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e2b1e36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import numpy as np
import glob
import datasets
_DESCRIPTION = """\
Precomputed hidden state activations from layer 23 of Gemma-3-1B-IT
for the OpenWebText dataset, tokenized with sequence length 1024.
Designed for training a Titans memory layer.
"""
_SHARD_BASE = "shard_[0-9]*.npy"
class OpenwebtextGemma3Tokenized1024ActivationsLayer23(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({
"activations": datasets.Array3D(shape=(1024, 3072), dtype="float32"),
"mask": datasets.Array2D(shape=(1024,), dtype="int32"),
"tokens": datasets.Array2D(shape=(1024,), dtype="int32"),
}),
)
def _split_generators(self, dl_manager):
all_npy = sorted(glob.glob(_SHARD_BASE))
shards = [f for f in all_npy
if not f.endswith("_masks.npy") and not f.endswith("_tokens.npy")]
return [datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"shard_files": shards},
)]
def _generate_examples(self, shard_files):
for shard_path in shard_files:
shard_id = shard_path.split("shard_")[1].split(".npy")[0]
act = np.load(shard_path).astype(np.float32)
mask = np.load(shard_path.replace(".npy", "_masks.npy"))
tokens = np.load(shard_path.replace(".npy", "_tokens.npy"))
for i in range(act.shape[0]):
yield f"{shard_id}_{i}", {
"activations": act[i],
"mask": mask[i],
"tokens": tokens[i],
}
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