prompts stringlengths 81 413 | metrics_response stringlengths 0 371 |
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What metrics were used to measure the Fuzzy Retrieval model in the EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification paper on the EVI fr-FR dataset? | Top-1 (%) |
What metrics were used to measure the MSM-MAE model in the Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the AudioMAE (local) model in the Masked Autoencoders that Listen paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the M2D ratio=0.6 model in the Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the ATST Base (ours) model in the ATST: Audio Representation Learning with Teacher-Student Transformer paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the AudioMAE (global) model in the Masked Autoencoders that Listen paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the AutoSpeech (N=8,C=128) model in the AutoSpeech: Neural Architecture Search for Speaker Recognition paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the SSAST-FRAME model in the SSAST: Self-Supervised Audio Spectrogram Transformer paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the SSAST-PATCH model in the SSAST: Self-Supervised Audio Spectrogram Transformer paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the COLA model in the Contrastive Learning of General-Purpose Audio Representations paper on the VoxCeleb1 dataset? | Top-1 (%), Top-5 (%), Number of Params, Accuracy |
What metrics were used to measure the TitaNet-M (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI Lapel dataset? | DER(%) |
What metrics were used to measure the TitaNet-S (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI Lapel dataset? | DER(%) |
What metrics were used to measure the TitaNet-L (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI Lapel dataset? | DER(%) |
What metrics were used to measure the ECAPA (SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI Lapel dataset? | DER(%) |
What metrics were used to measure the TitaNet-S (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the CH109 dataset? | DER(%) |
What metrics were used to measure the TitaNet-M (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the CH109 dataset? | DER(%) |
What metrics were used to measure the TitaNet-L (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the CH109 dataset? | DER(%) |
What metrics were used to measure the x-vector (PLDA + AHC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the CH109 dataset? | DER(%) |
What metrics were used to measure the TitaNet-L (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI MixHeadset dataset? | DER(%) |
What metrics were used to measure the ECAPA (SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI MixHeadset dataset? | DER(%) |
What metrics were used to measure the TitaNet-M (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI MixHeadset dataset? | DER(%) |
What metrics were used to measure the TitaNet-S (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the AMI MixHeadset dataset? | DER(%) |
What metrics were used to measure the SOND model in the Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios paper on the AliMeeting dataset? | DER(%) |
What metrics were used to measure the UIS-RNN-SML model in the Supervised online diarization with sample mean loss for multi-domain data paper on the DIHARD II dataset? | DER(%), DER - no overlap |
What metrics were used to measure the pyannote (waveform) model in the pyannote.audio: neural building blocks for speaker diarization paper on the AMI dataset? | DER(%), FA, Miss |
What metrics were used to measure the pyannote (MFCC) model in the pyannote.audio: neural building blocks for speaker diarization paper on the AMI dataset? | DER(%), FA, Miss |
What metrics were used to measure the d-vector + spectral model in the Speaker Diarization with LSTM paper on the CALLHOME-109 dataset? | DER(%) |
What metrics were used to measure the TOLD model in the TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the SA-EEND (2-spk, adapted) model in the End-to-End Neural Speaker Diarization with Self-attention paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the EEND-OLA model in the TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the SA-EEND (2-spk, no-adapt) model in the End-to-End Neural Speaker Diarization with Self-attention paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the COS+AHC (Oracle SAD) model in the Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the EEND model in the End-to-End Neural Speaker Diarization with Permutation-Free Objectives paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the COS+NJW-SC (Oracle SAD) model in the Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the COS+NME-SC (Oracle SAD) model in the Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the PLDA+AHC (Oracle SAD) model in the Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the COS+B-SC (Oracle SAD) model in the Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap paper on the CALLHOME dataset? | DER(%), DER(ig olp), FA, MI, CF |
What metrics were used to measure the x-vector (MCGAN) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the NIST-SRE 2000 dataset? | DER(%) |
What metrics were used to measure the TitaNet-S (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the NIST-SRE 2000 dataset? | DER(%) |
