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--- |
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language: |
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- tr |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:9623924 |
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- loss:MSELoss |
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base_model: BAAI/bge-m3 |
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widget: |
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- source_sentence: Ak Hunlar'ın kültürel etkileşimleri ve mirasları hakkında ne söyleyebiliriz? |
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Ak Hunlar'ın diğer kültürler üzerindeki etkileri ve izleri nelerdir? |
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sentences: |
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- Film, hangi oyun yazarının hayatını konu almaktadır? |
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- Bir Eskişehir-Afyonkarahisar tren yolculuğu ne kadar sürmektedir? |
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- Mektupta, Türkiye'nin adaya tek taraflı müdahalesinin Türk ve Yunan tarafları |
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arasında savaşa yol açabileceği ve NATO üyesi olan bu iki ülkenin savaşmasının |
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kabul edilemez olduğu ifade edilmiştir. Türkiye'nin müdahale kararı almadan önce |
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müttefiklerine danışması gerektiği anımsatılmıştır. Ayrıca bu savaşın Sovyetler |
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Birliği'nin de Türkiye'ye müdahale ihtimalini doğuracağı ve NATO'nun böyle bir |
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durumda Türkiye'yi savunma konusunda isteksiz olacağı ima edilmiştir. ABD'nin |
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Türkiye'ye sağladığı askeri malzemenin bu müdahalede kullanılmasına izin verilmeyeceği |
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belirtilmiştir. Mektubun ardından Türkiye müdahale kararından vazgeçmiştir. İsmet |
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İnönü 21 Haziran 1964'te ABD'ye giderek başkan Johnson ile bir görüşmede bulunmuştur. |
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- source_sentence: Evet, metinde teslimiyetçilik, edilgenlik veya boyun eğme olarak |
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da tanımlanmaktadır. |
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sentences: |
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- Cezary Kucharski'nin doğduğu tarih nedir? |
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- Beylerbeyi Camii, 2013 yılında yapılan restorasyon çalışmaları sonrasında ne durumda? |
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- "İkinci Dünya Savaşı esnasında ve sonrasında elektroniklerin doğasından kaynaklanan\ |
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\ birçok güvenilir olmama durumu ve ürün yorgunluğu gündeme geldi. 1945'te M.A.\ |
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\ Miner, ASME (Amerikan Makine Mühendisleri Topluluğu) Dergisi içerisinde \"Yorulma\ |
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\ Esnasında Birikimli Hasar\" adında taslak bir yazı paylaştı. Ordu için uygulanan\ |
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\ ilk güvenilirlik hususu, Radar Sistemleri ve diğer elektronik parçalarda kullanılan,\ |
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\ yine güvenilirlik analizi sayesinde kanıtlanmış, oldukça arıza çıkarmaya yatkın\ |
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\ ve maliyetli bir vakum silindiri idi. Elektrik ve Elektronik Mühendisleri Enstitüsü,\ |
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\ 1948 yılında Güvenilirlik Topluluğunu kurmuştur. 1950 yılı içerisinde, asker\ |
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\ tarafında, Elektronik Ekipman Güvenilirliği Tavsiye Grubu kurulmuştur. Bu grup,\ |
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\ 3 ana çalışma yolu tavsiye etmiştir. Bunlar:\n\n Parça güvenilirliğinin arttırılması,\n\ |
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\ Tedarikçiler için kalite ve güvenilirlik gereksinimlerinin tanımlanması,\n Saha\ |
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\ verilerinin toplanması ve kök analiz yapılması." |
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- source_sentence: Belgrad'ın ele geçirilmesinde Klingenberg'in rolü nedir ve bu olay |
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nasıl gerçekleşti? |
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sentences: |
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- Jimmy White ve Peter Ebdon. |
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- DualSense kontrolörünün titreşim özelliği hakkında detaylı bilgi verir misiniz? |
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- "Kozluk, Kocaeli ilinin İzmit ilçesine bağlı bir mahalledir.\n\nNüfus\n\nKaynakça\ |
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\ \n\nİzmit'in mahalleleri" |
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- source_sentence: 1996 yılında kurulmuştur. Ağırlıklı olarak standart caz repertuvarından |
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parçalar sunmuşlardır. |
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sentences: |
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- San Leucio'nun coğrafi konumu hakkında bilgi verir misiniz? |
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- Kinik felsefesinin öncüsüdür. |
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- Aydın Doğu Demirkol'un vizyona girmesi planlanan sinema filmleri nelerdir ve yönetmenleri |
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kimlerdir? |
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- source_sentence: Serbest pazar prensiplerinin varlıklı ve yoksul futbol kulüpleri |
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arasındaki farkı büyütmesine yönelik kaygılar nedeniyle bu durum önemlidir. |
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sentences: |
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- Yazar, 12 Mart baskınlarının ve işkencelerinin sonucunda, ideolojik kimlikleriyle |
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küçük burjuva kimlikleri arasında çelişkiye düşen devrimcilerin rejime boyun eğmelerini |
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gösterme çabasındadır. |
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- "Verilen kesin süre \niçinde şikayetçi tarafından ilgili masraflar yatırıldığından\ |
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\ PTT’ce söz konusu \nkeşfa.va.nsınıngeri önd.e-rilmesi sonucu talimat \nmahkemesince\ |
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\ keşf yapılmamış ise de burada şikayetçiye atfedilebilecek bir kusur \nbulunmadığından,\ |
