Solve

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Average Model Cost: $0.0000

Number of Runs: 9,371

Models by this creator

vit-zigzag-attribute-768dim-patch16-224

vit-zigzag-attribute-768dim-patch16-224

solve

The vit-zigzag-attribute-768dim-patch16-224 model is a vision transformer model trained specifically for feature extraction. It is based on the Vision Transformer (ViT) architecture and has a patch size of 16x16 and an image size of 224x224. The model generates a 768-dimensional feature representation for a given image, which can be used for various downstream tasks such as image classification, object detection, and image segmentation.

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$-/run

9.4K

Huggingface

wav2vec2-base-timit-demo-sol

wav2vec2-base-timit-demo-sol

wav2vec2-base-timit-demo-sol This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: Loss: 0.3922 Wer: 0.2862 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 0.0001 train_batch_size: 64 eval_batch_size: 8 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear lr_scheduler_warmup_steps: 1000 num_epochs: 30 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.17.0 Pytorch 1.10.1+cu102 Datasets 1.18.3 Tokenizers 0.12.1

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$-/run

2

Huggingface

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