Dataset Card for eoinf/pile_tok4096_200m

Original dataset

Original dataset: monology/pile-uncopyrighted

Dataset Details

• Total Tokens: 200,018,944
• Total Sequences: 195,331
• Context Length: 1024 tokens
• Tokenizer: eoinf/pile_tokenizer_4096
• Format: Each example contains a single field tokens with a list of 1024 token IDs

Preprocessing

Each document was:

1. Tokenized using the eoinf/pile_tokenizer_4096 tokenizer
2. Prefixed with a BOS (beginning of sequence) token
3. Suffixed with an EOS (end of sequence) token
4. Packed into fixed-length sequences of 1024 tokens

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("eoinf/pile_tok4096_200m")

# Access training data
train_data = dataset["train"]
print(train_data[0]["tokens"])  # First sequence

Use with PyTorch

import torch
from datasets import load_dataset
from torch.utils.data import DataLoader

dataset = load_dataset("eoinf/pile_tok4096_200m", split="train")

# Convert to PyTorch tensors
dataset.set_format(type="torch", columns=["tokens"])

# Create DataLoader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

for batch in dataloader:
    tokens = batch["tokens"]