autoencoder
p2-etf-variational-autoencoder-resultslegal-ir-autoencoder-checkpoints
Legal IR Autoencoder Checkpoints
This dataset stores checkpoint artifacts for the legal text -> formal logic IR autoencoder/Codex optimization loop.
Latest checkpoint in this upload: checkpoints/20260630T221836Z/.
Contents
state/legal-ir-autoencoder-canonical.state.json: canonical feature-level autoencoder warm-start state.
reports/: weight review, deprecation manifest, and consensus feature manifests.
scripts/review_autoencoder_weight_runs.py: script used to… See the full description on the dataset page: https://huggingface.co/datasets/justicedao/legal-ir-autoencoder-checkpoints.AutoencoderDataset
AutoencoderDataset
Data for training molvae — a SELFIES molecular VAE (matryoshka latent) for Bayesian
optimization of mechanophores. Two layers:
raw/ — the source chemical databases (SMILES/SELFIES, pre-tokenized), as collected.
mixes/ — the derived training datasets: weighted, shuffled, materialized token-shard
"mixes" built from the raw sources. Each mix is documented by its config + README + build
code, so it is fully reproducible. The multi-GB token shards themselves live… See the full description on the dataset page: https://huggingface.co/datasets/MechanophoresResearch/AutoencoderDataset.laplace-autoencoders-dataset
Transient CH₄ concentration fields
Simulation data used by Laplace autoencoders: transient CH₄ concentration fields U(t)
as a function of physical parameters θ = (k, A, C). The simulations were generated with
OpenFOAM.
Code: github.com/keyvanatt/laplace-autoencoders
Checkpoints: keyvanatt/laplace-autoencoders-checkpoints
Files
File
Shape
Content
doe.npy
(225,) structured (k, A, C)
design of experiments: 15 values of k × 15 values of C, A = 0… See the full description on the dataset page: https://huggingface.co/datasets/keyvanatt/laplace-autoencoders-dataset.autoencoder-paraphrase-dataset
Dataset Card for Machine Paraphrase Dataset (MPC)
Dataset Summary
The Autoencoder Paraphrase Corpus (APC) consists of ~200k examples of original, and paraphrases using three neural language models.
It uses three models (BERT, RoBERTa, Longformer) on three source texts (Wikipedia, arXiv, student theses).
The examples are aligned, i.e., we sample the same paragraphs for originals and paraphrased versions.
How to use it
You can load the dataset using the… See the full description on the dataset page: https://huggingface.co/datasets/jpwahle/autoencoder-paraphrase-dataset.uscode-autoencoder-sparse