What metrics were used to measure the TitaNet-M (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the NIST-SRE 2000 dataset? | DER(%) |
What metrics were used to measure the TitaNet-L (NME-SC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the NIST-SRE 2000 dataset? | DER(%) |
What metrics were used to measure the x-vector (PLDA + AHC) model in the TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context paper on the NIST-SRE 2000 dataset? | DER(%) |
What metrics were used to measure the pyannote (waveform) model in the pyannote.audio: neural building blocks for speaker diarization paper on the ETAPE dataset? | DER(%), FA, Miss |
What metrics were used to measure the pyannote (MFCC) model in the pyannote.audio: neural building blocks for speaker diarization paper on the ETAPE dataset? | DER(%), FA, Miss |
What metrics were used to measure the Baseline model in the pyannote.audio: neural building blocks for speaker diarization paper on the ETAPE dataset? | DER(%), FA, Miss |
What metrics were used to measure the UIS-RNN model in the Fully Supervised Speaker Diarization paper on the Hub5'00 CallHome dataset? | V |
What metrics were used to measure the pyannote (waveform) model in the pyannote.audio: neural building blocks for speaker diarization paper on the DIHARD dataset? | DER(%), FA, Miss |
What metrics were used to measure the pyannote (MFCC) model in the pyannote.audio: neural building blocks for speaker diarization paper on the DIHARD dataset? | DER(%), FA, Miss |
What metrics were used to measure the Baseline (the best result in the literature as of Oct.2019) model in the pyannote.audio: neural building blocks for speaker diarization paper on the DIHARD dataset? | DER(%), FA, Miss |
What metrics were used to measure the w2v2-aam model in the Fine-tuning wav2vec2 for speaker recognition paper on the VoxCeleb1 dataset? | EER |
What metrics were used to measure the GE2E model in the Generalized End-to-End Loss for Speaker Verification paper on the CALLHOME dataset? | Cosine EER |
What metrics were used to measure the model in the Generalized End-to-End Loss for Speaker Verification paper on the CALLHOME dataset? | Cosine EER |
What metrics were used to measure the SpeechNAS model in the SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification paper on the VoxCeleb dataset? | EER |
What metrics were used to measure the Multi Task SSL model in the Multi-task Voice Activated Framework using Self-supervised Learning paper on the VoxCeleb dataset? | EER |
What metrics were used to measure the X-Vectors with Attention Backend model in the Attention Back-end for Automatic Speaker Verification with Multiple Enrollment Utterances paper on the CN-CELEB dataset? | EER |
What metrics were used to measure the ResNet with Attention Backend model in the Attention Back-end for Automatic Speaker Verification with Multiple Enrollment Utterances paper on the CN-CELEB dataset? | EER |
What metrics were used to measure the SpeechNAS model in the SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification paper on the VoxCeleb1 dataset? | EER, EER Vox1-E, EER Vox1-H, EER Vox1-O, Minimum DCF Vox1-E Original prior=0.01, Minimum DCF Vox1-E prior=0.05, Minimum DCF Vox1-H Original prior=0.01, Minimum DCF Vox1-H prior=0.05, Minimum DCF Vox1-O prior=0.01, Minimum DCF Vox1-O prior=0.05, Test EER |
What metrics were used to measure the Fine-tuned HuBERT Large model in the A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding paper on the VoxCeleb1 dataset? | EER, EER Vox1-E, EER Vox1-H, EER Vox1-O, Minimum DCF Vox1-E Original prior=0.01, Minimum DCF Vox1-E prior=0.05, Minimum DCF Vox1-H Original prior=0.01, Minimum DCF Vox1-H prior=0.05, Minimum DCF Vox1-O prior=0.01, Minimum DCF Vox1-O prior=0.05, Test EER |
What metrics were used to measure the ResNet-50 model in the VoxCeleb2: Deep Speaker Recognition paper on the VoxCeleb2 dataset? | EER |
What metrics were used to measure the Ensemble locally constant networks model in the Oblique Decision Trees from Derivatives of ReLU Networks paper on the PDBbind dataset? | RMSE |
What metrics were used to measure the GLAM model in the An adaptive graph learning method for automated molecular interactions and properties predictions paper on the LIT-PCBA(KAT2A) dataset? | AUC |
What metrics were used to measure the TransformerCPI model in the TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments paper on the LIT-PCBA(KAT2A) dataset? | AUC |
What metrics were used to measure the DGraphDTA model in the Drug–target affinity prediction using graph neural network and contact maps paper on the LIT-PCBA(KAT2A) dataset? | AUC |
What metrics were used to measure the TrimNet model in the TrimNet: learning molecular representation from triplet messages for biomedicine paper on the MUV dataset? | AUC |
What metrics were used to measure the GraphConv + dummy super node model in the Learning Graph-Level Representation for Drug Discovery paper on the MUV dataset? | AUC |
What metrics were used to measure the GraphConv model in the Convolutional Networks on Graphs for Learning Molecular Fingerprints paper on the MUV dataset? | AUC |
What metrics were used to measure the ContextPred model in the Strategies for Pre-training Graph Neural Networks paper on the MUV dataset? | AUC |
What metrics were used to measure the RNN-DFS model in the Relational Pooling for Graph Representations paper on the MUV dataset? | AUC |
What metrics were used to measure the GLAM model in the An adaptive graph learning method for automated molecular interactions and properties predictions paper on the Tox21(scaffold) dataset? | AUC |
