Update README.md

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}
}
```

README.md

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   download_size: 354675733
   dataset_size: 918480000
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