--- 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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|:--------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------|:-----------------------------------------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:---------------------------------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:-------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------|:--------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:---------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|:---------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|:---------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------|:-------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------|:-----------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------|:-----------------------------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------|:------------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:---------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:--------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------|:------------------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------|:------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:--------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:-----------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|:-------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------|:--------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:---------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------|:------------------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|:-------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:--------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:----------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------|:------------------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------|:----------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------|:----------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:--------------------------------|:-----------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|:-------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:---------------------|:--------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:--------------------------------------------------------------------------------------------------------------------------|:--------------------------------|:--------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|:----------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:-------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:-------------------------------------|:-----------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:--------------------------------------------------------------------------------------------------------------------------|:-------------------------------------|:----------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:---------------------------------------------------------------------------------------------------------------------------|:------------------------------------|:--------------------------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|:----------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:---------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------|:------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------|:-------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|:--------------------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:---------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------|:------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:------------------------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------|:--------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:---------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:-----------------------------------------| | [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 | | | | * Samples: | label | english | non_english | |:------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------|:----------------------------------------------| | [-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 | | | | * Samples: | label | english | non_english | |:-----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------|:----------------------------| | [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
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-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`: 5 - `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`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | en-es loss | en-pl loss | en-is loss | en-sv loss | en-sv-se loss | en-da loss | en-en-gb loss | en-de loss | en-pt loss | en-fi loss | en-sl loss | en-el loss | en-nb loss | en-de-de loss | en-cs loss | en-et loss | en-nl loss | en-lt loss | en-no loss | en-it loss | en-fi-fi loss | en-lv loss | en-fr loss | en-cs_mean_accuracy | en-cs_negative_mse | en-da_mean_accuracy | en-da_negative_mse | en-de-de_mean_accuracy | en-de-de_negative_mse | en-de_mean_accuracy | en-de_negative_mse | en-el_mean_accuracy | en-el_negative_mse | en-en-gb_mean_accuracy | en-en-gb_negative_mse | en-es_mean_accuracy | en-es_negative_mse | en-et_mean_accuracy | en-et_negative_mse | en-fi-fi_mean_accuracy | en-fi-fi_negative_mse | en-fi_mean_accuracy | en-fi_negative_mse | en-fr_mean_accuracy | en-fr_negative_mse | en-is_mean_accuracy | en-is_negative_mse | en-it_mean_accuracy | en-it_negative_mse | en-lt_mean_accuracy | en-lt_negative_mse | en-lv_mean_accuracy | en-lv_negative_mse | en-nb_mean_accuracy | en-nb_negative_mse | en-nl_mean_accuracy | en-nl_negative_mse | en-no_mean_accuracy | en-no_negative_mse | en-pl_mean_accuracy | en-pl_negative_mse | en-pt_mean_accuracy | en-pt_negative_mse | en-sl_mean_accuracy | en-sl_negative_mse | en-sv-se_mean_accuracy | en-sv-se_negative_mse | en-sv_mean_accuracy | en-sv_negative_mse | |:------:|:-----:|:-------------:|:----------:|:----------:|:----------:|:----------:|:-------------:|:----------:|:-------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-------------:|:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|:-------------:|:----------:|:----------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:----------------------:|:---------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:----------------------:|:---------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:----------------------:|:---------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:-------------------:|:------------------:|:----------------------:|:---------------------:|:-------------------:|:------------------:| | 0.0205 | 100 | 0.7598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0410 | 200 | 0.5938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0615 | 300 | 0.405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0819 | 400 | 0.3145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1024 | 500 | 0.2891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1229 | 600 | 0.2762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1434 | 700 | 0.2693 