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Dataset results
11 results for “Fine-Tune model”
Transfer fine-tuned BERT models by paraphrases
<p>Transfer fine-tuned BERT models by phrasal paraphrases. </p> <ul> <li>transferFT_bert-base-uncased.pkl bases on the bert-base-uncased model</li> <li>transferFT_bert-large-uncased.pkl bases on the bert-large-uncased model</li> </ul> <p>For usage, please refer to our GitHub page.</p> <p><a href="https://github.com/yukiar/TransferFT">https://github.com/yukiar/TransferFT</a></p> <p>For details of these models, please refer to our paper.</p> <p>Yuki Arase and Junichi Tsujii. 2019. Transfer Fine-Tuning: A BERT Case Study. in Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP 2019).</p> <p><a href="https://arxiv.org/abs/1909.00931">https://arxiv.org/abs/1909.00931</a></p>
PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data
<p>Describes the taxonomy used in the paper "PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data", presented at the Second Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data<em> </em>at the Extended Semantic Web Conference (ESWC) 2024.</p>
Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)
<p>Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)</p>
Comprehensive large-scale datasets for 26 viral families for fine-tuning BERT-infect models
<p>These datasets were constructed in the paper "Hidden Challenges in Evaluating Spillover Risk of Zoonotic Viruses using Machine Learning Models" (doi: https://doi.org/10.1101/2024.04.25.591033). The details were also described in the git-hub (https://github.com/Junna-Kawasaki/BERT-infect_2024).</p> <ul> <li>The compressed files, such as ${virus}.tar.xz, contain fasta and genbank files.</li> </ul>
(supplementary material) Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation
<div> <div> <div> <div>Supplementary material for paper <strong>"Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation"</strong></div> <div> </div> <div> <div> <div>The script for the paper can be found in this GitHub repository: https://github.com/awsm-research/LLM-for-code-review-automatiton</div> </div> </div> </div> </div> </div>
"An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model" train and test data
<ul><li>Model for the article "An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model".</li><li>The .pth file is the pre-trained PtyNet-S model and the fine-tuned PtyNet-B model.</li><li>Please contact panxy@ihep.ac.cn if you have any questions.</li></ul>
Black-box Membership Inference Attacks against Fine-tuned Diffusion Models
<p>We have provided some fine-tuned model checkpoints and datasets to help readers reproduce the experiments presented in the paper.</p>
No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
<p>Data and models part of the replication package of the ICSE 24 submission entitled "<em>No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models</em>".</p>
CodeQual: A dataset for fine-tuning Large Language Models for code quality assessment task
Open the record for dataset details and reuse information.
Post-Translational Modification Prediction via Prompt-Based Fine-Tuning of a GPT-2 Model
<p>Training and Benchmark datasets for 19 PTMGPT2 models</p>
Model-driven design of synthetic N-terminal coding sequences for fine-tuning gene expression in yeast and bacteria
GEO Series GSE186378. Bacillus subtilis; Saccharomyces cerevisiae. 4 samples. Type: Other.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.