What metrics were used to measure the AttentionSiteDTI model in the AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification paper on the BindingDB dataset? | AUC |
What metrics were used to measure the GLAM model in the An adaptive graph learning method for automated molecular interactions and properties predictions paper on the BindingDB dataset? | AUC |
What metrics were used to measure the TransformerCPI model in the TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments paper on the BindingDB dataset? | AUC |
What metrics were used to measure the DGraphDTA model in the Drug–target affinity prediction using graph neural network and contact maps paper on the BindingDB dataset? | AUC |
What metrics were used to measure the SMT-DTA model in the SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction paper on the KIBA dataset? | CI, MSE |
What metrics were used to measure the DeepPurpose model in the DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction paper on the KIBA dataset? | CI, MSE |
What metrics were used to measure the DeepDTA model in the DeepDTA: Deep Drug-Target Binding Affinity Prediction paper on the KIBA dataset? | CI, MSE |
What metrics were used to measure the GraphDTA model in the GraphDTA: prediction of drug–target binding affinity using graph convolutional networks paper on the KIBA dataset? | CI, MSE |
What metrics were used to measure the HierG2G model in the Hierarchical Graph-to-Graph Translation for Molecules paper on the DRD2 dataset? | Diversity, Success |
What metrics were used to measure the GLAM model in the An adaptive graph learning method for automated molecular interactions and properties predictions paper on the SIDER(scaffold) dataset? | AUC |
What metrics were used to measure the GraphConv + dummy super node model in the Learning Graph-Level Representation for Drug Discovery paper on the PCBA dataset? | AUC |
What metrics were used to measure the GraphConv model in the Convolutional Networks on Graphs for Learning Molecular Fingerprints paper on the PCBA dataset? | AUC |
What metrics were used to measure the GLAM model in the An adaptive graph learning method for automated molecular interactions and properties predictions paper on the LIT-PCBA(MAPK1) dataset? | AUC |
What metrics were used to measure the TransformerCPI model in the TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments paper on the LIT-PCBA(MAPK1) dataset? | AUC |
What metrics were used to measure the DGraphDTA model in the Drug–target affinity prediction using graph neural network and contact maps paper on the LIT-PCBA(MAPK1) dataset? | AUC |
What metrics were used to measure the GLAM model in the An adaptive graph learning method for automated molecular interactions and properties predictions paper on the FreeSolv(scaffold) dataset? | RMSE |
What metrics were used to measure the TrimNet model in the TrimNet: learning molecular representation from triplet messages for biomedicine paper on the ClinTox dataset? | AUC |
What metrics were used to measure the ContextPred model in the Strategies for Pre-training Graph Neural Networks paper on the ClinTox dataset? | AUC |
What metrics were used to measure the GraphConv + dummy super node + focal loss model in the Learning Graph-Level Representation for Drug Discovery paper on the HIV dataset dataset? | AUC |
What metrics were used to measure the GraphConv model in the Convolutional Networks on Graphs for Learning Molecular Fingerprints paper on the HIV dataset dataset? | AUC |
What metrics were used to measure the TrimNet model in the TrimNet: learning molecular representation from triplet messages for biomedicine paper on the HIV dataset dataset? | AUC |
What metrics were used to measure the ContextPred model in the Strategies for Pre-training Graph Neural Networks paper on the HIV dataset dataset? | AUC |
What metrics were used to measure the RNN-DFS model in the Relational Pooling for Graph Representations paper on the HIV dataset dataset? | AUC |
What metrics were used to measure the elEmBERT-V1 model in the Structure to Property: Chemical Element Embeddings and a Deep Learning Approach for Accurate Prediction of Chemical Properties paper on the Tox21 dataset? | AUC |
What metrics were used to measure the SSVAE with multiple SMILES model in the All SMILES Variational Autoencoder paper on the Tox21 dataset? | AUC |
What metrics were used to measure the Ensemble predictor model in the ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors paper on the Tox21 dataset? | AUC |
What metrics were used to measure the TrimNet model in the TrimNet: learning molecular representation from triplet messages for biomedicine paper on the Tox21 dataset? | AUC |
What metrics were used to measure the GraphConv + dummy super node model in the Learning Graph-Level Representation for Drug Discovery paper on the Tox21 dataset? | AUC |
What metrics were used to measure the GraphConv model in the Convolutional Networks on Graphs for Learning Molecular Fingerprints paper on the Tox21 dataset? | AUC |
What metrics were used to measure the SNN (SELU Network) model in the Self-Normalizing Neural Networks paper on the Tox21 dataset? | AUC |
What metrics were used to measure the ContextPred model in the Strategies for Pre-training Graph Neural Networks paper on the Tox21 dataset? | AUC |
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