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\ keşif avansının ilgili mahkemeye tekrar gönderilerek keşfin \nyapılmasının sağlanarak\ |
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\ oluşacak sonuca göre bir karar verilmesi gerekir." |
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- This Kind of Bird Flies Backwards (Bu Cins Kuş Tersten Uçar) adlı ilk kitabı, |
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LeRoy Jones ve Hettie Jones'un kurduğu Totem Press tarafından 1958 yılında yayınlandı. |
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datasets: |
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- altaidevorg/tr-sentences |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- negative_mse |
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model-index: |
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- name: SentenceTransformer based on BAAI/bge-m3 |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: 0.9691269661048901 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.9650087926361528 |
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name: Spearman Cosine |
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- task: |
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type: knowledge-distillation |
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name: Knowledge Distillation |
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dataset: |
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name: Unknown |
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type: unknown |
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metrics: |
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- type: negative_mse |
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value: -0.006388394831446931 |
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name: Negative Mse |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: sts test |
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type: sts-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.9691398285942048 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.9650683134098942 |
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name: Spearman Cosine |
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--- |
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|
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# SentenceTransformer based on BAAI/bge-m3 |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) on the [tr-sentences](https://huggingface.co/datasets/altaidevorg/tr-sentences) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [tr-sentences](https://huggingface.co/datasets/altaidevorg/tr-sentences) |
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- **Language:** tr |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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|
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'Serbest pazar prensiplerinin varlıklı ve yoksul futbol kulüpleri arasındaki farkı büyütmesine yönelik kaygılar nedeniyle bu durum önemlidir.', |
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'Yazar, 12 Mart baskınlarının ve işkencelerinin sonucunda, ideolojik kimlikleriyle küçük burjuva kimlikleri arasında çelişkiye düşen devrimcilerin rejime boyun eğmelerini gösterme çabasındadır.', |
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"This Kind of Bird Flies Backwards (Bu Cins Kuş Tersten Uçar) adlı ilk kitabı, LeRoy Jones ve Hettie Jones'un kurduğu Totem Press tarafından 1958 yılında yayınlandı.", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Datasets: `sts-dev` and `sts-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | sts-dev | sts-test | |
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|:--------------------|:----------|:-----------| |
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| pearson_cosine | 0.9691 | 0.9691 | |
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| **spearman_cosine** | **0.965** | **0.9651** | |
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#### Knowledge Distillation |
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* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator) |
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| Metric | Value | |
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|:-----------------|:------------| |
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| **negative_mse** | **-0.0064** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### tr-sentences |
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* Dataset: [tr-sentences](https://huggingface.co/datasets/altaidevorg/tr-sentences) at [f5ebc52](https://huggingface.co/datasets/altaidevorg/tr-sentences/tree/f5ebc522ed687664c812bf5789714aead7a5842c) |
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* Size: 9,623,924 training samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------| |
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| type | string | list | |
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| details | <ul><li>min: 5 tokens</li><li>mean: 55.78 tokens</li><li>max: 468 tokens</li></ul> | <ul><li>size: 1024 elements</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>NBA tarihinde bu ödülü en çok kaç kez kim kazanmıştır?</code> | <code>[-0.027497457340359688, -0.024517377838492393, -0.013820995576679707, 0.00024465256137773395, -0.020534219220280647, ...]</code> | |
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| <code>Romero ve yapımcı Richard P. Rubinstein, yeni bir proje için herhangi bir yerli yatırımcılara temin koyamadıklarını söyledi. Romero Şans eseri, İtalyan korku yönetmeni Dario Argento'ya ulaştı. bu film Yaşayan Ölülerin Gecesi filmin'in kritik savunucusudur, Argento filmin korku klasik arasında yer almasına yardımcı olmak için istekliydi. uluslararası dağıtım hakları karşılığında finansman sağlamak için, Romero ve Rubinstein bir araya geldi. Senaryoyu yazarken bir sahnede değişiklik yapmak için Argento Roma'yı Romero filme davet etti. İkisi de daha sonra arsa gelişmelerini tartışmak için bir olabilirdi. Romero Monroeville Mall'ın durumunun yanı sıra Oxford Kalkınma'da alışveriş merkezi sahipleri ile bağlantıları ile ek bir güvenli finansman başardı. Döküm tamamlandıktan sonra, başlıca çekim tarihinin 13 Kasım, 1977 tarihinde film'in Pensilvanya'da başlaması planlanıyordu.</code> | <code>[-0.02431895025074482, -0.03177526593208313, -0.010546382516622543, 0.0393124595284462, -0.03390512242913246, ...]</code> | |
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| <code>Evet, Nasuhlar ismi Adapazarı, Kandıra ve Yenipazar ilçelerinde farklı yer isimlerine aittir.</code> | <code>[0.0020795632153749466, -0.013080586679279804, -0.018256550654768944, 0.022429518401622772, -0.03087380714714527, ...]</code> | |
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* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) |
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|
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### Evaluation Dataset |
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#### tr-sentences |
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* Dataset: [tr-sentences](https://huggingface.co/datasets/altaidevorg/tr-sentences) at [f5ebc52](https://huggingface.co/datasets/altaidevorg/tr-sentences/tree/f5ebc522ed687664c812bf5789714aead7a5842c) |
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* Size: 9,623,924 evaluation samples |
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* Columns: <code>sentence</code> and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence | label | |
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|:--------|:-----------------------------------------------------------------------------------|:--------------------------------------| |
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| type | string | list | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 51.95 tokens</li><li>max: 614 tokens</li></ul> | <ul><li>size: 1024 elements</li></ul> | |
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* Samples: |
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| sentence | label | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Bernhard, şiirle yazarlık hayatına başlamış ve 1963'te "Frost" (Don) adlı ilk romanını yayınlamıştır. 1957'den itibaren serbest yazarlık yapmaya başlamış ve hayatı boyunca yazarlık sayesinde geçimini sağlamıştır.</code> | <code>[-0.019921669736504555, -0.007309767417609692, 0.01690034568309784, -0.03302725777029991, -0.003539217868819833, ...]</code> | |
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| <code>Sonraki maçta AJ Styles ile Kevin Owens, WWE Birleşik Devletler Şampiyonluğu kemeri için maça çıktı. Shane McMahon, maçın özel konuk hakemliğini yaptı. As Shane, Owens'ı kontrol etti. Styles, Owens'a Springboard 450 Splash yapmaya çalışırken yanlışlıkla Shane'e de yaptı. Owens, Styles'a Pop Up Powerbomb yaptıktan sonra Styles'ı tuşlamaya çalıştı ancak Styles son anda kurtuldu. Owens, Shane'in kararını beğenmeyince ikisi arasında kısa süreli bir tartışma oldu. Owens, Styles'ın Calf Crusher hareketini karşıladıktan sonra Styles'tan tekme yiyince Shane'in üzerine düştü. Styles, Owens'ı Calf Crusher ile pes ettirse de ringin dışında aşağıda yatan Shane bunu göremedi. Bunun üzerine Styles da Shane ile tartıştı. Styles, Owens'a Styles Clash yaptıktan sonra tuşa gitti ancak Owens son anda kurtuldu. Owens'ın yaptığı Pop Up Powerbomb'dan sonra Styles'ı tuşladı ancak Shane son anda Styles'ın ayağının iplerde olduğunu fark edince tuşu iptal etti. Owens ve Shane tartışmaya başladı ve Shane,</code> | <code>[0.04532943293452263, -0.007217255420982838, -0.019380981102585793, -0.0026675150729715824, 0.018997980281710625, ...]</code> | |
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| <code>Leylek yavruları, anne ve babaları tarafından yiyip kısmen sindirdikleri besinleri kusarak beslenirler. Anne leylek yavruları yağmur, fırtına ve güneşten korurken, baba leylek yavrularını beslemekle yükümlüdür.</code> | <code>[-0.055585864931344986, 0.045432090759277344, -0.04405859857797623, 0.0009241091320291162, -0.0689476728439331, ...]</code> | |
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* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss) |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `learning_rate`: 0.0001 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `load_best_model_at_end`: True |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 0.0001 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: None |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `include_for_metrics`: [] |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | negative_mse | sts-test_spearman_cosine | |
|
|:----------:|:----------:|:-------------:|:---------------:|:-----------------------:|:------------:|:------------------------:| |
|
| 0 | 0 | - | - | 0.0074 | -0.1913 | - | |
|
| 0.0017 | 500 | - | 0.0009 | 0.3279 | -0.0860 | - | |
|
| 0.0033 | 1000 | 0.001 | 0.0007 | 0.5478 | -0.0651 | - | |
|
| 0.0050 | 1500 | - | 0.0006 | 0.6221 | -0.0573 | - | |
|
| 0.0067 | 2000 | 0.0007 | 0.0005 | 0.6635 | -0.0523 | - | |
|
| 0.0083 | 2500 | - | 0.0005 | 0.6916 | -0.0485 | - | |
|
| 0.0100 | 3000 | 0.0006 | 0.0005 | 0.7148 | -0.0455 | - | |
|
| 0.0117 | 3500 | - | 0.0004 | 0.7319 | -0.0429 | - | |
|
| 0.0133 | 4000 | 0.0005 | 0.0004 | 0.7485 | -0.0406 | - | |
|
| 0.0150 | 4500 | - | 0.0004 | 0.7622 | -0.0385 | - | |
|
| 0.0167 | 5000 | 0.0005 | 0.0004 | 0.7722 | -0.0368 | - | |
|
| 0.0183 | 5500 | - | 0.0004 | 0.7856 | -0.0352 | - | |
|
| 0.0200 | 6000 | 0.0004 | 0.0003 | 0.7999 | -0.0336 | - | |
|
| 0.0217 | 6500 | - | 0.0003 | 0.8074 | -0.0323 | - | |
|
| 0.0233 | 7000 | 0.0004 | 0.0003 | 0.8155 | -0.0311 | - | |
|
| 0.0250 | 7500 | - | 0.0003 | 0.8237 | -0.0299 | - | |
|
| 0.0267 | 8000 | 0.0004 | 0.0003 | 0.8308 | -0.0289 | - | |
|
| 0.0283 | 8500 | - | 0.0003 | 0.8322 | -0.0280 | - | |
|
| 0.0300 | 9000 | 0.0004 | 0.0003 | 0.8409 | -0.0270 | - | |
|
| 0.0317 | 9500 | - | 0.0003 | 0.8446 | -0.0262 | - | |
|
| 0.0333 | 10000 | 0.0003 | 0.0003 | 0.8513 | -0.0254 | - | |
|
| 0.0350 | 10500 | - | 0.0002 | 0.8519 | -0.0247 | - | |
|
| 0.0367 | 11000 | 0.0003 | 0.0002 | 0.8591 | -0.0240 | - | |
|
| 0.0383 | 11500 | - | 0.0002 | 0.8623 | -0.0233 | - | |
|
| 0.0400 | 12000 | 0.0003 | 0.0002 | 0.8674 | -0.0228 | - | |
|
| 0.0416 | 12500 | - | 0.0002 | 0.8659 | -0.0222 | - | |
|
| 0.0433 | 13000 | 0.0003 | 0.0002 | 0.8724 | -0.0215 | - | |
|
| 0.0450 | 13500 | - | 0.0002 | 0.8725 | -0.0212 | - | |
|
| 0.0466 | 14000 | 0.0003 | 0.0002 | 0.8793 | -0.0206 | - | |
|
| 0.0483 | 14500 | - | 0.0002 | 0.8834 | -0.0202 | - | |
|
| 0.0500 | 15000 | 0.0003 | 0.0002 | 0.8817 | -0.0197 | - | |
|
| 0.0516 | 15500 | - | 0.0002 | 0.8860 | -0.0194 | - | |
|
| 0.0533 | 16000 | 0.0003 | 0.0002 | 0.8842 | -0.0188 | - | |
|
| 0.0550 | 16500 | - | 0.0002 | 0.8893 | -0.0185 | - | |
|
| 0.0566 | 17000 | 0.0002 | 0.0002 | 0.8880 | -0.0181 | - | |
|
| 0.0583 | 17500 | - | 0.0002 | 0.8932 | -0.0179 | - | |
|
| 0.0600 | 18000 | 0.0002 | 0.0002 | 0.8913 | -0.0176 | - | |
|
| 0.0616 | 18500 | - | 0.0002 | 0.8963 | -0.0172 | - | |
|
| 0.0633 | 19000 | 0.0002 | 0.0002 | 0.8915 | -0.0170 | - | |
|
| 0.0650 | 19500 | - | 0.0002 | 0.8969 | -0.0167 | - | |
|
| 0.0666 | 20000 | 0.0002 | 0.0002 | 0.8984 | -0.0165 | - | |
|
| 0.0683 | 20500 | - | 0.0002 | 0.9021 | -0.0162 | - | |
|
| 0.0700 | 21000 | 0.0002 | 0.0002 | 0.9027 | -0.0160 | - | |
|
| 0.0716 | 21500 | - | 0.0002 | 0.9018 | -0.0158 | - | |
|
| 0.0733 | 22000 | 0.0002 | 0.0002 | 0.9043 | -0.0156 | - | |
|
| 0.0750 | 22500 | - | 0.0002 | 0.9028 | -0.0154 | - | |
|
| 0.0766 | 23000 | 0.0002 | 0.0002 | 0.9024 | -0.0153 | - | |
|
| 0.0783 | 23500 | - | 0.0002 | 0.9049 | -0.0152 | - | |
|
| 0.0800 | 24000 | 0.0002 | 0.0001 | 0.9087 | -0.0150 | - | |
|
| 0.0816 | 24500 | - | 0.0001 | 0.9079 | -0.0148 | - | |
|
| 0.0833 | 25000 | 0.0002 | 0.0001 | 0.9080 | -0.0147 | - | |
|
| 0.0850 | 25500 | - | 0.0001 | 0.9096 | -0.0145 | - | |
|
| 0.0866 | 26000 | 0.0002 | 0.0001 | 0.9061 | -0.0145 | - | |
|
| 0.0883 | 26500 | - | 0.0001 | 0.9098 | -0.0143 | - | |
|
| 0.0900 | 27000 | 0.0002 | 0.0001 | 0.9122 | -0.0142 | - | |
|
| 0.0916 | 27500 | - | 0.0001 | 0.9131 | -0.0140 | - | |
|
| 0.0933 | 28000 | 0.0002 | 0.0001 | 0.9114 | -0.0139 | - | |
|
| 0.0950 | 28500 | - | 0.0001 | 0.9126 | -0.0139 | - | |
|
| 0.0966 | 29000 | 0.0002 | 0.0001 | 0.9163 | -0.0138 | - | |
|
| 0.0983 | 29500 | - | 0.0001 | 0.9140 | -0.0137 | - | |
|
| 0.1000 | 30000 | 0.0002 | 0.0001 | 0.9141 | -0.0136 | - | |
|
| 0.1016 | 30500 | - | 0.0001 | 0.9163 | -0.0135 | - | |
|
| 0.1033 | 31000 | 0.0002 | 0.0001 | 0.9159 | -0.0135 | - | |
|
| 0.1050 | 31500 | - | 0.0001 | 0.9153 | -0.0132 | - | |
|
| 0.1066 | 32000 | 0.0002 | 0.0001 | 0.9194 | -0.0131 | - | |
|
| 0.1083 | 32500 | - | 0.0001 | 0.9203 | -0.0131 | - | |
|
| 0.1100 | 33000 | 0.0002 | 0.0001 | 0.9187 | -0.0129 | - | |
|
| 0.1116 | 33500 | - | 0.0001 | 0.9218 | -0.0129 | - | |
|
| 0.1133 | 34000 | 0.0002 | 0.0001 | 0.9204 | -0.0127 | - | |
|
| 0.1150 | 34500 | - | 0.0001 | 0.9216 | -0.0127 | - | |
|
| 0.1166 | 35000 | 0.0002 | 0.0001 | 0.9232 | -0.0125 | - | |
|
| 0.1183 | 35500 | - | 0.0001 | 0.9212 | -0.0125 | - | |
|
| 0.1200 | 36000 | 0.0002 | 0.0001 | 0.9227 | -0.0125 | - | |
|
| 0.1216 | 36500 | - | 0.0001 | 0.9233 | -0.0124 | - | |
|
| 0.1233 | 37000 | 0.0002 | 0.0001 | 0.9261 | -0.0123 | - | |
|
| 0.1249 | 37500 | - | 0.0001 | 0.9256 | -0.0122 | - | |
|
| 0.1266 | 38000 | 0.0002 | 0.0001 | 0.9273 | -0.0121 | - | |
|
| 0.1283 | 38500 | - | 0.0001 | 0.9274 | -0.0120 | - | |
|
| 0.1299 | 39000 | 0.0002 | 0.0001 | 0.9273 | -0.0119 | - | |
|
| 0.1316 | 39500 | - | 0.0001 | 0.9287 | -0.0119 | - | |
|
| 0.1333 | 40000 | 0.0002 | 0.0001 | 0.9266 | -0.0118 | - | |
|
| 0.1349 | 40500 | - | 0.0001 | 0.9283 | -0.0118 | - | |
|
| 0.1366 | 41000 | 0.0002 | 0.0001 | 0.9307 | -0.0117 | - | |
|
| 0.1383 | 41500 | - | 0.0001 | 0.9277 | -0.0117 | - | |
|
| 0.1399 | 42000 | 0.0002 | 0.0001 | 0.9281 | -0.0115 | - | |
|
| 0.1416 | 42500 | - | 0.0001 | 0.9299 | -0.0115 | - | |
|
| 0.1433 | 43000 | 0.0002 | 0.0001 | 0.9306 | -0.0115 | - | |
|
| 0.1449 | 43500 | - | 0.0001 | 0.9301 | -0.0114 | - | |
|
| 0.1466 | 44000 | 0.0002 | 0.0001 | 0.9302 | -0.0114 | - | |
|
| 0.1483 | 44500 | - | 0.0001 | 0.9321 | -0.0114 | - | |
|
| 0.1499 | 45000 | 0.0002 | 0.0001 | 0.9320 | -0.0113 | - | |
|
| 0.1516 | 45500 | - | 0.0001 | 0.9333 | -0.0112 | - | |
|
| 0.1533 | 46000 | 0.0002 | 0.0001 | 0.9343 | -0.0111 | - | |
|
| 0.1549 | 46500 | - | 0.0001 | 0.9315 | -0.0111 | - | |
|
| 0.1566 | 47000 | 0.0002 | 0.0001 | 0.9326 | -0.0111 | - | |
|
| 0.1583 | 47500 | - | 0.0001 | 0.9324 | -0.0110 | - | |
|
| 0.1599 | 48000 | 0.0001 | 0.0001 | 0.9362 | -0.0110 | - | |
|
| 0.1616 | 48500 | - | 0.0001 | 0.9370 | -0.0109 | - | |
|
| 0.1633 | 49000 | 0.0001 | 0.0001 | 0.9348 | -0.0109 | - | |
|
| 0.1649 | 49500 | - | 0.0001 | 0.9352 | -0.0108 | - | |
|
| 0.1666 | 50000 | 0.0001 | 0.0001 | 0.9364 | -0.0107 | - | |
|
| 0.1683 | 50500 | - | 0.0001 | 0.9351 | -0.0107 | - | |
|
| 0.1699 | 51000 | 0.0001 | 0.0001 | 0.9372 | -0.0108 | - | |
|
| 0.1716 | 51500 | - | 0.0001 | 0.9357 | -0.0108 | - | |
|
| 0.1733 | 52000 | 0.0001 | 0.0001 | 0.9384 | -0.0106 | - | |
|
| 0.1749 | 52500 | - | 0.0001 | 0.9366 | -0.0106 | - | |
|
| 0.1766 | 53000 | 0.0001 | 0.0001 | 0.9375 | -0.0106 | - | |
|
| 0.1783 | 53500 | - | 0.0001 | 0.9381 | -0.0105 | - | |
|
| 0.1799 | 54000 | 0.0001 | 0.0001 | 0.9382 | -0.0105 | - | |
|
| 0.1816 | 54500 | - | 0.0001 | 0.9368 | -0.0106 | - | |
|
| 0.1833 | 55000 | 0.0001 | 0.0001 | 0.9383 | -0.0105 | - | |
|
| 0.1849 | 55500 | - | 0.0001 | 0.9393 | -0.0104 | - | |
|
| 0.1866 | 56000 | 0.0001 | 0.0001 | 0.9383 | -0.0104 | - | |
|
| 0.1883 | 56500 | - | 0.0001 | 0.9397 | -0.0104 | - | |
|
| 0.1899 | 57000 | 0.0001 | 0.0001 | 0.9404 | -0.0103 | - | |
|
| 0.1916 | 57500 | - | 0.0001 | 0.9378 | -0.0103 | - | |
|
| 0.1933 | 58000 | 0.0001 | 0.0001 | 0.9379 | -0.0103 | - | |
|
| 0.1949 | 58500 | - | 0.0001 | 0.9397 | -0.0102 | - | |
|
| 0.1966 | 59000 | 0.0001 | 0.0001 | 0.9406 | -0.0102 | - | |
|
| 0.1983 | 59500 | - | 0.0001 | 0.9402 | -0.0102 | - | |
|
| 0.1999 | 60000 | 0.0001 | 0.0001 | 0.9408 | -0.0101 | - | |
|
| 0.2016 | 60500 | - | 0.0001 | 0.9410 | -0.0101 | - | |
|
| 0.2033 | 61000 | 0.0001 | 0.0001 | 0.9409 | -0.0101 | - | |
|
| 0.2049 | 61500 | - | 0.0001 | 0.9405 | -0.0101 | - | |
|
| 0.2066 | 62000 | 0.0001 | 0.0001 | 0.9424 | -0.0100 | - | |
|
| 0.2082 | 62500 | - | 0.0001 | 0.9378 | -0.0101 | - | |
|
| 0.2099 | 63000 | 0.0001 | 0.0001 | 0.9408 | -0.0099 | - | |
|
| 0.2116 | 63500 | - | 0.0001 | 0.9404 | -0.0100 | - | |
|
| 0.2132 | 64000 | 0.0001 | 0.0001 | 0.9397 | -0.0099 | - | |
|
| 0.2149 | 64500 | - | 0.0001 | 0.9411 | -0.0099 | - | |
|
| 0.2166 | 65000 | 0.0001 | 0.0001 | 0.9401 | -0.0099 | - | |
|
| 0.2182 | 65500 | - | 0.0001 | 0.9415 | -0.0098 | - | |
|
| 0.2199 | 66000 | 0.0001 | 0.0001 | 0.9413 | -0.0098 | - | |
|
| 0.2216 | 66500 | - | 0.0001 | 0.9417 | -0.0098 | - | |
|
| 0.2232 | 67000 | 0.0001 | 0.0001 | 0.9411 | -0.0097 | - | |
|
| 0.2249 | 67500 | - | 0.0001 | 0.9423 | -0.0097 | - | |
|
| 0.2266 | 68000 | 0.0001 | 0.0001 | 0.9424 | -0.0097 | - | |
|
| 0.2282 | 68500 | - | 0.0001 | 0.9424 | -0.0098 | - | |
|
| 0.2299 | 69000 | 0.0001 | 0.0001 | 0.9439 | -0.0096 | - | |
|
| 0.2316 | 69500 | - | 0.0001 | 0.9423 | -0.0097 | - | |
|
| 0.2332 | 70000 | 0.0001 | 0.0001 | 0.9420 | -0.0096 | - | |
|
| 0.2349 | 70500 | - | 0.0001 | 0.9429 | -0.0096 | - | |
|
| 0.2366 | 71000 | 0.0001 | 0.0001 | 0.9440 | -0.0096 | - | |
|
| 0.2382 | 71500 | - | 0.0001 | 0.9425 | -0.0096 | - | |
|
| 0.2399 | 72000 | 0.0001 | 0.0001 | 0.9438 | -0.0096 | - | |
|
| 0.2416 | 72500 | - | 0.0001 | 0.9442 | -0.0095 | - | |
|
| 0.2432 | 73000 | 0.0001 | 0.0001 | 0.9451 | -0.0095 | - | |
|
| 0.2449 | 73500 | - | 0.0001 | 0.9432 | -0.0095 | - | |
|
| 0.2466 | 74000 | 0.0001 | 0.0001 | 0.9441 | -0.0095 | - | |
|
| 0.2482 | 74500 | - | 0.0001 | 0.9442 | -0.0094 | - | |
|
| 0.2499 | 75000 | 0.0001 | 0.0001 | 0.9436 | -0.0094 | - | |
|
| 0.2516 | 75500 | - | 0.0001 | 0.9450 | -0.0094 | - | |
|
| 0.2532 | 76000 | 0.0001 | 0.0001 | 0.9455 | -0.0094 | - | |
|
| 0.2549 | 76500 | - | 0.0001 | 0.9439 | -0.0094 | - | |
|
| 0.2566 | 77000 | 0.0001 | 0.0001 | 0.9444 | -0.0094 | - | |
|
| 0.2582 | 77500 | - | 0.0001 | 0.9449 | -0.0093 | - | |
|
| 0.2599 | 78000 | 0.0001 | 0.0001 | 0.9444 | -0.0093 | - | |
|
| 0.2616 | 78500 | - | 0.0001 | 0.9454 | -0.0093 | - | |
|
| 0.2632 | 79000 | 0.0001 | 0.0001 | 0.9452 | -0.0093 | - | |
|
| 0.2649 | 79500 | - | 0.0001 | 0.9465 | -0.0093 | - | |
|
| 0.2666 | 80000 | 0.0001 | 0.0001 | 0.9450 | -0.0093 | - | |
|
| 0.2682 | 80500 | - | 0.0001 | 0.9467 | -0.0092 | - | |
|
| 0.2699 | 81000 | 0.0001 | 0.0001 | 0.9470 | -0.0092 | - | |
|
| 0.2716 | 81500 | - | 0.0001 | 0.9447 | -0.0092 | - | |
|
| 0.2732 | 82000 | 0.0001 | 0.0001 | 0.9477 | -0.0092 | - | |
|
| 0.2749 | 82500 | - | 0.0001 | 0.9442 | -0.0092 | - | |
|
| 0.2766 | 83000 | 0.0001 | 0.0001 | 0.9482 | -0.0091 | - | |
|
| 0.2782 | 83500 | - | 0.0001 | 0.9475 | -0.0091 | - | |
|
| 0.2799 | 84000 | 0.0001 | 0.0001 | 0.9451 | -0.0091 | - | |
|
| 0.2816 | 84500 | - | 0.0001 | 0.9471 | -0.0091 | - | |
|
| 0.2832 | 85000 | 0.0001 | 0.0001 | 0.9470 | -0.0090 | - | |
|
| 0.2849 | 85500 | - | 0.0001 | 0.9468 | -0.0091 | - | |
|
| 0.2865 | 86000 | 0.0001 | 0.0001 | 0.9464 | -0.0090 | - | |
|
| 0.2882 | 86500 | - | 0.0001 | 0.9482 | -0.0090 | - | |
|
| 0.2899 | 87000 | 0.0001 | 0.0001 | 0.9466 | -0.0090 | - | |
|
| 0.2915 | 87500 | - | 0.0001 | 0.9474 | -0.0090 | - | |
|
| 0.2932 | 88000 | 0.0001 | 0.0001 | 0.9476 | -0.0090 | - | |
|
| 0.2949 | 88500 | - | 0.0001 | 0.9480 | -0.0089 | - | |
|
| 0.2965 | 89000 | 0.0001 | 0.0001 | 0.9489 | -0.0090 | - | |
|
| 0.2982 | 89500 | - | 0.0001 | 0.9475 | -0.0089 | - | |
|
| 0.2999 | 90000 | 0.0001 | 0.0001 | 0.9483 | -0.0089 | - | |
|
| 0.3015 | 90500 | - | 0.0001 | 0.9478 | -0.0089 | - | |
|
| 0.3032 | 91000 | 0.0001 | 0.0001 | 0.9471 | -0.0090 | - | |
|
| 0.3049 | 91500 | - | 0.0001 | 0.9470 | -0.0089 | - | |
|
| 0.3065 | 92000 | 0.0001 | 0.0001 | 0.9472 | -0.0089 | - | |
|
| 0.3082 | 92500 | - | 0.0001 | 0.9485 | -0.0089 | - | |
|
| 0.3099 | 93000 | 0.0001 | 0.0001 | 0.9468 | -0.0089 | - | |
|
| 0.3115 | 93500 | - | 0.0001 | 0.9484 | -0.0088 | - | |
|
| 0.3132 | 94000 | 0.0001 | 0.0001 | 0.9482 | -0.0088 | - | |
|
| 0.3149 | 94500 | - | 0.0001 | 0.9503 | -0.0088 | - | |
|
| 0.3165 | 95000 | 0.0001 | 0.0001 | 0.9485 | -0.0088 | - | |
|
| 0.3182 | 95500 | - | 0.0001 | 0.9509 | -0.0087 | - | |
|
| 0.3199 | 96000 | 0.0001 | 0.0001 | 0.9492 | -0.0088 | - | |
|
| 0.3215 | 96500 | - | 0.0001 | 0.9488 | -0.0087 | - | |
|
| 0.3232 | 97000 | 0.0001 | 0.0001 | 0.9500 | -0.0087 | - | |
|
| 0.3249 | 97500 | - | 0.0001 | 0.9495 | -0.0087 | - | |
|
| 0.3265 | 98000 | 0.0001 | 0.0001 | 0.9499 | -0.0087 | - | |
|
| 0.3282 | 98500 | - | 0.0001 | 0.9496 | -0.0087 | - | |
|
| 0.3299 | 99000 | 0.0001 | 0.0001 | 0.9493 | -0.0087 | - | |
|
| 0.3315 | 99500 | - | 0.0001 | 0.9497 | -0.0087 | - | |
|
| 0.3332 | 100000 | 0.0001 | 0.0001 | 0.9511 | -0.0086 | - | |
|
| 0.3349 | 100500 | - | 0.0001 | 0.9508 | -0.0086 | - | |
|
| 0.3365 | 101000 | 0.0001 | 0.0001 | 0.9502 | -0.0086 | - | |
|
| 0.3382 | 101500 | - | 0.0001 | 0.9488 | -0.0087 | - | |
|
| 0.3399 | 102000 | 0.0001 | 0.0001 | 0.9505 | -0.0086 | - | |
|
| 0.3415 | 102500 | - | 0.0001 | 0.9497 | -0.0086 | - | |
|
| 0.3432 | 103000 | 0.0001 | 0.0001 | 0.9500 | -0.0085 | - | |
|
| 0.3449 | 103500 | - | 0.0001 | 0.9497 | -0.0086 | - | |
|
| 0.3465 | 104000 | 0.0001 | 0.0001 | 0.9521 | -0.0085 | - | |
|
| 0.3482 | 104500 | - | 0.0001 | 0.9499 | -0.0085 | - | |
|
| 0.3499 | 105000 | 0.0001 | 0.0001 | 0.9488 | -0.0085 | - | |
|
| 0.3515 | 105500 | - | 0.0001 | 0.9490 | -0.0085 | - | |
|
| 0.3532 | 106000 | 0.0001 | 0.0001 | 0.9503 | -0.0085 | - | |
|
| 0.3549 | 106500 | - | 0.0001 | 0.9504 | -0.0085 | - | |
|
| 0.3565 | 107000 | 0.0001 | 0.0001 | 0.9503 | -0.0085 | - | |
|
| 0.3582 | 107500 | - | 0.0001 | 0.9514 | -0.0085 | - | |
|
| 0.3599 | 108000 | 0.0001 | 0.0001 | 0.9509 | -0.0084 | - | |
|
| 0.3615 | 108500 | - | 0.0001 | 0.9513 | -0.0084 | - | |
|
| 0.3632 | 109000 | 0.0001 | 0.0001 | 0.9512 | -0.0084 | - | |
|
| 0.3649 | 109500 | - | 0.0001 | 0.9515 | -0.0084 | - | |
|
| 0.3665 | 110000 | 0.0001 | 0.0001 | 0.9509 | -0.0084 | - | |
|
| 0.3682 | 110500 | - | 0.0001 | 0.9495 | -0.0084 | - | |
|
| 0.3698 | 111000 | 0.0001 | 0.0001 | 0.9507 | -0.0084 | - | |
|
| 0.3715 | 111500 | - | 0.0001 | 0.9512 | -0.0083 | - | |
|
| 0.3732 | 112000 | 0.0001 | 0.0001 | 0.9519 | -0.0084 | - | |
|
| 0.3748 | 112500 | - | 0.0001 | 0.9512 | -0.0084 | - | |
|
| 0.3765 | 113000 | 0.0001 | 0.0001 | 0.9511 | -0.0083 | - | |
|
| 0.3782 | 113500 | - | 0.0001 | 0.9513 | -0.0083 | - | |
|
| 0.3798 | 114000 | 0.0001 | 0.0001 | 0.9512 | -0.0084 | - | |
|
| 0.3815 | 114500 | - | 0.0001 | 0.9501 | -0.0083 | - | |
|
| 0.3832 | 115000 | 0.0001 | 0.0001 | 0.9515 | -0.0083 | - | |
|
| 0.3848 | 115500 | - | 0.0001 | 0.9526 | -0.0083 | - | |
|
| 0.3865 | 116000 | 0.0001 | 0.0001 | 0.9518 | -0.0083 | - | |
|
| 0.3882 | 116500 | - | 0.0001 | 0.9521 | -0.0083 | - | |
|
| 0.3898 | 117000 | 0.0001 | 0.0001 | 0.9515 | -0.0083 | - | |
|
| 0.3915 | 117500 | - | 0.0001 | 0.9515 | -0.0083 | - | |
|
| 0.3932 | 118000 | 0.0001 | 0.0001 | 0.9530 | -0.0082 | - | |
|
| 0.3948 | 118500 | - | 0.0001 | 0.9533 | -0.0082 | - | |
|
| 0.3965 | 119000 | 0.0001 | 0.0001 | 0.9523 | -0.0082 | - | |
|
| 0.3982 | 119500 | - | 0.0001 | 0.9520 | -0.0082 | - | |
|
| 0.3998 | 120000 | 0.0001 | 0.0001 | 0.9511 | -0.0082 | - | |
|
| 0.4015 | 120500 | - | 0.0001 | 0.9530 | -0.0083 | - | |
|
| 0.4032 | 121000 | 0.0001 | 0.0001 | 0.9525 | -0.0082 | - | |
|
| 0.4048 | 121500 | - | 0.0001 | 0.9526 | -0.0082 | - | |
|
| 0.4065 | 122000 | 0.0001 | 0.0001 | 0.9527 | -0.0082 | - | |
|
| 0.4082 | 122500 | - | 0.0001 | 0.9522 | -0.0082 | - | |
|
| 0.4098 | 123000 | 0.0001 | 0.0001 | 0.9535 | -0.0081 | - | |
|
| 0.4115 | 123500 | - | 0.0001 | 0.9527 | -0.0081 | - | |
|
| 0.4132 | 124000 | 0.0001 | 0.0001 | 0.9530 | -0.0082 | - | |
|
| 0.4148 | 124500 | - | 0.0001 | 0.9520 | -0.0082 | - | |
|
| 0.4165 | 125000 | 0.0001 | 0.0001 | 0.9526 | -0.0081 | - | |
|
| 0.4182 | 125500 | - | 0.0001 | 0.9528 | -0.0081 | - | |
|
| 0.4198 | 126000 | 0.0001 | 0.0001 | 0.9535 | -0.0081 | - | |
|
| 0.4215 | 126500 | - | 0.0001 | 0.9530 | -0.0081 | - | |
|
| 0.4232 | 127000 | 0.0001 | 0.0001 | 0.9539 | -0.0081 | - | |
|
| 0.4248 | 127500 | - | 0.0001 | 0.9531 | -0.0081 | - | |
|
| 0.4265 | 128000 | 0.0001 | 0.0001 | 0.9540 | -0.0081 | - | |
|
| 0.4282 | 128500 | - | 0.0001 | 0.9534 | -0.0081 | - | |
|
| 0.4298 | 129000 | 0.0001 | 0.0001 | 0.9536 | -0.0080 | - | |
|
| 0.4315 | 129500 | - | 0.0001 | 0.9536 | -0.0081 | - | |
|
| 0.4332 | 130000 | 0.0001 | 0.0001 | 0.9547 | -0.0080 | - | |
|
| 0.4348 | 130500 | - | 0.0001 | 0.9535 | -0.0080 | - | |
|
| 0.4365 | 131000 | 0.0001 | 0.0001 | 0.9541 | -0.0080 | - | |
|
| 0.4382 | 131500 | - | 0.0001 | 0.9542 | -0.0080 | - | |
|
| 0.4398 | 132000 | 0.0001 | 0.0001 | 0.9540 | -0.0080 | - | |
|
| 0.4415 | 132500 | - | 0.0001 | 0.9537 | -0.0080 | - | |
|
| 0.4432 | 133000 | 0.0001 | 0.0001 | 0.9538 | -0.0080 | - | |
|
| 0.4448 | 133500 | - | 0.0001 | 0.9540 | -0.0079 | - | |
|
| 0.4465 | 134000 | 0.0001 | 0.0001 | 0.9540 | -0.0080 | - | |
|
| 0.4481 | 134500 | - | 0.0001 | 0.9544 | -0.0080 | - | |
|
| 0.4498 | 135000 | 0.0001 | 0.0001 | 0.9535 | -0.0079 | - | |
|
| 0.4515 | 135500 | - | 0.0001 | 0.9541 | -0.0079 | - | |
|
| 0.4531 | 136000 | 0.0001 | 0.0001 | 0.9546 | -0.0079 | - | |
|
| 0.4548 | 136500 | - | 0.0001 | 0.9543 | -0.0079 | - | |
|
| 0.4565 | 137000 | 0.0001 | 0.0001 | 0.9548 | -0.0079 | - | |
|
| 0.4581 | 137500 | - | 0.0001 | 0.9555 | -0.0079 | - | |
|
| 0.4598 | 138000 | 0.0001 | 0.0001 | 0.9548 | -0.0079 | - | |
|
