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diff --git a/text_recognizer/networks/transformer/embeddings/rotary.py b/text_recognizer/networks/transformer/embeddings/rotary.py
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+++ b/text_recognizer/networks/transformer/embeddings/rotary.py
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+"""Roatary embedding.
+
+Stolen from lucidrains:
+    https://github.com/lucidrains/rotary-embedding-torch
+
+Explanation of roatary:
+    https://blog.eleuther.ai/rotary-embeddings/
+"""
+from typing import Tuple
+
+from einops import rearrange
+import torch
+from torch import nn
+from torch import Tensor
+
+
+class RotaryEmbedding(nn.Module):
+    """Rotary positional embedding."""
+
+    def __init__(self, dim: int):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+    def forward(self, x: Tensor, seq_dim: int = 1) -> Tensor:
+        """Encodes tensor x with rotary embeddings."""
+        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq)
+        freqs = torch.einsum("i , j -> i j", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return rearrange(emb, "n d -> () () n d")
+
+
+def rotate_half(x: Tensor) -> Tensor:
+    x = rearrange(x, "... (j d) -> ... j d", j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(t: Tensor, freqs: Tensor) -> Tensor:
+    seq_len = t.shape[-2]
+    freqs = freqs[:, :, -seq_len:]
+    return (t * freqs.cos()) + (rotate_half(t) * freqs.sin())