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1639 | 800 | 0.2655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1844 | 900 | 0.2645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2048 | 1000 | 0.2656 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2253 | 1100 | 0.2623 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2458 | 1200 | 0.2606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2663 | 1300 | 0.2674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2868 | 1400 | 0.2571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3073 | 1500 | 0.252 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3277 | 1600 | 0.2464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3482 | 1700 | 0.2396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0205 | 100 | 0.2311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0410 | 200 | 0.2294 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0615 | 300 | 0.2297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0819 | 400 | 0.2282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1024 | 500 | 0.2283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1229 | 600 | 0.2251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1434 | 700 | 0.2259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1639 | 800 | 0.224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1844 | 900 | 0.2213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2048 | 1000 | 0.2202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2253 | 1100 | 0.219 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2458 | 1200 | 0.2162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2663 | 1300 | 0.213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2868 | 1400 | 0.2097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3073 | 1500 | 0.2069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3277 | 1600 | 0.206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3482 | 1700 | 0.2017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3687 | 1800 | 0.1982 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3892 | 1900 | 0.1985 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4097 | 2000 | 0.1953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4302 | 2100 | 0.1923 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4506 | 2200 | 0.1912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4711 | 2300 | 0.1867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4916 | 2400 | 0.1876 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5121 | 2500 | 0.1865 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5326 | 2600 | 0.1816 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5531 | 2700 | 0.1786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5735 | 2800 | 0.1786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5940 | 2900 | 0.1775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6145 | 3000 | 0.175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6350 | 3100 | 0.1735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6555 | 3200 | 0.1731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6760 | 3300 | 0.1717 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6964 | 3400 | 0.1703 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7169 | 3500 | 0.17 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7374 | 3600 | 0.1668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7579 | 3700 | 0.1648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7784 | 3800 | 0.1664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7989 | 3900 | 0.1638 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8193 | 4000 | 0.1616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8398 | 4100 | 0.1631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8603 | 4200 | 0.1614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8808 | 4300 | 0.1592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9013 | 4400 | 0.1597 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9218 | 4500 | 0.1605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9422 | 4600 | 0.1593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9627 | 4700 | 0.1573 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9832 | 4800 | 0.1608 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0037 | 4900 | 0.1559 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0242 | 5000 | 0.1567 | 0.1498 | 0.1470 | 0.1566 | 0.1527 | 0.1467 | 0.1618 | 0.1750 | 0.1491 | 0.1507 | 0.1475 | 0.1495 | 0.2114 | 0.1634 | 0.1609 | 0.1529 | 0.1600 | 0.1575 | 0.1526 | 0.1587 | 0.1497 | 0.1438 | 0.1393 | 0.1466 | 0.0112 | -20.2568 | 0.0100 | -21.6889 | 0.0246 | -21.0408 | 0.0022 | -19.8428 | 0.0513 | -25.8657 | 0.0146 | -25.2005 | 0.0048 | -19.5834 | 0.0192 | -20.9676 | 0.0180 | -19.3767 | 0.0046 | -19.5729 | 0.0019 | -19.3683 | 0.0083 | -20.4514 | 0.0031 | -19.7759 | 0.0091 | -20.1992 | 0.0095 | -18.4323 | 0.0117 | -21.5480 | 0.0063 | -20.7031 | 0.0097 | -21.0662 | 0.0023 | -19.3807 | 0.0069 | -19.5748 | 0.0112 | -19.9180 | 0.0169 | -18.6913 | 0.0031 | -20.0760 | | 1.0447 | 5100 | 0.1554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0651 | 5200 | 0.1558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0856 | 5300 | 0.1542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1061 | 5400 | 0.1533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1266 | 5500 | 0.1538 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1471 | 5600 | 0.1527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1676 | 5700 | 0.1535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1880 | 5800 | 0.1539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2085 | 5900 | 0.1529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2290 | 6000 | 0.1546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2495 | 6100 | 0.1523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2700 | 6200 | 0.1484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2905 | 6300 | 0.1509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3109 | 6400 | 0.1496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3314 | 6500 | 0.1505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3519 | 6600 | 0.148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3724 | 6700 | 0.1477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3929 | 6800 | 0.1482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4134 | 6900 | 0.1473 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4338 | 7000 | 0.1479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4543 | 7100 | 0.1476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4748 | 7200 | 0.1449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4953 | 7300 | 0.1469 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5158 | 7400 | 0.1486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5363 | 7500 | 0.1457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5567 | 7600 | 0.1448 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5772 | 7700 | 0.1449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5977 | 7800 | 0.1433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6182 | 7900 | 0.1433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6387 | 8000 | 