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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:482091 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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- loss:CoSENTLoss |
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base_model: Alibaba-NLP/gte-multilingual-base |
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widget: |
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- source_sentence: Ya da dışarı çıkıp yürü ya da biraz koşun. Bunu düzenli olarak |
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yapmıyorum ama Washington bunu yapmak için harika bir yer. |
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sentences: |
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- “Washington's yürüyüş ya da koşu için harika bir yer.” |
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- H-2A uzaylılar Amerika Birleşik Devletleri'nde zaman kısa süreleri var. |
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- “Washington'da düzenli olarak yürüyüşe ya da koşuya çıkıyorum.” |
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- source_sentence: Orta yaylalar ve güney kıyıları arasındaki kontrast daha belirgin |
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olamazdı. |
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sentences: |
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- İşitme Yardımı Uyumluluğu Müzakere Kuralları Komitesi, Federal İletişim Komisyonu'nun |
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bir ürünüdür. |
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- Dağlık ve sahil arasındaki kontrast kolayca işaretlendi. |
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- Kontrast işaretlenemedi. |
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- source_sentence: Bir 1997 Henry J. Kaiser Aile Vakfı anket yönetilen bakım planlarında |
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Amerikalılar temelde kendi bakımı ile memnun olduğunu bulundu. |
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sentences: |
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- Kaplanları takip ederken çok sessiz olmalısın. |
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- Henry Kaiser vakfı insanların sağlık hizmetlerinden hoşlandığını gösteriyor. |
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- Henry Kaiser Vakfı insanların sağlık hizmetlerinden nefret ettiğini gösteriyor. |
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- source_sentence: Eminim yapmışlardır. |
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sentences: |
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- Eminim öyle yapmışlardır. |
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- Batı Teksas'ta 100 10 dereceydi. |
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- Eminim yapmamışlardır. |
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- source_sentence: Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti, |
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her şeyi denedi ve daha az ilgileniyordu. |
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sentences: |
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- Oğlu her şeye olan ilgisini kaybediyordu. |
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- Pek bir şey yapmadım. |
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- Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu. |
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datasets: |
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- emrecan/all-nli-tr |
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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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- cosine_accuracy |
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- pearson_cosine |
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- spearman_cosine |
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model-index: |
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- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base |
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results: |
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- task: |
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type: triplet |
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name: Triplet |
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dataset: |
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name: all nli tr test |
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type: all-nli-tr-test |
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metrics: |
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- type: cosine_accuracy |
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value: 0.8966145437983908 |
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name: Cosine Accuracy |
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- type: cosine_accuracy |
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value: 0.9351753453772582 |
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name: Cosine Accuracy |
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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: 0.8043925123766598 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.804133282756889 |
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name: Spearman Cosine |
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- type: pearson_cosine |
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value: 0.8133873820848544 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8199552151367876 |
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name: Spearman Cosine |
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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: sts22 test |
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type: sts22-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.647912337747937 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6694072470896322 |
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name: Spearman Cosine |
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- type: pearson_cosine |
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value: 0.6514085062457564 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.6827342891126081 |
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name: Spearman Cosine |
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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 gte multilingual base |
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type: sts-dev-gte-multilingual-base |
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metrics: |
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- type: pearson_cosine |
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value: 0.838717139426684 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8428367492381358 |
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name: Spearman Cosine |
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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 gte multilingual base |
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type: sts-test-gte-multilingual-base |
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metrics: |
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- type: pearson_cosine |
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value: 0.8133873820848544 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8199552151367876 |
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name: Spearman Cosine |
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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: stsb dev 768 |
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type: stsb-dev-768 |
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metrics: |
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- type: pearson_cosine |
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value: 0.870311456444647 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8747522169942328 |
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name: Spearman Cosine |
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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: stsb dev 512 |
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type: stsb-dev-512 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8696934286998554 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8753487201891684 |
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name: Spearman Cosine |
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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: stsb dev 256 |
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type: stsb-dev-256 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8644706498119142 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.873468734899321 |
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name: Spearman Cosine |
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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: stsb dev 128 |
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type: stsb-dev-128 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8591309130178328 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8700377378574327 |
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name: Spearman Cosine |
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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: stsb dev 64 |
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type: stsb-dev-64 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8479124810212979 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8655596653561272 |
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name: Spearman Cosine |
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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: stsb test 768 |
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type: stsb-test-768 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8455412308380735 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8535290217691063 |
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name: Spearman Cosine |
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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: stsb test 512 |
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type: stsb-test-512 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8464773608783734 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8553900248212041 |
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name: Spearman Cosine |
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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: stsb test 256 |
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type: stsb-test-256 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8443046458551826 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8550098621393595 |
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name: Spearman Cosine |
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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: stsb test 128 |
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type: stsb-test-128 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8363964421208214 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8511193715667303 |
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name: Spearman Cosine |
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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: stsb test 64 |
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type: stsb-test-64 |
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metrics: |
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- type: pearson_cosine |
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value: 0.8235450515966374 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8460761238725121 |
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name: Spearman Cosine |
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--- |
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# SentenceTransformer based on Alibaba-NLP/gte-multilingual-base |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) on the [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) dataset. 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. |
