Microsoft
Rank:Average Model Cost: $0.0037
Number of Runs: 26,081,737
Models by this creator
layoutlmv3-base
layoutlmv3-base
LayoutLMv3-base is a pre-trained model designed for document layout analysis tasks. It uses a transformer-based architecture to recognize textual and visual elements in documents, such as text blocks, tables, and images. The model can be fine-tuned on specific document analysis tasks, such as document classification, entity recognition, or table understanding. It achieves state-of-the-art performance on popular document datasets and can be used as a starting point for building document understanding systems.
$-/run
7.8M
Huggingface
layoutlm-base-uncased
layoutlm-base-uncased
LayoutLM is a pre-trained model designed for document layout understanding tasks, such as document classification, information extraction, and table recognition. It combines text and layout information by taking advantage of an integrated text and layout embedding learning framework. LayoutLM can be fine-tuned on specific downstream tasks to achieve high performance in tasks related to document analysis. The 'layoutlm-base-uncased' model is the base version of LayoutLM that has been trained on a large corpus of text and layout data and can be further fine-tuned on specific documents and tasks.
$-/run
3.7M
Huggingface
BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext is a language model specifically designed for biomedical text analysis. It is based on the BERT (Bidirectional Encoder Representations from Transformers) architecture and is trained on a large corpus of biomedical literature from PubMed. The model can be used for various natural language processing tasks, such as filling in masked tokens in a given text (fill-mask task). It has shown good performance in understanding and extracting information from biomedical literature.
$-/run
1.4M
Huggingface
deberta-large-mnli
deberta-large-mnli
DeBERTa-large-MNLI is a text classification model that is based on the DeBERTa-large architecture. It has been fine-tuned on the Multi-Genre Natural Language Inference (MNLI) dataset. This model can be used to perform various natural language understanding tasks, such as sentence pair classification, and can be particularly useful for tasks that require accurate understanding of sentence relationships and inferences.
$-/run
1.2M
Huggingface
beit-base-patch16-224-pt22k-ft22k
beit-base-patch16-224-pt22k-ft22k
The beit-base-patch16-224-pt22k-ft22k model is an image-classification model that has been trained on a large dataset. It uses an architecture called BEit, which combines vision and language in order to better understand and classify images. The model takes in an image as input and outputs a set of labels that describe what is in the image. It has been fine-tuned on a dataset with 22,000 classes, allowing it to recognize and classify a wide variety of objects and concepts in images.
$-/run
742.5K
Huggingface
bringing-old-photos-back-to-life
The model presented in this research is focused on bringing old photos back to life. It aims to improve the quality and restore details in old, damaged, and low-resolution photographs. The model leverages the power of Deep Learning, specifically Generative Adversarial Networks (GANs), to achieve this. The GAN consists of two primary components: a generator and a discriminator. The generator network takes a low-resolution or damaged input image as its input and generates a high-resolution, restored version of the image. The discriminator network receives both the generated image and the original high-resolution image, and its task is to determine which one is real and which one is fake. Through the adversarial training between the generator and the discriminator, the model learns to generate realistic and sharp images that closely resemble the original photograph. The research also proposes a new dataset, called "Old Photo Restoration Dataset" (OPRD), which consists of old and deteriorated photos paired with their corresponding high-resolution images. The proposed model achieves impressive results, surpassing previous methods in image restoration tasks and demonstrating the potential for bringing old photographs back to life.
$0.037/run
693.0K
Replicate
wavlm-large
wavlm-large
The model wavlm-large is a large-scale deep learning model that is capable of performing feature extraction on audio waveforms. It is specifically designed for audio processing tasks and can extract high-level features from audio signals. The model is trained on a large dataset and uses advanced techniques to capture complex patterns and structures in audio data. It can be used for tasks such as speech recognition, music classification, and audio synthesis. Overall, wavlm-large is a powerful tool for analyzing and processing audio waveforms.
$-/run
639.5K
Huggingface
trocr-base-handwritten
$-/run
506.5K
Huggingface