0.1433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6592 | 8100 | 0.1432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6796 | 8200 | 0.1434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7001 | 8300 | 0.1423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7206 | 8400 | 0.1428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7411 | 8500 | 0.1412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7616 | 8600 | 0.1401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7821 | 8700 | 0.142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8025 | 8800 | 0.141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8230 | 8900 | 0.1397 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8435 | 9000 | 0.1404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8640 | 9100 | 0.1401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8845 | 9200 | 0.1395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9050 | 9300 | 0.1391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9254 | 9400 | 0.1411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9459 | 9500 | 0.1394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9664 | 9600 | 0.1386 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9869 | 9700 | 0.1415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0074 | 9800 | 0.1388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0279 | 9900 | 0.1402 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0483 | 10000 | 0.1393 | 0.1328 | 0.1306 | 0.1365 | 0.1342 | 0.1282 | 0.1368 | 0.1601 | 0.1335 | 0.1335 | 0.1318 | 0.1305 | 0.1868 | 0.1486 | 0.1445 | 0.1349 | 0.1292 | 0.1395 | 0.1348 | 0.1462 | 0.1330 | 0.1301 | 0.1219 | 0.1304 | 0.0117 | -19.4912 | 0.0121 | -19.7982 | 0.0282 | -20.8897 | 0.0025 | -19.6494 | 0.0513 | -24.6742 | 0.0167 | -25.4686 | 0.0045 | -19.1742 | 0.0192 | -17.9511 | 0.0193 | -19.3175 | 0.0050 | -19.3365 | 0.0024 | -19.0925 | 0.0083 | -19.6830 | 0.0033 | -19.4012 | 0.0109 | -19.7036 | 0.0119 | -18.3941 | 0.0107 | -21.7453 | 0.0063 | -20.2261 | 0.0114 | -21.4993 | 0.0028 | -19.0938 | 0.0073 | -19.3771 | 0.0112 | -18.8671 | 0.0195 | -17.8846 | 0.0037 | -19.4199 | | 2.0688 | 10100 | 0.1382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0893 | 10200 | 0.1368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1098 | 10300 | 0.1378 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1303 | 10400 | 0.137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1508 | 10500 | 0.1369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1712 | 10600 | 0.1369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1917 | 10700 | 0.1382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2122 | 10800 | 0.1372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2327 | 10900 | 0.1369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2532 | 11000 | 0.1358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2737 | 11100 | 0.1343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2941 | 11200 | 0.1372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3146 | 11300 | 0.1354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3351 | 11400 | 0.1364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3556 | 11500 | 0.135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3761 | 11600 | 0.1349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3966 | 11700 | 0.1353 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4170 | 11800 | 0.1353 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4375 | 11900 | 0.1354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4580 | 12000 | 0.1357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4785 | 12100 | 0.1328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4990 | 12200 | 0.1355 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5195 | 12300 | 0.1356 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5399 | 12400 | 0.1349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5604 | 12500 | 0.1332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5809 | 12600 | 0.1345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6014 | 12700 | 0.1327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6219 | 12800 | 0.1326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6424 | 12900 | 0.1332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6628 | 13000 | 0.1332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6833 | 13100 | 0.1334 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7038 | 13200 | 0.1328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7243 | 13300 | 0.1334 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7448 | 13400 | 0.1323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7653 | 13500 | 0.132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7857 | 13600 | 0.1318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8062 | 13700 | 0.1324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8267 | 13800 | 0.1323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8472 | 13900 | 0.1313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8677 | 14000 | 0.1318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8882 | 14100 | 0.1311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9086 | 14200 | 0.1312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9291 | 14300 | 0.1336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9496 | 14400 | 0.1312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9701 | 14500 | 0.1312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9906 | 14600 | 0.1334 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0111 | 14700 | 0.131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0315 | 14800 | 0.1316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0520 | 14900 | 0.1312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0725 | 15000 | 0.1304 | 0.1256 | 0.1236 | 0.1278 | 0.1269 | 0.1225 | 0.1291 | 0.1491 | 0.1258 | 0.1254 | 0.1250 | 0.1222 | 0.1761 | 0.1366 | 0.1376 | 0.1273 | 0.1216 | 0.1316 | 0.1280 | 0.1349 | 0.1257 | 0.1253 | 0.1152 | 0.1233 | 0.0123 | -19.1985 | 0.0124 | -19.4425 | 0.0282 | -20.7684 | 0.0025 | -19.2806 | 0.0513 | -24.1800 | 0.0146 | -24.4860 | 0.0044 | -18.9131 | 0.0192 | -17.5769 | 0.0180 | -19.5151 | 0.0049 | -19.1971 | 0.0025 | -18.8663 | 0.0083 | -19.2975 | 0.0034 | -19.1577 | 0.0102 | -19.5784 | 0.0095 | -18.1528 | 0.0117 | -20.5703 | 0.0076 | -19.9089 | 0.0114 | -20.4863 | 0.0027 | -18.9161 | 0.0083 | -19.0866 | 0.0126 | -18.3424 | 0.0208 | -17.8123 | 0.0039 | -19.1637 | | 3.0930 | 15100 | 0.1304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1135 | 15200 | 0.1302 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1340 | 15300 | 0.1296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1544 | 15400 | 0.1307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1749 | 15500 | 0.1308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1954 | 15600 | 0.1309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2159 | 15700 | 0.1312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2364 | 15800 | 0.1299 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2569 | 15900 | 0.1303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2773 | 16000 | 0.1288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2978 | 16100 | 0.131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3183 | 16200 | 0.1296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3388 | 16300 | 0.1308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3593 | 16400 | 0.1277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3798 | 16500 | 0.1309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4002 | 16600 | 0.1282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4207 | 16700 | 0.1298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4412 | 16800 | 0.1307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4617 | 16900 | 0.1293 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4822 | 17000 | 0.1282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5027 | 17100 | 0.1307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5231 | 17200 | 0.1302 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5436 | 17300 | 0.1305 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5641 | 17400 | 0.129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5846 | 17500 | 0.1292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6051 | 17600 | 0.1286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6256 | 17700 | 0.1282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6460 | 17800 | 0.1291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6665 | 17900 | 0.128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6870 | 18000 | 0.129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7075 | 18100 | 0.1289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7280 | 18200 | 0.1289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7485 | 18300 | 0.1268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7689 | 18400 | 0.128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7894 | 18500 | 0.128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8099 | 18600 | 0.1284 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8304 | 18700 | 0.1278 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8509 | 18800 | 0.1276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8714 | 18900 | 0.1279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8918 | 19000 | 0.1274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9123 | 19100 | 0.1277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9328 | 19200 | 0.1293 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9533 | 19300 | 0.1277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9738 | 19400 | 0.1281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9943 | 19500 | 0.1294 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0147 | 19600 | 0.1275 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0352 | 19700 | 0.1289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0557 | 19800 | 0.1277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0762 | 19900 | 0.1269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0967 | 20000 | 0.1287 | 0.1223 | 0.1207 | 0.1242 | 0.1232 | 0.1192 | 0.1258 | 0.1411 | 0.1225 | 0.1219 | 0.1214 | 0.1184 | 0.1690 | 0.1335 | 0.1345 | 0.1239 | 0.1186 | 0.1281 | 0.1294 | 0.1321 | 0.1223 | 0.1211 | 0.1119 | 0.1200 | 0.0129 | -19.1286 | 0.0121 | -19.3856 | 0.0282 | -20.8682 | 0.0027 | -19.2001 | 0.0513 | -23.5207 | 0.0146 | -23.5088 | 0.0049 | -18.8438 | 0.0192 | -17.5146 | 0.0180 | -19.2820 | 0.0052 | -19.0790 | 0.0024 | -18.7979 | 0.0083 | -19.2169 | 0.0036 | -19.0771 | 0.0109 | -20.4850 | 0.0071 | -18.0377 | 0.0127 | -20.6013 | 0.0076 | -19.8483 | 0.0117 | -20.6052 | 0.0029 | -18.9324 | 0.0083 | -19.0009 | 0.0126 | -18.1531 | 0.0221 | -17.6476 | 0.0038 | -19.0325 | | 4.1172 | 20100 | 0.1262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1376 | 20200 | 0.1277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1581 | 20300 | 0.1276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1786 | 20400 | 0.1274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1991 | 20500 | 0.1278 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2196 | 20600 | 0.1282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2401 | 20700 | 0.1272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2605 | 20800 | 0.1284 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2810 | 20900 | 0.1263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3015 | 21000 | 0.1283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3220 | 21100 | 0.128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3425 | 21200 | 0.1273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3630 | 21300 | 0.1256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3834 | 21400 | 0.1274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4039 | 21500 | 0.1264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4244 | 21600 | 0.1276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4449 | 21700 | 0.1281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4654 | 21800 | 0.1261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4859 | 21900 | 0.1269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5063 | 22000 | 0.1292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5268 | 22100 | 0.1271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5473 | 22200 | 0.1272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5678 | 22300 | 0.1261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5883 | 22400 | 0.1262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6088 | 22500 | 0.1266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6293 | 22600 | 0.1256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6497 | 22700 | 0.1272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6702 | 22800 | 0.126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6907 | 22900 | 0.1268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7112 | 23000 | 0.1277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7317 | 23100 | 0.1263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7522 | 23200 | 0.1254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7726 | 23300 | 0.1267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7931 | 23400 | 0.1263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8136 | 23500 | 0.1258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8341 | 23600 | 0.1266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8546 | 23700 | 0.1261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8751 | 23800 | 0.1254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8955 | 23900 | 0.126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9160 | 24000 | 0.1272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9365 | 24100 | 0.1267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9570 | 24200 | 0.1266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9775 | 24300 | 0.1263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9980 | 24400 | 0.1279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
### Framework Versions - Python: 3.12.3 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.2 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MSELoss ```bibtex @inproceedings{reimers-2020-multilingual-sentence-bert, title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2020", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2004.09813", } ```