Revenue
| Umsatzerlose
|
| [0.6505127549171448, 0.502105712890625, 0.05527564138174057, 0.031440261751413345, -0.10601992905139923, ...]
| Interest received
| Erhaltene Zinsen
|
| [0.5774980783462524, 0.4874580204486847, -0.11888153851032257, 0.025767352432012558, 0.07453231513500214, ...]
| Total revenue
| Umsatzerlose
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-es
* Dataset: en-es
* Size: 12,190 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.011251086369156837, 0.17945028841495514, -0.23512840270996094, 0.601173996925354, 0.3077372610569, ...]
| Gross profit
| MARGEN BRUTO
|
| [0.11572762578725815, 1.1026241779327393, 0.13374821841716766, 0.13492360711097717, -0.2561551034450531, ...]
| Other operating income
| Ingresos accesorios y otros de gestion corriente
|
| [0.7072424292564392, 0.3346295654773712, -0.25377705693244934, 0.5542840361595154, -0.9292711615562439, ...]
| Operating profit EBIT
| MARGEN BRUTO
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-cs
* Dataset: en-cs
* Size: 894 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]
| Financial income
| Financni vynosy
|
| [0.8856601715087891, 0.7636779546737671, -0.22451487183570862, 0.9918713569641113, 0.730712890625, ...]
| Finance income
| Financni vynosy
|
| [0.35414567589759827, 0.484447717666626, 0.41246268153190613, 0.26654252409935, -0.46763384342193604, ...]
| Noncontrolling interests
| Nekontrolnim podilum
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-nl
* Dataset: en-nl
* Size: 3,429 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.5604397058486938, 0.9408637285232544, 0.12189843505620956, -0.34225529432296753, -0.11250410228967667, ...]
| Interest paid etc
| Betaalde rente
|
| [0.31753233075141907, 0.9272720813751221, -0.13628403842449188, -0.618966817855835, -0.11626463383436203, ...]
| Income tax paid
| Betaalde winstbelastingen
|
| [0.39916926622390747, 0.20327667891979218, 0.41986599564552307, -0.6084388494491577, -0.4903983175754547, ...]
| Intangible assets
| Immateriele vaste activa
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-da
* Dataset: en-da
* Size: 1,901 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.405586302280426, 0.545492947101593, 0.5445799231529236, 0.5528497695922852, 0.3698521554470062, ...]
| Financial income
| Finansielle indtaegter
|
| [0.5749809145927429, 0.25882387161254883, 0.06829871982336044, 0.3255525231361389, -0.193973109126091, ...]
| Movements on credit facilities
| Kreditinstitutter
|
| [-0.5068938136100769, 0.421630859375, 0.4049156904220581, -0.48719698190689087, -0.10700821876525879, ...]
| Share capital
| Aktiekapital
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-lt
* Dataset: en-lt
* Size: 1,377 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.04448840767145157, 0.44983237981796265, 0.3570672273635864, 0.5602210760116577, -0.18019315600395203, ...]
| Tax on profit for the year
| Pelno mokescio sanaudos
|
| [0.053332049399614334, 0.6696042418479919, 0.218048557639122, 0.22305572032928467, -0.7841112017631531, ...]
| Other receivables
| Kitos gautinos sumos
|
| [-0.5280259251594543, 0.39407506585121155, -0.17667946219444275, -0.9611474871635437, -1.0850781202316284, ...]
| Inventories
| Atsargos
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-is
* Dataset: en-is
* Size: 966 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.28052818775177, 0.5305177569389343, -0.2726171910762787, -0.6555124521255493, -1.195023775100708, ...]
| Property plant and equipment
| Rekstrarfjarmunir
|
| [0.5614703893661499, 0.7126756906509399, -0.7462524175643921, -0.8577789068222046, -0.2560833990573883, ...]
| Decrease increase in payables
| Vidskiptaskuldir og adrar skammtimaskuldir
|
| [0.6009606122970581, 1.0522949695587158, 0.024701133370399475, -0.4767942428588867, -0.27263158559799194, ...]
| Income tax
| Tekjuskattur
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-sl
* Dataset: en-sl
* Size: 357 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.044488903135061264, 0.44983190298080444, 0.35706815123558044, 0.560221791267395, -0.18019415438175201, ...]
| Tax on profit for the year
| Davek iz dobicka
|
| [0.10000382363796234, 0.1258276104927063, 0.48933619260787964, 0.4827534556388855, -1.07231605052948, ...]