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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:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision ca1791e0bcc104f6db161f27de1340241b13c5a4 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) |
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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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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel |
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(1): Pooling({'word_embedding_dimension': 768, '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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'Ve gerçekten, baba haklıydı, oğlu zaten her şeyi tecrübe etmişti, her şeyi denedi ve daha az ilgileniyordu.', |
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'Oğlu her şeye olan ilgisini kaybediyordu.', |
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'Baba oğlunun tecrübe için hala çok şey olduğunu biliyordu.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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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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#### Triplet |
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* Dataset: `all-nli-tr-test` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| **cosine_accuracy** | **0.8966** | |
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#### Semantic Similarity |
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* Datasets: `sts-test`, `sts22-test`, `sts-dev-gte-multilingual-base`, `sts-test-gte-multilingual-base`, `sts-test`, `sts22-test`, `stsb-dev-768`, `stsb-dev-512`, `stsb-dev-256`, `stsb-dev-128`, `stsb-dev-64`, `stsb-test-768`, `stsb-test-512`, `stsb-test-256`, `stsb-test-128` and `stsb-test-64` |
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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-test | sts22-test | sts-dev-gte-multilingual-base | sts-test-gte-multilingual-base | stsb-dev-768 | stsb-dev-512 | stsb-dev-256 | stsb-dev-128 | stsb-dev-64 | stsb-test-768 | stsb-test-512 | stsb-test-256 | stsb-test-128 | stsb-test-64 | |
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|:--------------------|:---------|:-----------|:------------------------------|:-------------------------------|:-------------|:-------------|:-------------|:-------------|:------------|:--------------|:--------------|:--------------|:--------------|:-------------| |
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| pearson_cosine | 0.8134 | 0.6514 | 0.8387 | 0.8134 | 0.8703 | 0.8697 | 0.8645 | 0.8591 | 0.8479 | 0.8455 | 0.8465 | 0.8443 | 0.8364 | 0.8235 | |
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| **spearman_cosine** | **0.82** | **0.6827** | **0.8428** | **0.82** | **0.8748** | **0.8753** | **0.8735** | **0.87** | **0.8656** | **0.8535** | **0.8554** | **0.855** | **0.8511** | **0.8461** | |
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#### Triplet |
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* Dataset: `all-nli-tr-test` |
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* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| **cosine_accuracy** | **0.9352** | |
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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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#### all-nli-tr |
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* Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d) |
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* Size: 482,091 training samples |
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence1 | sentence2 | score | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
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| type | string | string | float | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 10.51 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 10.47 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.23</li><li>max: 5.0</li></ul> | |
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* Samples: |
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| sentence1 | sentence2 | score | |
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|:----------------------------------------------------------|:-------------------------------------------------------------------|:-----------------| |
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| <code>Bir uçak kalkıyor.</code> | <code>Bir hava uçağı kalkıyor.</code> | <code>5.0</code> | |
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| <code>Bir adam büyük bir flüt çalıyor.</code> | <code>Bir adam flüt çalıyor.</code> | <code>3.8</code> | |
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| <code>Bir adam pizzaya rendelenmiş peynir yayıyor.</code> | <code>Bir adam pişmemiş pizzaya rendelenmiş peynir yayıyor.</code> | <code>3.8</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "CoSENTLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1 |
|
|
], |
|
|
"n_dims_per_step": -1 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Evaluation Dataset |
|
|
|
|
|
#### all-nli-tr |
|
|
|
|
|
* Dataset: [all-nli-tr](https://huggingface.co/datasets/emrecan/all-nli-tr) at [daeabfb](https://huggingface.co/datasets/emrecan/all-nli-tr/tree/daeabfbc01f82757ab998bd23ce0ddfceaa5e24d) |
|
|
* Size: 6,567 evaluation samples |
|
|
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | sentence1 | sentence2 | score | |
|
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
|
|
| type | string | string | float | |
|
|
| details | <ul><li>min: 6 tokens</li><li>mean: 15.89 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.02 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.1</li><li>max: 5.0</li></ul> | |
|
|
* Samples: |
|
|
| sentence1 | sentence2 | score | |
|
|
|:---------------------------------------------|:----------------------------------------------------|:------------------| |
|
|
| <code>Şapkalı bir adam dans ediyor.</code> | <code>Sert şapka takan bir adam dans ediyor.</code> | <code>5.0</code> | |
|
|
