Delete openwebtext-gemma3-tokenized-1024-activations-layer23.py

openwebtext-gemma3-tokenized-1024-activations-layer23.py

@@ -1,48 +0,0 @@
-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],
-                }