# High Accuracy

A text classification model fine-tuned on distilbert-base-uncased, trained on the AG News dataset with an accuracy of 89.06%

Qwen2.5-Coder-7B-Instruct is a 7B-parameter code generation model based on the Qwen2.5 architecture, specializing in instruction fine-tuning, suitable for code generation and programming assistance tasks.

This is a Korean sentiment analysis model fine-tuned from a multilingual BERT model, specifically designed for analyzing the sentiment tendencies of Korean customer reviews.

A video analysis model fine-tuned based on MCG-NJU/videomae-base for deception detection tasks, achieving an accuracy of 70.37%

DeBERTa-v3-small is a lightweight variant of the DeBERTa model released by Microsoft, suitable for text classification tasks.

A fine-tuned DistilBERT-base-uncased food/non-food text classifier achieving 100% accuracy on the evaluation set

This is a sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 93.2% accuracy on the evaluation set.

This is an English spam SMS classification model fine-tuned from the bert-base-uncased model, capable of accurately distinguishing between spam and legitimate SMS messages.

This model is a fine-tuned version based on MCG-NJU/videomae-base-finetuned-kinetics, primarily used for video understanding tasks, achieving an accuracy of 91.38% on the evaluation set.

A video understanding model fine-tuned based on MCG-NJU/videomae-base-finetuned-kinetics, achieving 91.23% accuracy on the evaluation set

Video classification model fine-tuned on a subset of UCF101 based on the VideoMAE base model

A video understanding model fine-tuned based on MCG-NJU/videomae-base-finetuned-kinetics, achieving an accuracy of 90.97% on the evaluation set

A financial document classification model fine-tuned based on XLM-RoBERTa-Large, supporting classification tasks for 37 document types.

A DistilBERT-based text classification model for distinguishing between food and non-food texts

Transformer model based on Distilbert architecture, specializing in Brazilian Portuguese sentiment analysis

Transformer model based on Distilbert architecture, specialized in sentiment analysis for Brazilian Portuguese

A financial domain sentiment analysis model fine-tuned based on econo-sentence-v2, achieving 96.2% accuracy on the Financial Phrase Bank dataset

MathFusionQA is a mathematical problem-solving model based on deepseek-math-7b-base, enhancing the mathematical problem-solving capabilities of large language models through instruction fusion.

A video understanding model fine-tuned based on MCG-NJU/videomae-base, specialized in sign language recognition tasks

An audio classification model fine-tuned based on MIT/ast-finetuned-audioset-10-10-0.4593, specializing in heart sound classification tasks, achieving 96.95% accuracy on the validation set.

DeepSeek-R1 is an improved version based on the deepseek-ai foundation model, specializing in audio-to-text transcription tasks and supporting English language processing.

Fine-tuned YouTube comment sentiment analysis model based on cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual, with an accuracy of 80.17%

A sentiment analysis model fine-tuned based on distilbert-base-uncased, achieving an accuracy of 85.67% on the evaluation set

A lightweight text sentiment analysis model based on DistilBERT, specifically fine-tuned for book review data

A deepfake audio detection model fine-tuned based on wav2vec2-base, achieving 99.66% accuracy

This is a sentiment analysis model fine-tuned based on BERT, specifically designed to analyze sentiment tendencies (positive or negative) in IMDb movie reviews.

A fine-tuned speech processing model based on facebook/wav2vec2-base, achieving 99.48% accuracy on the evaluation set

This model is an audio classification model fine-tuned on the GTZAN music genre classification dataset based on DistilHuBERT, with an accuracy of 84%.

An audio classification model based on the DistilHuBERT architecture, fine-tuned on the GTZAN dataset for music genre classification tasks.

A question classifier fine-tuned based on IBM Granite embedding model, used to distinguish between general questions and directed questions

An image classification model built with PyTorch and HuggingPics, capable of accurately distinguishing between pictures of cats and dogs.

A fine-tuned audio classification model based on openai/whisper-tiny, excelling in voice command datasets

A Turkish sentiment analysis model fine-tuned on dbmdz/bert-base-turkish-cased, supporting negative, neutral, and positive sentiment classification.

InternLM3-8B-Instruct is an 8-billion-parameter instruction model developed by Shanghai AI Laboratory, designed for general-purpose use and advanced reasoning, featuring high efficiency and low cost.

A video understanding model fine-tuned on a subset of the UCF101 action recognition dataset based on the VideoMAE base model

A sentiment analysis model fine-tuned on distilbert-base-uncased, achieving 94% accuracy on the evaluation set

A zero-shot classification model fine-tuned on the facebook/xnli German dataset based on deepset/gbert-large

A multilingual depression detection model based on XLM-RoBERTa, supporting text classification in 6 languages for identifying depression indicators

Audio classification model fine-tuned on the GTZAN music classification dataset based on the DistilHuBERT architecture, achieving 86% accuracy

This model is a fine-tuned version of distilbert-base-uncased on an emotions dataset, designed for text sentiment classification.