| 0.4615 | 138500 | - | 0.0001 | 0.9542 | -0.0079 | - | |
|
| 0.4631 | 139000 | 0.0001 | 0.0001 | 0.9548 | -0.0079 | - | |
|
| 0.4648 | 139500 | - | 0.0001 | 0.9544 | -0.0079 | - | |
|
| 0.4665 | 140000 | 0.0001 | 0.0001 | 0.9546 | -0.0079 | - | |
|
| 0.4681 | 140500 | - | 0.0001 | 0.9553 | -0.0078 | - | |
|
| 0.4698 | 141000 | 0.0001 | 0.0001 | 0.9542 | -0.0078 | - | |
|
| 0.4715 | 141500 | - | 0.0001 | 0.9553 | -0.0078 | - | |
|
| 0.4731 | 142000 | 0.0001 | 0.0001 | 0.9548 | -0.0079 | - | |
|
| 0.4748 | 142500 | - | 0.0001 | 0.9545 | -0.0078 | - | |
|
| 0.4765 | 143000 | 0.0001 | 0.0001 | 0.9553 | -0.0079 | - | |
|
| 0.4781 | 143500 | - | 0.0001 | 0.9561 | -0.0078 | - | |
|
| 0.4798 | 144000 | 0.0001 | 0.0001 | 0.9551 | -0.0078 | - | |
|
| 0.4815 | 144500 | - | 0.0001 | 0.9550 | -0.0078 | - | |
|
| 0.4831 | 145000 | 0.0001 | 0.0001 | 0.9557 | -0.0078 | - | |
|
| 0.4848 | 145500 | - | 0.0001 | 0.9557 | -0.0077 | - | |
|
| 0.4865 | 146000 | 0.0001 | 0.0001 | 0.9552 | -0.0077 | - | |
|
| 0.4881 | 146500 | - | 0.0001 | 0.9553 | -0.0078 | - | |
|
| 0.4898 | 147000 | 0.0001 | 0.0001 | 0.9555 | -0.0077 | - | |
|
| 0.4915 | 147500 | - | 0.0001 | 0.9561 | -0.0077 | - | |
|
| 0.4931 | 148000 | 0.0001 | 0.0001 | 0.9558 | -0.0077 | - | |
|
| 0.4948 | 148500 | - | 0.0001 | 0.9558 | -0.0077 | - | |
|
| 0.4965 | 149000 | 0.0001 | 0.0001 | 0.9560 | -0.0077 | - | |
|
| 0.4981 | 149500 | - | 0.0001 | 0.9558 | -0.0077 | - | |
|
| 0.4998 | 150000 | 0.0001 | 0.0001 | 0.9553 | -0.0077 | - | |
|
| 0.5015 | 150500 | - | 0.0001 | 0.9557 | -0.0077 | - | |
|
| 0.5031 | 151000 | 0.0001 | 0.0001 | 0.9562 | -0.0077 | - | |
|
| 0.5048 | 151500 | - | 0.0001 | 0.9558 | -0.0077 | - | |
|
| 0.5065 | 152000 | 0.0001 | 0.0001 | 0.9553 | -0.0077 | - | |
|
| 0.5081 | 152500 | - | 0.0001 | 0.9553 | -0.0076 | - | |
|
| 0.5098 | 153000 | 0.0001 | 0.0001 | 0.9559 | -0.0077 | - | |
|
| 0.5115 | 153500 | - | 0.0001 | 0.9560 | -0.0076 | - | |
|
| 0.5131 | 154000 | 0.0001 | 0.0001 | 0.9557 | -0.0076 | - | |
|
| 0.5148 | 154500 | - | 0.0001 | 0.9563 | -0.0076 | - | |
|
| 0.5165 | 155000 | 0.0001 | 0.0001 | 0.9567 | -0.0076 | - | |
|
| 0.5181 | 155500 | - | 0.0001 | 0.9559 | -0.0076 | - | |
|
| 0.5198 | 156000 | 0.0001 | 0.0001 | 0.9565 | -0.0076 | - | |
|
| 0.5215 | 156500 | - | 0.0001 | 0.9563 | -0.0076 | - | |
|
| 0.5231 | 157000 | 0.0001 | 0.0001 | 0.9569 | -0.0076 | - | |
|
| 0.5248 | 157500 | - | 0.0001 | 0.9571 | -0.0076 | - | |
|
| 0.5265 | 158000 | 0.0001 | 0.0001 | 0.9560 | -0.0076 | - | |
|
| 0.5281 | 158500 | - | 0.0001 | 0.9562 | -0.0076 | - | |
|
| 0.5298 | 159000 | 0.0001 | 0.0001 | 0.9569 | -0.0076 | - | |
|
| 0.5314 | 159500 | - | 0.0001 | 0.9556 | -0.0076 | - | |
|
| 0.5331 | 160000 | 0.0001 | 0.0001 | 0.9560 | -0.0075 | - | |
|
| 0.5348 | 160500 | - | 0.0001 | 0.9555 | -0.0075 | - | |
|
| 0.5364 | 161000 | 0.0001 | 0.0001 | 0.9555 | -0.0076 | - | |
|
| 0.5381 | 161500 | - | 0.0001 | 0.9564 | -0.0075 | - | |
|
| 0.5398 | 162000 | 0.0001 | 0.0001 | 0.9574 | -0.0076 | - | |
|
| 0.5414 | 162500 | - | 0.0001 | 0.9569 | -0.0075 | - | |
|
| 0.5431 | 163000 | 0.0001 | 0.0001 | 0.9578 | -0.0075 | - | |
|
| 0.5448 | 163500 | - | 0.0001 | 0.9571 | -0.0075 | - | |
|
| 0.5464 | 164000 | 0.0001 | 0.0001 | 0.9578 | -0.0075 | - | |
|
| 0.5481 | 164500 | - | 0.0001 | 0.9580 | -0.0075 | - | |
|
| 0.5498 | 165000 | 0.0001 | 0.0001 | 0.9568 | -0.0075 | - | |
|
| 0.5514 | 165500 | - | 0.0001 | 0.9582 | -0.0075 | - | |
|
| 0.5531 | 166000 | 0.0001 | 0.0001 | 0.9578 | -0.0075 | - | |
|
| 0.5548 | 166500 | - | 0.0001 | 0.9569 | -0.0075 | - | |
|
| 0.5564 | 167000 | 0.0001 | 0.0001 | 0.9568 | -0.0075 | - | |
|
| 0.5581 | 167500 | - | 0.0001 | 0.9576 | -0.0075 | - | |
|
| 0.5598 | 168000 | 0.0001 | 0.0001 | 0.9581 | -0.0075 | - | |
|
| 0.5614 | 168500 | - | 0.0001 | 0.9581 | -0.0075 | - | |
|
| 0.5631 | 169000 | 0.0001 | 0.0001 | 0.9573 | -0.0075 | - | |
|
| 0.5648 | 169500 | - | 0.0001 | 0.9581 | -0.0074 | - | |
|
| 0.5664 | 170000 | 0.0001 | 0.0001 | 0.9568 | -0.0074 | - | |
|
| 0.5681 | 170500 | - | 0.0001 | 0.9573 | -0.0075 | - | |
|
| 0.5698 | 171000 | 0.0001 | 0.0001 | 0.9579 | -0.0074 | - | |
|
| 0.5714 | 171500 | - | 0.0001 | 0.9578 | -0.0074 | - | |
|
| 0.5731 | 172000 | 0.0001 | 0.0001 | 0.9581 | -0.0074 | - | |
|
| 0.5748 | 172500 | - | 0.0001 | 0.9567 | -0.0074 | - | |
|
| 0.5764 | 173000 | 0.0001 | 0.0001 | 0.9581 | -0.0074 | - | |
|
| 0.5781 | 173500 | - | 0.0001 | 0.9584 | -0.0074 | - | |
|
| 0.5798 | 174000 | 0.0001 | 0.0001 | 0.9585 | -0.0074 | - | |
|
| 0.5814 | 174500 | - | 0.0001 | 0.9583 | -0.0074 | - | |
|
| 0.5831 | 175000 | 0.0001 | 0.0001 | 0.9590 | -0.0074 | - | |
|
| 0.5848 | 175500 | - | 0.0001 | 0.9580 | -0.0074 | - | |
|
| 0.5864 | 176000 | 0.0001 | 0.0001 | 0.9580 | -0.0073 | - | |
|
| 0.5881 | 176500 | - | 0.0001 | 0.9584 | -0.0073 | - | |
|
| 0.5898 | 177000 | 0.0001 | 0.0001 | 0.9591 | -0.0074 | - | |
|
| 0.5914 | 177500 | - | 0.0001 | 0.9592 | -0.0073 | - | |
|
| 0.5931 | 178000 | 0.0001 | 0.0001 | 0.9582 | -0.0073 | - | |
|
| 0.5948 | 178500 | - | 0.0001 | 0.9585 | -0.0073 | - | |
|
| 0.5964 | 179000 | 0.0001 | 0.0001 | 0.9590 | -0.0074 | - | |
|
| 0.5981 | 179500 | - | 0.0001 | 0.9586 | -0.0073 | - | |
|
| 0.5998 | 180000 | 0.0001 | 0.0001 | 0.9588 | -0.0073 | - | |
|
| 0.6014 | 180500 | - | 0.0001 | 0.9584 | -0.0073 | - | |
|
| 0.6031 | 181000 | 0.0001 | 0.0001 | 0.9588 | -0.0073 | - | |
|
| 0.6048 | 181500 | - | 0.0001 | 0.9581 | -0.0073 | - | |
|
| 0.6064 | 182000 | 0.0001 | 0.0001 | 0.9585 | -0.0073 | - | |
|
| 0.6081 | 182500 | - | 0.0001 | 0.9588 | -0.0073 | - | |
|
| 0.6098 | 183000 | 0.0001 | 0.0001 | 0.9589 | -0.0073 | - | |
|
| 0.6114 | 183500 | - | 0.0001 | 0.9590 | -0.0073 | - | |
|
| 0.6131 | 184000 | 0.0001 | 0.0001 | 0.9592 | -0.0073 | - | |
|
| 0.6147 | 184500 | - | 0.0001 | 0.9585 | -0.0072 | - | |
|
| 0.6164 | 185000 | 0.0001 | 0.0001 | 0.9591 | -0.0073 | - | |
|
| 0.6181 | 185500 | - | 0.0001 | 0.9581 | -0.0072 | - | |
|
| 0.6197 | 186000 | 0.0001 | 0.0001 | 0.9583 | -0.0072 | - | |
|
| 0.6214 | 186500 | - | 0.0001 | 0.9592 | -0.0072 | - | |
|
| 0.6231 | 187000 | 0.0001 | 0.0001 | 0.9594 | -0.0072 | - | |
|
| 0.6247 | 187500 | - | 0.0001 | 0.9596 | -0.0072 | - | |
|
| 0.6264 | 188000 | 0.0001 | 0.0001 | 0.9599 | -0.0072 | - | |
|
| 0.6281 | 188500 | - | 0.0001 | 0.9598 | -0.0072 | - | |
|
| 0.6297 | 189000 | 0.0001 | 0.0001 | 0.9597 | -0.0072 | - | |
|
| 0.6314 | 189500 | - | 0.0001 | 0.9596 | -0.0072 | - | |
|
| 0.6331 | 190000 | 0.0001 | 0.0001 | 0.9603 | -0.0072 | - | |
|
| 0.6347 | 190500 | - | 0.0001 | 0.9600 | -0.0072 | - | |
|
| 0.6364 | 191000 | 0.0001 | 0.0001 | 0.9591 | -0.0072 | - | |
|
| 0.6381 | 191500 | - | 0.0001 | 0.9590 | -0.0072 | - | |
|
| 0.6397 | 192000 | 0.0001 | 0.0001 | 0.9586 | -0.0072 | - | |
|
| 0.6414 | 192500 | - | 0.0001 | 0.9591 | -0.0072 | - | |
|
| 0.6431 | 193000 | 0.0001 | 0.0001 | 0.9595 | -0.0072 | - | |
|
| 0.6447 | 193500 | - | 0.0001 | 0.9599 | -0.0071 | - | |
|
| 0.6464 | 194000 | 0.0001 | 0.0001 | 0.9598 | -0.0072 | - | |
|
| 0.6481 | 194500 | - | 0.0001 | 0.9591 | -0.0072 | - | |
