Search Results for "using class net.sourceforge.jtds.jdbc.driver"
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Showing 6 open source projects for "using class net.sourceforge.jtds.jdbc.driver"
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vit-age-classifier
Vision Transformer model fine-tuned for facial age classification
vit-age-classifier is a Vision Transformer (ViT) model fine-tuned by nateraw to classify a person's age based on their facial image. Trained on the FairFace dataset, the model predicts age group categories using facial features with high accuracy. It leverages the robust image representation capabilities of ViT for fine-grained facial analysis. With 85.8 million parameters, the model operates efficiently for image classification tasks on faces. The model outputs probabilities for predefined age... -
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t5-base
Flexible text-to-text transformer model for multilingual NLP tasks
t5-base is a pre-trained transformer model from Google’s T5 (Text-To-Text Transfer Transformer) family that reframes all NLP tasks into a unified text-to-text format. With 220 million parameters, it can handle a wide range of tasks, including translation, summarization, question answering, and classification. Unlike traditional models like BERT, which output class labels or spans, T5 always generates text outputs. It was trained on the C4 dataset, along with a variety of supervised NLP... -
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vit-base-patch16-224
Transformer model for image classification with patch-based input.
vit-base-patch16-224 is a Vision Transformer (ViT) model developed by Google for image classification tasks. It was pretrained on ImageNet-21k (14 million images, 21,843 classes) and fine-tuned on ImageNet-1k (1 million images, 1,000 classes), both using 224x224 resolution. The model treats images as sequences of 16x16 pixel patches, which are linearly embedded and processed using a transformer encoder. A special [CLS] token is used to summarize the image for classification. ViT learns high... -
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BLEURT-20-D12
Custom BLEURT model for evaluating text similarity using PyTorch
BLEURT-20-D12 is a PyTorch implementation of BLEURT, a model designed to assess the semantic similarity between two text sequences. It serves as an automatic evaluation metric for natural language generation tasks like summarization and translation. The model predicts a score indicating how similar a candidate sentence is to a reference sentence, with higher scores indicating greater semantic overlap. Unlike standard BLEURT models from TensorFlow, this version is built from a custom PyTorch... -
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segmentation-3.0
Speaker segmentation model for 10s audio chunks with powerset labels
segmentation-3.0 is a voice activity and speaker segmentation model from the pyannote.audio framework, designed to analyze 10-second mono audio sampled at 16kHz. It outputs a (num_frames, num_classes) matrix using a powerset encoding that includes non-speech, individual speakers, and overlapping speech for up to three speakers. Trained with pyannote.audio 3.0.0 on a rich blend of datasets—including AISHELL, DIHARD, VoxConverse, and more—it enables downstream tasks like voice activity detection... -
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resnet50.a1_in1k
Zero-shot image-text classification with ViT-B/32 encoder.
clip-vit-base-patch32 is a zero-shot image classification model from OpenAI based on the CLIP (Contrastive Language–Image Pretraining) framework. It uses a Vision Transformer with base size and 32x32 patches (ViT-B/32) as the image encoder and a masked self-attention transformer as the text encoder. These components are jointly trained using contrastive loss to align images and text in a shared embedding space. The model excels in generalizing across tasks without additional fine-tuning...
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