| <code>Küçük bir çocuk ata biniyor.</code> | <code>Bir çocuk ata biniyor.</code> | <code>4.75</code> | |
|
|
| <code>Bir adam yılana fare yediriyor.</code> | <code>Adam yılana fare yediriyor.</code> | <code>5.0</code> | |
|
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"loss": "CoSENTLoss", |
|
|
"matryoshka_dims": [ |
|
|
768, |
|
|
512, |
|
|
256, |
|
|
128, |
|
|
64 |
|
|
], |
|
|
"matryoshka_weights": [ |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1 |
|
|
], |
|
|
"n_dims_per_step": -1 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: steps |
|
|
- `per_device_train_batch_size`: 32 |
|
|
- `per_device_eval_batch_size`: 32 |
|
|
- `learning_rate`: 1e-05 |
|
|
- `weight_decay`: 0.01 |
|
|
- `num_train_epochs`: 10 |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `warmup_steps`: 144 |
|
|
- `bf16`: True |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: steps |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 32 |
|
|
- `per_device_eval_batch_size`: 32 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 1 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_steps`: None |
|
|
- `learning_rate`: 1e-05 |
|
|
- `weight_decay`: 0.01 |
|
|
- `adam_beta1`: 0.9 |
|
|
- `adam_beta2`: 0.999 |
|
|
- `adam_epsilon`: 1e-08 |
|
|
- `max_grad_norm`: 1.0 |
|
|
- `num_train_epochs`: 10 |
|
|
- `max_steps`: -1 |
|
|
- `lr_scheduler_type`: linear |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `warmup_steps`: 144 |
|
|
- `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`: True |
|
|
- `fp16`: False |
|
|
- `fp16_opt_level`: O1 |
|
|
- `half_precision_backend`: auto |
|
|
- `bf16_full_eval`: False |
|
|
- `fp16_full_eval`: False |
|
|
- `tf32`: None |
|
|
- `local_rank`: 0 |
|
|
- `ddp_backend`: None |
|
|
- `tpu_num_cores`: None |
|
|
- `tpu_metrics_debug`: False |
|
|
- `debug`: [] |
|
|
- `dataloader_drop_last`: False |
|
|
- `dataloader_num_workers`: 0 |
|
|
- `dataloader_prefetch_factor`: None |
|
|
- `past_index`: -1 |
|
|
- `disable_tqdm`: False |
|
|
- `remove_unused_columns`: True |
|
|
- `label_names`: None |
|
|
- `load_best_model_at_end`: False |
|
|
- `ignore_data_skip`: False |
|
|
- `fsdp`: [] |
|
|
- `fsdp_min_num_params`: 0 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: None |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `dispatch_batches`: None |
|
|
- `split_batches`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | Validation Loss | all-nli-tr-test_cosine_accuracy | sts-test_spearman_cosine | sts22-test_spearman_cosine | sts-dev-gte-multilingual-base_spearman_cosine | sts-test-gte-multilingual-base_spearman_cosine | stsb-dev-768_spearman_cosine | stsb-dev-512_spearman_cosine | stsb-dev-256_spearman_cosine | stsb-dev-128_spearman_cosine | stsb-dev-64_spearman_cosine | stsb-test-768_spearman_cosine | stsb-test-512_spearman_cosine | stsb-test-256_spearman_cosine | stsb-test-128_spearman_cosine | stsb-test-64_spearman_cosine | |
|
|
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:------------------------:|:--------------------------:|:---------------------------------------------:|:----------------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:-----------------------------:|:----------------------------:| |
|
|
| 0 | 0 | - | - | 0.8966 | 0.8041 | 0.6694 | - | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 0.1327 | 1000 | 2.5299 | 3.3893 | - | - | - | 0.8318 | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 0.2655 | 2000 | 2.1132 | 3.3050 | - | - | - | 0.8345 | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 0.3982 | 3000 | 5.1488 | 2.7752 | - | - | - | 0.8481 | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 0.5310 | 4000 | 5.4103 | 2.7242 | - | - | - | 0.8445 | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 0.6637 | 5000 | 5.1896 | 2.6701 | - | - | - | 0.8451 | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 0.7965 | 6000 | 5.0105 | 2.6489 | - | - | - | 0.8431 | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 0.9292 | 7000 | 5.1059 | 2.6114 | - | - | - | 0.8428 | - | - | - | - | - | - | - | - | - | - | - | |
|
|
| 1.0 | 7533 | - | - | 0.9352 | 0.8200 | 0.6827 | - | 0.8200 | - | - | - | - | - | - | - | - | - | - | |
|
|
| 1.1111 | 200 | 34.2828 | 29.8737 | - | - | - | - | - | 0.8671 | 0.8671 | 0.8639 | 0.8606 | 0.8546 | - | - | - | - | - | |
|
|
| 2.2222 | 400 | 28.038 | 28.8915 | - | - | - | - | - | 0.8740 | 0.8742 | 0.8720 | 0.8691 | 0.8648 | - | - | - | - | - | |
|
|
| 3.3333 | 600 | 27.3829 | 29.3391 | - | - | - | - | - | 0.8747 | 0.8751 | 0.8728 | 0.8699 | 0.8653 | - | - | - | - | - | |
|
|
| 4.4444 | 800 | 26.807 | 30.0090 | - | - | - | - | - | 0.8756 | 0.8761 | 0.8741 | 0.8710 | 0.8665 | - | - | - | - | - | |
|
|
| 5.5556 | 1000 | 26.4543 | 30.5886 | - | - | - | - | - | 0.8753 | 0.8757 | 0.8739 | 0.8705 | 0.8662 | - | - | - | - | - | |
|
|
| 6.6667 | 1200 | 26.0413 | 31.3750 | - | - | - | - | - | 0.8744 | 0.8751 | 0.8730 | 0.8698 | 0.8655 | - | - | - | - | - | |
|
|
| 7.7778 | 1400 | 25.8221 | 31.6515 | - | - | - | - | - | 0.8752 | 0.8758 | 0.8739 | 0.8706 | 0.8661 | - | - | - | - | - | |
|
|
| 8.8889 | 1600 | 25.6656 | 31.9805 | - | - | - | - | - | 0.8746 | 0.8752 | 0.8733 | 0.8700 | 0.8655 | - | - | - | - | - | |
|
|
| 10.0 | 1800 | 25.5355 | 32.0454 | - | - | - | - | - | 0.8748 | 0.8753 | 0.8735 | 0.8700 | 0.8656 | 0.8535 | 0.8554 | 0.8550 | 0.8511 | 0.8461 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 3.3.1 |
|
|
- Transformers: 4.49.0.dev0 |
|
|
- PyTorch: 2.5.1+cu121 |
|
|
- Accelerate: 1.2.1 |
|
|
- Datasets: 3.2.0 |
|
|
- 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MatryoshkaLoss |
|
|
```bibtex |
|
|
@misc{kusupati2024matryoshka, |
|
|
title={Matryoshka Representation Learning}, |
|
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
|
year={2024}, |
|
|
eprint={2205.13147}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.LG} |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CoSENTLoss |
|
|
```bibtex |
|
|
@online{kexuefm-8847, |
|
|
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
|
|
author={Su Jianlin}, |
|
|
year={2022}, |
|
|
month={Jan}, |
|
|
url={https://kexue.fm/archives/8847}, |
|
|
} |
|
|
``` |
|
|
|
|
|
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