|
| 0.6497 | 195000 | 0.0001 | 0.0001 | 0.9589 | -0.0071 | - | |
|
| 0.6514 | 195500 | - | 0.0001 | 0.9597 | -0.0071 | - | |
|
| 0.6531 | 196000 | 0.0001 | 0.0001 | 0.9596 | -0.0071 | - | |
|
| 0.6547 | 196500 | - | 0.0001 | 0.9602 | -0.0071 | - | |
|
| 0.6564 | 197000 | 0.0001 | 0.0001 | 0.9598 | -0.0071 | - | |
|
| 0.6581 | 197500 | - | 0.0001 | 0.9599 | -0.0071 | - | |
|
| 0.6597 | 198000 | 0.0001 | 0.0001 | 0.9602 | -0.0071 | - | |
|
| 0.6614 | 198500 | - | 0.0001 | 0.9604 | -0.0071 | - | |
|
| 0.6631 | 199000 | 0.0001 | 0.0001 | 0.9601 | -0.0071 | - | |
|
| 0.6647 | 199500 | - | 0.0001 | 0.9606 | -0.0071 | - | |
|
| 0.6664 | 200000 | 0.0001 | 0.0001 | 0.9598 | -0.0071 | - | |
|
| 0.6681 | 200500 | - | 0.0001 | 0.9601 | -0.0071 | - | |
|
| 0.6697 | 201000 | 0.0001 | 0.0001 | 0.9599 | -0.0071 | - | |
|
| 0.6714 | 201500 | - | 0.0001 | 0.9602 | -0.0071 | - | |
|
| 0.6731 | 202000 | 0.0001 | 0.0001 | 0.9595 | -0.0071 | - | |
|
| 0.6747 | 202500 | - | 0.0001 | 0.9607 | -0.0071 | - | |
|
| 0.6764 | 203000 | 0.0001 | 0.0001 | 0.9607 | -0.0071 | - | |
|
| 0.6781 | 203500 | - | 0.0001 | 0.9603 | -0.0071 | - | |
|
| 0.6797 | 204000 | 0.0001 | 0.0001 | 0.9612 | -0.0070 | - | |
|
| 0.6814 | 204500 | - | 0.0001 | 0.9605 | -0.0071 | - | |
|
| 0.6831 | 205000 | 0.0001 | 0.0001 | 0.9611 | -0.0070 | - | |
|
| 0.6847 | 205500 | - | 0.0001 | 0.9607 | -0.0070 | - | |
|
| 0.6864 | 206000 | 0.0001 | 0.0001 | 0.9601 | -0.0070 | - | |
|
| 0.6881 | 206500 | - | 0.0001 | 0.9606 | -0.0070 | - | |
|
| 0.6897 | 207000 | 0.0001 | 0.0001 | 0.9601 | -0.0070 | - | |
|
| 0.6914 | 207500 | - | 0.0001 | 0.9611 | -0.0070 | - | |
|
| 0.6930 | 208000 | 0.0001 | 0.0001 | 0.9613 | -0.0070 | - | |
|
| 0.6947 | 208500 | - | 0.0001 | 0.9607 | -0.0070 | - | |
|
| 0.6964 | 209000 | 0.0001 | 0.0001 | 0.9605 | -0.0070 | - | |
|
| 0.6980 | 209500 | - | 0.0001 | 0.9611 | -0.0070 | - | |
|
| 0.6997 | 210000 | 0.0001 | 0.0001 | 0.9604 | -0.0070 | - | |
|
| 0.7014 | 210500 | - | 0.0001 | 0.9609 | -0.0070 | - | |
|
| 0.7030 | 211000 | 0.0001 | 0.0001 | 0.9611 | -0.0070 | - | |
|
| 0.7047 | 211500 | - | 0.0001 | 0.9611 | -0.0070 | - | |
|
| 0.7064 | 212000 | 0.0001 | 0.0001 | 0.9612 | -0.0070 | - | |
|
| 0.7080 | 212500 | - | 0.0001 | 0.9610 | -0.0070 | - | |
|
| 0.7097 | 213000 | 0.0001 | 0.0001 | 0.9614 | -0.0070 | - | |
|
| 0.7114 | 213500 | - | 0.0001 | 0.9613 | -0.0069 | - | |
|
| 0.7130 | 214000 | 0.0001 | 0.0001 | 0.9619 | -0.0070 | - | |
|
| 0.7147 | 214500 | - | 0.0001 | 0.9612 | -0.0070 | - | |
|
| 0.7164 | 215000 | 0.0001 | 0.0001 | 0.9615 | -0.0069 | - | |
|
| 0.7180 | 215500 | - | 0.0001 | 0.9614 | -0.0069 | - | |
|
| 0.7197 | 216000 | 0.0001 | 0.0001 | 0.9614 | -0.0070 | - | |
|
| 0.7214 | 216500 | - | 0.0001 | 0.9613 | -0.0069 | - | |
|
| 0.7230 | 217000 | 0.0001 | 0.0001 | 0.9612 | -0.0069 | - | |
|
| 0.7247 | 217500 | - | 0.0001 | 0.9608 | -0.0069 | - | |
|
| 0.7264 | 218000 | 0.0001 | 0.0001 | 0.9619 | -0.0069 | - | |
|
| 0.7280 | 218500 | - | 0.0001 | 0.9612 | -0.0069 | - | |
|
| 0.7297 | 219000 | 0.0001 | 0.0001 | 0.9613 | -0.0069 | - | |
|
| 0.7314 | 219500 | - | 0.0001 | 0.9617 | -0.0069 | - | |
|
| 0.7330 | 220000 | 0.0001 | 0.0001 | 0.9620 | -0.0069 | - | |
|
| 0.7347 | 220500 | - | 0.0001 | 0.9621 | -0.0069 | - | |
|
| 0.7364 | 221000 | 0.0001 | 0.0001 | 0.9616 | -0.0069 | - | |
|
| 0.7380 | 221500 | - | 0.0001 | 0.9622 | -0.0069 | - | |
|
| 0.7397 | 222000 | 0.0001 | 0.0001 | 0.9620 | -0.0069 | - | |
|
| 0.7414 | 222500 | - | 0.0001 | 0.9612 | -0.0069 | - | |
|
| 0.7430 | 223000 | 0.0001 | 0.0001 | 0.9615 | -0.0069 | - | |
|
| 0.7447 | 223500 | - | 0.0001 | 0.9615 | -0.0069 | - | |
|
| 0.7464 | 224000 | 0.0001 | 0.0001 | 0.9621 | -0.0069 | - | |
|
| 0.7480 | 224500 | - | 0.0001 | 0.9622 | -0.0068 | - | |
|
| 0.7497 | 225000 | 0.0001 | 0.0001 | 0.9616 | -0.0069 | - | |
|
| 0.7514 | 225500 | - | 0.0001 | 0.9616 | -0.0069 | - | |
|
| 0.7530 | 226000 | 0.0001 | 0.0001 | 0.9614 | -0.0069 | - | |
|
| 0.7547 | 226500 | - | 0.0001 | 0.9614 | -0.0069 | - | |
|
| 0.7564 | 227000 | 0.0001 | 0.0001 | 0.9614 | -0.0068 | - | |
|
| 0.7580 | 227500 | - | 0.0001 | 0.9613 | -0.0069 | - | |
|
| 0.7597 | 228000 | 0.0001 | 0.0001 | 0.9620 | -0.0068 | - | |
|
| 0.7614 | 228500 | - | 0.0001 | 0.9616 | -0.0068 | - | |
|
| 0.7630 | 229000 | 0.0001 | 0.0001 | 0.9621 | -0.0068 | - | |
|
| 0.7647 | 229500 | - | 0.0001 | 0.9620 | -0.0069 | - | |
|
| 0.7664 | 230000 | 0.0001 | 0.0001 | 0.9618 | -0.0068 | - | |
|
| 0.7680 | 230500 | - | 0.0001 | 0.9616 | -0.0068 | - | |
|
| 0.7697 | 231000 | 0.0001 | 0.0001 | 0.9624 | -0.0068 | - | |
|
| 0.7714 | 231500 | - | 0.0001 | 0.9618 | -0.0068 | - | |
|
| 0.7730 | 232000 | 0.0001 | 0.0001 | 0.9621 | -0.0068 | - | |
|
| 0.7747 | 232500 | - | 0.0001 | 0.9618 | -0.0068 | - | |
|
| 0.7763 | 233000 | 0.0001 | 0.0001 | 0.9617 | -0.0068 | - | |
|
| 0.7780 | 233500 | - | 0.0001 | 0.9620 | -0.0068 | - | |
|
| 0.7797 | 234000 | 0.0001 | 0.0001 | 0.9620 | -0.0068 | - | |
|
| 0.7813 | 234500 | - | 0.0001 | 0.9624 | -0.0068 | - | |
|
| 0.7830 | 235000 | 0.0001 | 0.0001 | 0.9624 | -0.0068 | - | |
|
| 0.7847 | 235500 | - | 0.0001 | 0.9624 | -0.0068 | - | |
|
| 0.7863 | 236000 | 0.0001 | 0.0001 | 0.9627 | -0.0068 | - | |
|
| 0.7880 | 236500 | - | 0.0001 | 0.9620 | -0.0068 | - | |
|
| 0.7897 | 237000 | 0.0001 | 0.0001 | 0.9626 | -0.0068 | - | |
|
| 0.7913 | 237500 | - | 0.0001 | 0.9629 | -0.0068 | - | |
|
| 0.7930 | 238000 | 0.0001 | 0.0001 | 0.9621 | -0.0067 | - | |
|
| 0.7947 | 238500 | - | 0.0001 | 0.9630 | -0.0067 | - | |
|
| 0.7963 | 239000 | 0.0001 | 0.0001 | 0.9627 | -0.0067 | - | |
|
| 0.7980 | 239500 | - | 0.0001 | 0.9628 | -0.0068 | - | |
|
| 0.7997 | 240000 | 0.0001 | 0.0001 | 0.9626 | -0.0067 | - | |
|
| 0.8013 | 240500 | - | 0.0001 | 0.9624 | -0.0067 | - | |
|
| 0.8030 | 241000 | 0.0001 | 0.0001 | 0.9623 | -0.0067 | - | |
|
| 0.8047 | 241500 | - | 0.0001 | 0.9622 | -0.0067 | - | |
|
| 0.8063 | 242000 | 0.0001 | 0.0001 | 0.9620 | -0.0067 | - | |
|
| 0.8080 | 242500 | - | 0.0001 | 0.9622 | -0.0067 | - | |
|
| 0.8097 | 243000 | 0.0001 | 0.0001 | 0.9626 | -0.0067 | - | |
|
| 0.8113 | 243500 | - | 0.0001 | 0.9634 | -0.0067 | - | |
|
| 0.8130 | 244000 | 0.0001 | 0.0001 | 0.9623 | -0.0067 | - | |
|
| 0.8147 | 244500 | - | 0.0001 | 0.9632 | -0.0067 | - | |
|
| 0.8163 | 245000 | 0.0001 | 0.0001 | 0.9630 | -0.0067 | - | |
|
| 0.8180 | 245500 | - | 0.0001 | 0.9634 | -0.0067 | - | |
|
| 0.8197 | 246000 | 0.0001 | 0.0001 | 0.9627 | -0.0067 | - | |
|
| 0.8213 | 246500 | - | 0.0001 | 0.9625 | -0.0067 | - | |
|
| 0.8230 | 247000 | 0.0001 | 0.0001 | 0.9629 | -0.0067 | - | |
|
| 0.8247 | 247500 | - | 0.0001 | 0.9633 | -0.0067 | - | |
|
| 0.8263 | 248000 | 0.0001 | 0.0001 | 0.9628 | -0.0067 | - | |
|
| 0.8280 | 248500 | - | 0.0001 | 0.9636 | -0.0067 | - | |
|
| 0.8297 | 249000 | 0.0001 | 0.0001 | 0.9632 | -0.0067 | - | |
|
| 0.8313 | 249500 | - | 0.0001 | 0.9630 | -0.0067 | - | |
|
| 0.8330 | 250000 | 0.0001 | 0.0001 | 0.9639 | -0.0067 | - | |
|
| 0.8347 | 250500 | - | 0.0001 | 0.9633 | -0.0067 | - | |
