---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: val_dense
    path: data/val_dense-*
  - split: val_sparse
    path: data/val_sparse-*
dataset_info:
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 825600000
    num_examples: 1600000
  - name: val
    num_bytes: 8256000
    num_examples: 16000
  - name: val_dense
    num_bytes: 2064000
    num_examples: 4000
  - name: val_sparse
    num_bytes: 82560000
    num_examples: 160000
  download_size: 354675733
  dataset_size: 918480000
---

Data for [**Flip-Flop Language Modeling**](https://arxiv.org/abs/2306.00946). The task is to correctly execute the sequential operations of a 1-bit register. The Transformer architecture, despite being apparently built for this operation, makes sporadic extrapolation errors (*attention glitches*). An open challenge is to fix these without recourse to long-tailed data or a recurrent architecture. Splits reflect the FFLM setup from the paper:
- `train`: 1.6M sequences from FFL(0.8) *(256 instructions, 80% ignore, 10% read, 10% write)*.
- `val`: 16K sequences from FFL(0.8).
- `val_dense`: 4K sequences from FFL(0.1).
- `val_sparse`: 160K sequences from FFL(0.98).

Usage
---
```python
import torch
import datasets

dataset = datasets.load_dataset('synthseq/flipflop')
dataset['train'][0]  # {'text': 'w1i1w0i0 ...

def tokenize_batch(batch):
    mapping = {'w': 0, 'r': 1, 'i': 2, '0': 3, '1': 4}
    tokenized_batch = [[mapping[char] for char in s] for s in batch['text']]
    return {'tokens': torch.tensor(tokenized_batch, dtype=torch.int64)}

dataset.set_transform(tokenize_batch)
dataset['train'][0]  # {'tokens': tensor([0, 4, 2, 4, 0, 3, 2, 3, 2 ...
```

Citation
---

```
@article{liu2023exposing,
  title={Exposing Attention Glitches with Flip-Flop Language Modeling},
  author={Liu, Bingbin and Ash, Jordan T and Goel, Surbhi and Krishnamurthy, Akshay and Zhang, Cyril},
  journal={arXiv preprint arXiv:2306.00946},
  year={2023}
}
```