# Pre-trained Model

A general-purpose text generation model based on the Transformers library, supporting various natural language processing tasks

This is an automatically generated transformers model card, and the specific information is to be supplemented.

This is an image classification model based on the Apache-2.0 license, specifically designed to recognize 30 different types of plants.

This is a pre-trained time series forecasting model, focusing on time series data analysis and prediction tasks.

This is a medical summarization model trained on the Bilal-Mamji/Medical-summary dataset, primarily used for generating concise summaries from medical texts.

PatchTSMixer is a time series forecasting foundational model under IBM's Granite project, featuring an innovative Patch hybrid architecture suitable for various time series forecasting tasks.

T5 (Text-to-Text Transfer Transformer) is a general-purpose text-to-text conversion model developed by Google, capable of handling various natural language processing tasks.

A Qwen2 model pre-trained on classical Chinese texts for POS tagging tasks, supporting Universal Dependencies annotation.

This is a feature extraction model based on 🤗 transformers. Specific functions and uses require further information.

This is a 🤗 Transformers model card hosted on Hugging Face Hub. The content is automatically generated.

The Chinese HuBERT base model is a pre-trained model for text-to-speech tasks, supporting Chinese speech processing.

Chinese pre-trained model based on RoBERTa architecture, supporting text-to-speech tasks

The AIMI Foundation Model Suite is a collection of foundation models for the radiology domain developed by the Stanford AIMI team, focusing on medical image analysis tasks.

An optical character recognition model based on Hugging Face Transformers, specifically designed for recognizing MNIST-style digit images

ONNX format conversion of Facebook's wav2vec2-base-960h model, designed for Transformers.js, supporting browser-side speech recognition

ReasTAP is a pre-trained model for table reasoning, which injects table reasoning skills through synthetic reasoning examples and is fine-tuned on the WikiTableQuestions dataset

This is a visual image classification model trained on the ImageNet-1k dataset, capable of recognizing various common objects and scenes.

A SpeechT5 speech synthesis (text-to-speech) model fine-tuned on the LibriTTS dataset, supporting high-quality text-to-speech conversion.

A general-purpose image classification model trained on the ImageNet-1k dataset, capable of recognizing various common objects and scenes.

CodeReviewer is a pre-trained model using code changes and code review data, designed to support code review tasks.

CodeT5 is an identifier-aware unified pre-trained encoder-decoder model focused on code understanding and generation tasks.

The VinAI translation model is a pre-trained neural machine translation system for Vietnamese-English and English-Vietnamese translation tasks, currently the most advanced text translation model.

Image classification model based on Transformer architecture, pre-trained and fine-tuned on ImageNet-21k and ImageNet-1k datasets

A convolutional neural network based on the DenseNet architecture, specifically designed for X-ray image classification tasks, achieving dense inter-layer connections through dense blocks.

EfficientNetV2 is a highly efficient convolutional neural network architecture, specially optimized for training speed and parameter efficiency. The 61-channel version is a variant of this architecture.

Chinese version of the PAGASUS-base model specialized in text summarization tasks

IndoBERT is a BERT model specifically optimized for Indonesian, excelling in multiple Indonesian NLP tasks.

KoBERT is a BERT model specifically optimized for Korean, developed by SKT Brain and trained on Korean corpora.

A Korean text summarization model based on the KoBART architecture, capable of generating concise summaries of Korean news articles.

Advanced French language model based on the RoBERTa architecture, compatible with the TensorFlow framework

BETO is a BERT model trained on a large Spanish corpus, supporting both cased and uncased versions, suitable for various Spanish NLP tasks.

SEW (Squeezed and Efficient Wav2vec) is a speech recognition pre-trained model developed by ASAPP Research, outperforming wav2vec 2.0 in both performance and efficiency.

A compact Korean pre-trained RoBERTa model developed by the KLUE benchmark team

AraBERT is a pre-trained language model based on Google's BERT architecture, specifically designed for Arabic natural language understanding tasks.

TrOCR is a Transformer-based pre-trained optical character recognition model that adopts an encoder-decoder architecture, suitable for OCR tasks on single-line text images.

This is a Chinese pre-trained model based on the BigBird architecture, optimized for Chinese text processing and supporting long text sequence handling.

BERTje is a Dutch pre-trained BERT model developed by the University of Groningen, specifically optimized for Dutch language.