Datasetpublic

Kalso42/WorldModelForMaze

WorldModelForMaze Code, datasets, and trained checkpoints for studying world-model representations in maze navigation, based on a modified NanoGPT. Contents *.py — training, testing, probing, and visualization scripts (see readme.md). model/ — architectures: transformer, transformer-rope, transformer-nextlat, mamba, mamba2, gated-deltanet, gru. data/maze/100/ — tokenized maze datasets for Tasks A/C/E/H/I (RWs paths, 100 nodes). out/ — final (10000-iter)… See the full description on the dataset page: https://huggingface.co/datasets/Kalso42/WorldModelForMaze.

sourceHugging Facemitupdated 3mo agoView on Hugging Face

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rootlogger.py

logger.py17 linesDownload Raw Back to root

1import logging
2import torch.nn as nn
3
4def get_logger(filename, verbosity=0, name=None):
5    level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}
6    formatter = logging.Formatter(
7        "[%(asctime)s][%(levelname)s] %(message)s"
8    )
9    logger = logging.getLogger(name)
10    logger.setLevel(level_dict[verbosity])
11
12    fh = logging.FileHandler(filename, "w")
13    fh.setLevel(level_dict[verbosity]) 
14    fh.setFormatter(formatter)
15    logger.addHandler(fh)
16
17    return logger