| Current asset investments
| Financne nalozbe
|
| [0.00028255581855773926, -0.16900330781936646, -0.0987740308046341, 0.19973833858966827, -0.23712165653705597, ...]
| Net cash outflow from investing activities
| activities
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-sv-se
* Dataset: en-sv-se
* Size: 385 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.5604397058486938, 0.9408637285232544, 0.12189843505620956, -0.34225529432296753, -0.11250410228967667, ...]
| Interest paid etc
| Betald ranta
|
| [-0.15956099331378937, -0.104736328125, 0.17104840278625488, 0.3255482017993927, -0.4631202518939972, ...]
| Cash flows from investing activities
| Kassaflode fran investeringsverksamheten
|
| [0.11968827247619629, 0.7799925208091736, -0.08703255653381348, -1.228922724723816, -1.6603511571884155, ...]
| Cash and cash equivalents
| Likvida medel
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-fi-fi
* Dataset: en-fi-fi
* Size: 389 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.08681110292673111, 0.06999394297599792, 0.16943465173244476, -0.6658964157104492, -1.3333454132080078, ...]
| Equity shareholders
| Emoyhtion osakkeenomistajille
|
| [-0.4602399170398712, 1.3417373895645142, 0.6107428073883057, 0.45281982421875, -0.7822347283363342, ...]
| Exchange differences arising on translation of foreign operations
| Muuntoerot
|
| [0.20600593090057373, 0.06086999550461769, 0.1364181935787201, 0.6713289618492126, -0.8476033210754395, ...]
| Attributable to the shareholders
| Emoyhtion osakkeenomistajille
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-en-gb
* Dataset: en-en-gb
* Size: 239 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.35414567589759827, 0.484447717666626, 0.41246268153190613, 0.26654252409935, -0.46763384342193604, ...]
| Noncontrolling interests
| Noncontrolling interests
|
| [-0.26346728205680847, 1.010565161705017, 0.25545963644981384, -0.09261462837457657, -0.5145906805992126, ...]
| Trade and other payables
| Trade and other payables
|
| [0.3337377905845642, 0.28091752529144287, 0.26623502373695374, 0.8748410940170288, -0.44941988587379456, ...]
| Attributable to noncontrolling interest
| Noncontrolling interests
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-lv
* Dataset: en-lv
* Size: 210 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.3419339656829834, -0.2543298006057739, 0.34351760149002075, 0.6980054378509521, 0.699012815952301, ...]
| Interestbearing loans and borrowings
| Aiznemumi
|
| [0.27617645263671875, 0.6733821630477905, 0.47860750555992126, 0.4202423095703125, 0.044836655259132385, ...]
| Borrowings and bank overdrafts
| Aiznemumi
|
| [0.36503127217292786, -0.47215989232063293, 0.6517267227172852, 0.6172035932540894, 1.0784108638763428, ...]
| loans and borrowings
| Aiznemumi no kreditiestadem
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-el
* Dataset: en-el
* Size: 39 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | list | string | string |
| details | [-0.07889065891504288, -0.6466420888900757, 0.4228314757347107, -0.11737698316574097, -0.06180833652615547, ...]
| Share premium account
| Reserves
|
| [0.07863229513168335, 0.6249228119850159, -0.08239512890577316, 0.9754469990730286, 0.02359396405518055, ...]
| Derivative liabilities note
| Derivative financial instruments
|
| [-0.1764196902513504, 0.4463600814342499, 0.06581983715295792, 0.787315845489502, -0.7786881923675537, ...]
| Derivatives liabilities
| Derivative financial instruments
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
#### en-et
* Dataset: en-et
* Size: 52 evaluation samples
* Columns: label
, english
, and non_english
* Approximate statistics based on the first 1000 samples:
| | label | english | non_english |
|:--------|:-------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
| type | list | string | string |
| details | [0.5006873607635498, 0.9590571522712708, 0.5849384069442749, -0.725926399230957, -0.5808520317077637, ...]
| impairment of noncurrent assets
| Pohivara kulum
|
| [-0.12556228041648865, 0.2528606057167053, -0.2748187780380249, 0.25966036319732666, -0.31089597940444946, ...]
| depreciation and amortisation
| Pohivara kulum
|
| [0.458812415599823, 1.155530571937561, -0.515108585357666, 0.35893556475639343, 0.506560206413269, ...]
| Amortyzacja
| Pohivara kulum
|
* Loss: [MSELoss
](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters