Dataset

This dataset was created by Megha Kapoor

Contents

Description

This is the dataset repository used in the pyiqa toolbox. Please refer to Awesome Image Quality Assessment for details of each dataset Example commandline script with huggingface-cli: huggingface-cli download chaofengc/IQA-PyTorch-Datasets live.tgz --local-dir ./datasets --repo-type dataset cd datasets tar -xzvf live.tgz

  Disclaimer for This Dataset Collection

This collection of datasets is compiled and maintained for academic, research, and educational… See the full description on the dataset page: https://huggingface.co/datasets/chaofengc/IQA-PyTorch-Datasets.

forked from https://github.com/huggingface/transformers

pytorch-image-models metrics

This dataset contains metrics about the huggingface/pytorch-image-models package. Number of repositories in the dataset: 3615 Number of packages in the dataset: 89

  Package dependents

This contains the data available in the used-by tab on GitHub.

  Package & Repository star count

This section shows the package and repository star count, individually.

Package Repository

There are 18 packages that have more than 1000… See the full description on the dataset page: https://huggingface.co/datasets/open-source-metrics/pytorch-image-models-dependents.

!python -m pip install --upgrade /kaggle/input/pytorchhuggingface-wheels-cuda-116/*.whl

The SloNER is a model for Slovenian Named Entity Recognition. It is is a PyTorch neural network model, intended for usage with the HuggingFace transformers library (https://github.com/huggingface/transformers).

The model is based on the Slovenian RoBERTa contextual embeddings model SloBERTa 2.0 (http://hdl.handle.net/11356/1397). The model was trained on the SUK 1.0 training corpus (http://hdl.handle.net/11356/1747).The source code of the model is available on GitHub repository https://github.com/clarinsi/SloNER.

!python -m pip install --upgrade /kaggle/input/pytorchhuggingface-wheels/*.whl

kye/all-pytorch-code dataset hosted on Hugging Face and contributed by the HF Datasets community

Deberta v3 PyTorch model from huggingface:

https://huggingface.co/microsoft/deberta-v3-base/tree/main

Meant to be used for competitions where internet access is disallowed.

Context

Accelerate is a Python library that allows running raw PyTorch training scripts on any kind of device very easily. It allows easy integration into your code. More details are here - https://huggingface.co/blog/accelerate-library

pytorch-survival/gene_annotations dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by RAHUL BAJAJ

Contents

To import pretrained transformer weights, simply specify the path in the corresponding function: model_path = '../input/transformers/roberta-base' model = AutoModel.from_pretrained(model_path) See this notebook for a more detailed example.

The dataset includes the following weights, configs and tokenizers: - albert-large-v2 - bert-base-uncased - bert-large-uncased - distilroberta-base - distilbert-base-uncased - google/electra-base-discriminator - facebook/bart-base - facebook/bart-large - funnel-transformer/small - funnel-transformer/large - roberta-base - roberta-large - t5-base - t5-large - xlnet-base-cased - xlnet-large-cased

All files are downloaded from Huggingface Model Hub at https://huggingface.co/models. License: Apache License 2.0

i44p/dped-pytorch dataset hosted on Hugging Face and contributed by the HF Datasets community

All the models are taken directly from Huggingface Hub

Crayon2023/pytorch-llama dataset hosted on Hugging Face and contributed by the HF Datasets community

Pretrained language models for detecting and classifying the presence of sex education concepts in Slovene curriculum documents. The models are PyTorch neural network models, intended for usage with the HuggingFace transformers library (https://github.com/huggingface/transformers).

The models are based on the Slovenian RoBERTa contextual embeddings model SloBERTa 2.0 (http://hdl.handle.net/11356/1397) and on the CroSloEngual BERT model (http://hdl.handle.net/11356/1330). The source code of the model and example usage is available in GitHub repository https://github.com/TimotejK/SemSex. The models and tokenizers can be loaded using the AutoModelForSequenceClassification.from_pretrained() and the AutoTokenizer.from_pretrained() functions from the transformers library. An example of such usage is available at https://github.com/TimotejK/SemSex/blob/main/Concept%20detection/Classifiers/full_pipeline.py.

The corpus on which these models have been trained is available at http://hdl.handle.net/11356/1895.

Dataset

This dataset was created by Hari Prasath V

Contents

kye/pytorch-repo-code dataset hosted on Hugging Face and contributed by the HF Datasets community

Crayon2023/pytorch-Qwen-7B dataset hosted on Hugging Face and contributed by the HF Datasets community