|
| 0.8363 | 251000 | 0.0001 | 0.0001 | 0.9635 | -0.0066 | - | |
|
| 0.8380 | 251500 | - | 0.0001 | 0.9637 | -0.0066 | - | |
|
| 0.8397 | 252000 | 0.0001 | 0.0001 | 0.9632 | -0.0067 | - | |
|
| 0.8413 | 252500 | - | 0.0001 | 0.9638 | -0.0066 | - | |
|
| 0.8430 | 253000 | 0.0001 | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8447 | 253500 | - | 0.0001 | 0.9635 | -0.0066 | - | |
|
| 0.8463 | 254000 | 0.0001 | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8480 | 254500 | - | 0.0001 | 0.9630 | -0.0066 | - | |
|
| 0.8497 | 255000 | 0.0001 | 0.0001 | 0.9633 | -0.0066 | - | |
|
| 0.8513 | 255500 | - | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8530 | 256000 | 0.0001 | 0.0001 | 0.9635 | -0.0066 | - | |
|
| 0.8546 | 256500 | - | 0.0001 | 0.9640 | -0.0066 | - | |
|
| 0.8563 | 257000 | 0.0001 | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8580 | 257500 | - | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8596 | 258000 | 0.0001 | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8613 | 258500 | - | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8630 | 259000 | 0.0001 | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8646 | 259500 | - | 0.0001 | 0.9635 | -0.0066 | - | |
|
| 0.8663 | 260000 | 0.0001 | 0.0001 | 0.9637 | -0.0066 | - | |
|
| 0.8680 | 260500 | - | 0.0001 | 0.9637 | -0.0066 | - | |
|
| 0.8696 | 261000 | 0.0001 | 0.0001 | 0.9639 | -0.0066 | - | |
|
| 0.8713 | 261500 | - | 0.0001 | 0.9640 | -0.0066 | - | |
|
| 0.8730 | 262000 | 0.0001 | 0.0001 | 0.9640 | -0.0066 | - | |
|
| 0.8746 | 262500 | - | 0.0001 | 0.9642 | -0.0066 | - | |
|
| 0.8763 | 263000 | 0.0001 | 0.0001 | 0.9636 | -0.0066 | - | |
|
| 0.8780 | 263500 | - | 0.0001 | 0.9640 | -0.0066 | - | |
|
| 0.8796 | 264000 | 0.0001 | 0.0001 | 0.9642 | -0.0066 | - | |
|
| 0.8813 | 264500 | - | 0.0001 | 0.9640 | -0.0066 | - | |
|
| 0.8830 | 265000 | 0.0001 | 0.0001 | 0.9642 | -0.0066 | - | |
|
| 0.8846 | 265500 | - | 0.0001 | 0.9645 | -0.0066 | - | |
|
| 0.8863 | 266000 | 0.0001 | 0.0001 | 0.9637 | -0.0066 | - | |
|
| 0.8880 | 266500 | - | 0.0001 | 0.9640 | -0.0066 | - | |
|
| 0.8896 | 267000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.8913 | 267500 | - | 0.0001 | 0.9641 | -0.0065 | - | |
|
| 0.8930 | 268000 | 0.0001 | 0.0001 | 0.9639 | -0.0065 | - | |
|
| 0.8946 | 268500 | - | 0.0001 | 0.9642 | -0.0065 | - | |
|
| 0.8963 | 269000 | 0.0001 | 0.0001 | 0.9642 | -0.0065 | - | |
|
| 0.8980 | 269500 | - | 0.0001 | 0.9640 | -0.0065 | - | |
|
| 0.8996 | 270000 | 0.0001 | 0.0001 | 0.9642 | -0.0065 | - | |
|
| 0.9013 | 270500 | - | 0.0001 | 0.9639 | -0.0065 | - | |
|
| 0.9030 | 271000 | 0.0001 | 0.0001 | 0.9641 | -0.0065 | - | |
|
| 0.9046 | 271500 | - | 0.0001 | 0.9640 | -0.0065 | - | |
|
| 0.9063 | 272000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9080 | 272500 | - | 0.0001 | 0.9645 | -0.0065 | - | |
|
| 0.9096 | 273000 | 0.0001 | 0.0001 | 0.9645 | -0.0065 | - | |
|
| 0.9113 | 273500 | - | 0.0001 | 0.9645 | -0.0065 | - | |
|
| 0.9130 | 274000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9146 | 274500 | - | 0.0001 | 0.9645 | -0.0065 | - | |
|
| 0.9163 | 275000 | 0.0001 | 0.0001 | 0.9642 | -0.0065 | - | |
|
| 0.9180 | 275500 | - | 0.0001 | 0.9645 | -0.0065 | - | |
|
| 0.9196 | 276000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9213 | 276500 | - | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9230 | 277000 | 0.0001 | 0.0001 | 0.9644 | -0.0065 | - | |
|
| 0.9246 | 277500 | - | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9263 | 278000 | 0.0001 | 0.0001 | 0.9644 | -0.0065 | - | |
|
| 0.9280 | 278500 | - | 0.0001 | 0.9646 | -0.0065 | - | |
|
| 0.9296 | 279000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9313 | 279500 | - | 0.0001 | 0.9644 | -0.0065 | - | |
|
| 0.9330 | 280000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9346 | 280500 | - | 0.0001 | 0.9644 | -0.0065 | - | |
|
| 0.9363 | 281000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9379 | 281500 | - | 0.0001 | 0.9645 | -0.0065 | - | |
|
| 0.9396 | 282000 | 0.0001 | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9413 | 282500 | - | 0.0001 | 0.9643 | -0.0065 | - | |
|
| 0.9429 | 283000 | 0.0001 | 0.0001 | 0.9646 | -0.0065 | - | |
|
| 0.9446 | 283500 | - | 0.0001 | 0.9644 | -0.0064 | - | |
|
| 0.9463 | 284000 | 0.0001 | 0.0001 | 0.9646 | -0.0065 | - | |
|
| 0.9479 | 284500 | - | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9496 | 285000 | 0.0001 | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9513 | 285500 | - | 0.0001 | 0.9647 | -0.0064 | - | |
|
| 0.9529 | 286000 | 0.0001 | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9546 | 286500 | - | 0.0001 | 0.9645 | -0.0064 | - | |
|
| 0.9563 | 287000 | 0.0001 | 0.0001 | 0.9646 | -0.0064 | - | |
|
| 0.9579 | 287500 | - | 0.0001 | 0.9647 | -0.0064 | - | |
|
| 0.9596 | 288000 | 0.0001 | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9613 | 288500 | - | 0.0001 | 0.9647 | -0.0064 | - | |
|
| 0.9629 | 289000 | 0.0001 | 0.0001 | 0.9647 | -0.0064 | - | |
|
| 0.9646 | 289500 | - | 0.0001 | 0.9647 | -0.0064 | - | |
|
| 0.9663 | 290000 | 0.0001 | 0.0001 | 0.9649 | -0.0064 | - | |
|
| 0.9679 | 290500 | - | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9696 | 291000 | 0.0001 | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9713 | 291500 | - | 0.0001 | 0.9649 | -0.0064 | - | |
|
| 0.9729 | 292000 | 0.0001 | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9746 | 292500 | - | 0.0001 | 0.9649 | -0.0064 | - | |
|
| 0.9763 | 293000 | 0.0001 | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9779 | 293500 | - | 0.0001 | 0.9648 | -0.0064 | - | |
|
| 0.9796 | 294000 | 0.0001 | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9813 | 294500 | - | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9829 | 295000 | 0.0001 | 0.0001 | 0.9649 | -0.0064 | - | |
|
| 0.9846 | 295500 | - | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9863 | 296000 | 0.0001 | 0.0001 | 0.9649 | -0.0064 | - | |
|
| 0.9879 | 296500 | - | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9896 | 297000 | 0.0001 | 0.0001 | 0.9650 | -0.0064 | - | |
|
| **0.9913** | **297500** | **-** | **0.0001** | **0.9651** | **-0.0064** | **-** | |
|
| 0.9929 | 298000 | 0.0001 | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9946 | 298500 | - | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9963 | 299000 | 0.0001 | 0.0001 | 0.9651 | -0.0064 | - | |
|
| 0.9979 | 299500 | - | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 0.9996 | 300000 | 0.0001 | 0.0001 | 0.9650 | -0.0064 | - | |
|
| 1.0 | 300123 | - | - | - | - | 0.9651 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.12.4 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.48.0 |
|
- PyTorch: 2.4.1+cu121 |
|
- Accelerate: 1.0.1 |
|
- Datasets: 2.19.0 |
|
- Tokenizers: 0.21.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### 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", |
|
} |
|
``` |
|
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