2673 models match the criteria

This is a POS tagging model fine-tuned based on the Indonesian RoBERTa model, trained on the indonlu dataset for Indonesian text POS tagging tasks.

An image classification model built with PyTorch and HuggingPics for recognizing gender in images

This model is a voice command recognition model fine-tuned on the speech_commands dataset based on facebook/wav2vec2-base, achieving an accuracy of 97.59%.

A speech recognition model fine-tuned on Filipino speech datasets based on facebook/wav2vec2-xls-r-300m

This is an image classification model based on the PyTorch framework and generated using HuggingPics tools, specifically designed for gender classification tasks.

AraBERT is an Arabic pre-trained language model based on the BERT architecture, specifically optimized for Arabic language understanding tasks.

A medium-sized multilingual model from the BLOOMZ series, suitable for various natural language processing tasks.

A speech language identification model fine-tuned on OpenAI Whisper-medium, achieving 88.05% accuracy on the FLEURS dataset

Distil-Whisper is a knowledge-distilled version of Whisper large-v3, focusing on English automatic speech recognition, offering faster inference speeds while maintaining accuracy close to the original model.

A financial news sentiment analysis model fine-tuned based on DistilRoBERTa, with an accuracy rate of 98.23%.

A multilingual named entity recognition model combining neural networks and knowledge bases, supporting 9 languages

A general language identification model fine-tuned based on openai/whisper-small, achieving 88.6% accuracy on the evaluation dataset

Distil-Whisper is a distilled version of the Whisper model, 6 times faster than the original, with a 49% reduction in size, while maintaining performance close to the original in English speech recognition tasks.

An image classification model for categorizing human skin types, committed to fairness to ensure accurate performance across all skin tones.

This model is a fine-tuned version based on ibert-roberta-base, specifically designed for detecting abusive or threatening speech.

An automatic speech recognition model fine-tuned on the LIBRISPEECH_ASR - CLEAN dataset based on microsoft/wavlm-base-plus

This is a multilingual news classification model that can classify news content in Norwegian, Swedish, and English according to IPTC news codes, supporting 16 predefined categories.

A BPMN process information extraction model fine-tuned based on bert-base-cased, used to extract key elements such as executors and tasks from textual process descriptions

A Nynorsk automatic speech recognition model fine-tuned based on Facebook/Meta's XLS-R feature extractor, achieving a WER of 11.32% on the NPSC test set.

CLIP model based on ConvNeXt-Large architecture, trained on LAION-2B dataset, supporting zero-shot image classification and image-text retrieval tasks

Large-scale ConvNeXt-Large CLIP model trained on LAION-2B dataset, supporting zero-shot image classification and image-text retrieval tasks

A vision-language model trained on the English subset of LAION-2B using the OpenCLIP framework, supporting zero-shot image classification and image-text retrieval

A 300M-parameter speech recognition model fine-tuned on the VoxRex feature extractor, optimized for Nynorsk (New Norwegian), achieving a WER of 12.22% on the NPSC test set

An object detection model based on YOLOv8m, specifically designed for table extraction tasks, capable of detecting both bordered and borderless tables.

Lightweight license plate detection model based on YOLOv5n, optimized for license plate recognition tasks

A table detection model based on YOLOv8s, capable of accurately identifying bordered and borderless tables in images.

A lightweight named entity recognition model fine-tuned based on DistilBERT, balancing performance and efficiency

Distil-Whisper is a distilled version of the Whisper model, achieving 6x speedup and 49% size reduction with only a 1% WER difference on out-of-distribution evaluation sets.

CLIP model based on ConvNeXt-Base architecture, trained on a subset of LAION-5B using OpenCLIP, focusing on zero-shot image classification tasks

A speech emotion recognition model fine-tuned on Wav2Vec 2.0, capable of identifying 8 English emotions with an accuracy of 82.23% on the RAVDESS dataset

A gender classification model fine-tuned on distilbert-base-uncased, achieving an accuracy of 1.0 on the evaluation set

Distil-Whisper is a distilled version of the Whisper model, 6x faster with 49% smaller size, achieving near 1% WER on out-of-distribution evaluation sets.

English-Filipino speech recognition model fine-tuned from jonatasgrosman/wav2vec2-large-xlsr-53-english, achieving a WER of 0.5750 on the evaluation set

A YOLOv8s fine-tuned model specialized for locating signatures in document images

Norwegian automatic speech recognition model developed by the National Library of Norway based on OpenAI Whisper, specifically optimized for verbatim transcription scenarios, outputting all-lowercase, punctuation-free text

Norwegian automatic speech recognition model fine-tuned based on Facebook/Meta's XLS-R feature extractor, achieving a 6.33% word error rate on the NPSC test set

BioMistral is an open-source large language model optimized for the medical domain based on the Mistral architecture, further pre-trained on PubMed Central open-access text data, supporting multilingual medical question-answering tasks.

AraGPT2 is a Transformer-based Arabic text generation pre-trained model developed by AUB MIND Lab, supporting multiple model variants of different sizes.

The CNER model is a named entity recognition model based on the DeBERTa-v3-base architecture, capable of jointly identifying and classifying concepts and named entities with fine-grained labels.

FullStop is a Transformer-based multilingual punctuation prediction model that supports English, German, French, Italian, Dutch, and other languages.

This is an experimental model designed to correct spelling errors and punctuation in English text.

A pre-trained model based on Google's Vision Transformer (ViT) architecture, fine-tuned on the CIFAR-10 dataset for image classification tasks.