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This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. - Accuracy on SNLI-test dataset: 92.20 - Accuracy on MNLI mismatched set: 90.49
翻訳待ち・原文を表示
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder.
翻訳待ち・原文を表示
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder.
翻訳待ち・原文を表示
This model was trained using SentenceTransformers Cross-Encoder class. The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For evaluation results, see SBERT.net - Pretrained Cross-Encoder.
翻訳待ち・原文を表示
This model was trained using SentenceTransformers Cross-Encoder class. The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For evaluation results, see SBERT.net - Pretrained Cross-Encoder.
翻訳待ち・原文を表示
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. You can use the model also directly with Transformers library (without SentenceTransformers library):
翻訳待ち・原文を表示
Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task. For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html]. You can use the model also directly with Transformers library (without SentenceTransformers library):
翻訳待ち・原文を表示
This model was trained using SentenceTransformers Cross-Encoder class. Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task. For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html].
翻訳待ち・原文を表示
This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates. Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rather low score, as these are not duplicates. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
This model was trained using SentenceTransformers Cross-Encoder class. This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
翻訳待ち・原文を表示
This model was trained using SentenceTransformers Cross-Encoder class. This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
翻訳待ち・原文を表示
This model was trained using SentenceTransformers Cross-Encoder class. This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
翻訳待ち・原文を表示
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
翻訳待ち・原文を表示
This is a Cross Encoder model finetuned from prajjwal1/bert-tiny using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - **Model Type:** Cross Encoder - **Base model:** prajjwal1/bert-tiny - **Maximum Sequence Length:** 512 tokens - **Number of Output Labels:** 1 label - **Documentation:** Sentence Transformers Documentation - **Documentation:** Cross Encoder Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Cross Encoders on Hugging Face
翻訳待ち・原文を表示
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
This is a Cross Encoder model finetuned from prajjwal1/bert-tiny using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Carbon emissions were measured using CodeCarbon. - **Energy Consumed**: 0.019 kWh - **Carbon Emitted**: 0.007 kg of CO2 - **Hours Used**: 0.099 hours
翻訳待ち・原文を表示
This is a Cross Encoder model finetuned from prajjwal1/bert-tiny using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Carbon emissions were measured using CodeCarbon. - **Energy Consumed**: 0.019 kWh - **Carbon Emitted**: 0.007 kg of CO2 - **Hours Used**: 0.099 hours
翻訳待ち・原文を表示
This is a Cross Encoder model finetuned from prajjwal1/bert-tiny using the sentence-transformers library. It computes scores for pairs of texts, which can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. Carbon emissions were measured using CodeCarbon. - **Energy Consumed**: 0.019 kWh - **Carbon Emitted**: 0.007 kg of CO2 - **Hours Used**: 0.099 hours
翻訳待ち・原文を表示
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
