Update README.md

README.md

@@ -32,3 +32,38 @@ dataset_info:
   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}
+}
+```