representation
Debunk_Traffic_Representation
Packet-level classification: classify based on packet
Per-packet-split: Mix all packets and split them into train, val, and test sets, based on 8:1:1
Per-flow-split: Split the pcap files based on 5-tuples (src_IP, dst_IP, src_port, dst_port, and protocol), using 3-fold validation, and there is no intersection between the train, val, and test sets.
Flow-level classification: classify based on flow
quantum-representations
Epsilon-Transformers Belief Analysis Dataset
This dataset contains trained neural network models and their corresponding belief state regression analysis from the Epsilon-Transformers project. The models were trained on four different stochastic processes and analyzed for their ability to learn and represent belief states.
See https://github.com/adamimos/epsilon-transformers/tree/quantum-public for codebase which generated this data.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/SimplexAI/quantum-representations.p2-etf-representation-disentanglement-rl-resultsmemory-representation-contextbench-artifacts
Memory Representation ContextBench Artifacts
Dataset Summary
This repository contains processed artifacts for the paper "Memory as a Map: Prior-Trajectory Representations for Software Engineering Agents." The artifact supports reproduction and inspection of a controlled prior-context representation experiment over SWEContextBench prior-target pairs.
The experiment renders each target under four prompt conditions: no prior context, stripped Claude Code transcript… See the full description on the dataset page: https://huggingface.co/datasets/shshwtsuthar/memory-representation-contextbench-artifacts.FuseChat-Mixture-InternLM2-Chat-20B-Representation
Dataset Card for FuseChat-Mixture
Dataset Description
FuseChat-Mixture is the training dataset used in 📑FuseChat: Knowledge Fusion of Chat Models
FuseChat-Mixture is a comprehensive training dataset covers different styles and capabilities, featuring both human-written and model-generated, and spanning general instruction-following and specific skills. These sources include:
Orca-Best: We sampled 20,000 examples from Orca-Best, which is filtered from the original GPT-4… See the full description on the dataset page: https://huggingface.co/datasets/FuseAI/FuseChat-Mixture-InternLM2-Chat-20B-Representation.vdr-jina-v4-layer-representation