# Pre-trained model

A robot strategy model trained based on the LeRobot framework, suitable for robot control tasks

This is an automatically generated transformers model card, lacking specific model information.

OpenBA is an open-source 15-billion-parameter bilingual asymmetric sequence-to-sequence model, pre-trained from scratch.

A PyTorch-based semantic segmentation model supporting various encoder architectures, suitable for image segmentation tasks.

A PyTorch-based FPN image segmentation model supporting various encoder architectures, suitable for semantic segmentation tasks.

This is a transformers model card automatically generated by the system. Specific model information is to be supplemented.

This is a transformer model card automatically generated by the system, and specific information is to be supplemented

This is a Transformers model released by Hugging Face. The specific functions and uses are to be supplemented.

VesselFM is a foundation model for universal 3D vascular segmentation in any imaging domain.

This is a monolingual Czech SpeechT5 base model, pre-trained on 120,000 hours of Czech audio and a 17.5 billion-word text corpus, designed as a starting point for Czech TTS fine-tuning.

CuBERT is a context embedding model based on Python code, specifically designed for source code analysis tasks.

This is a transformers model hosted on Hugging Face Hub, with no specific functionality or purpose clearly stated.

A foundational speech recognition model based on the Wav2Vec2 architecture, trained on 960 hours of English speech data, suitable for speech classification tasks.

An emotion classification model for English text that can identify 20 different emotional states.

An image classification model based on the Hugging Face platform, capable of recognizing common objects such as animals and daily items

A vision image classification model trained on the ImageNet-1k dataset, capable of recognizing multiple common object categories.

A fine-tuned model based on the Deltalm foundation within the Fengshen framework, integrating Chinese-English datasets and iwslt Chinese-English parallel corpora for English-to-Chinese translation.

An image classification model built on PyTorch and HuggingPics, specifically designed for food classification

TrOCR is a Transformer-based optical character recognition model designed for single-line text images, fine-tuned on multiple standard datasets.

A text summarization model fine-tuned on Vietnamese news datasets based on the ViT5-Base model, supporting automatic summarization of Vietnamese texts.

This is an English-to-Chinese translation model fine-tuned based on the Helsinki-NLP/opus-mt-en-zh pre-trained model, using the Tsinghua University Open Chinese Lexicon (THUOCL) dataset for fine-tuning.

This is a tiny version of the BERT model, case-insensitive, suitable for natural language processing tasks in resource-constrained environments.

KoSimCSE-BERT-multitask is a high-performance Korean sentence embedding model based on BERT architecture and optimized with multi-task learning strategy, specifically designed for generating high-quality Korean sentence embeddings.

A Korean sentence vector embedding model based on the RoBERTa architecture, optimized for sentence representation through contrastive learning, suitable for tasks such as semantic similarity calculation.

An image classification model based on PyTorch that can accurately identify pictures of different room types.

A BERT base model for the English language, pre-trained using the Masked Language Modeling (MLM) objective, case-insensitive.

A Dutch pre-trained BERT model developed by the University of Groningen, suitable for various Dutch NLP tasks.

TrOCR is a Transformer-based pre-trained model for Optical Character Recognition (OCR) tasks.

A RoBERTa model pre-trained on Korean, suitable for Korean understanding tasks.

A pre-trained natural language inference model based on the MiniLMv2 architecture, used to determine the relationship between sentence pairs (contradiction/entailment/neutral).