README.md

# Dataset Card for eoinf/tokenized_dataset_test2

## Original dataset
Original dataset: monology/pile-uncopyrighted

## Dataset Details

- **Total Tokens**: 51,200
- **Total Sequences**: 50
- **Context Length**: 1024 tokens
- **Tokenizer**: meta-llama/Llama-2-7b-hf
- **Format**: Each example contains a single field `tokens` with a list of 1024 token IDs

## Preprocessing

Each document was:
1. Tokenized using the meta-llama/Llama-2-7b-hf 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
```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("eoinf/tokenized_dataset_test2")

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

## Use with PyTorch
```python
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader

dataset = load_dataset("eoinf/tokenized_dataset_test2", 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"]
```