Update README.md with How to use

README.md

@@ -22,9 +22,52 @@ tissue-specific promoters and enhancers, and CTCF-bound sites) elements.
 
 ### How to use
 
-To Be Done
+Until its next release, the transformers library needs to be installed from source with the following command in order to use the models. PyTorch should also be installed in order to one-hot encode the input sequences.
 
+```
+pip install --upgrade git+https://github.com/huggingface/transformers.git
+pip install torch
+```
+
+A small snippet of code is given here in order to retrieve both logits from dummy DNA sequences.
 
+```
+import torch
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("InstaDeepAI/segment_enformer", trust_remote_code=True)
+
+def encode_sequences(sequences):
+    one_hot_map = {
+        'a': torch.tensor([1., 0., 0., 0.]),
+        'c': torch.tensor([0., 1., 0., 0.]),
+        'g': torch.tensor([0., 0., 1., 0.]),
+        't': torch.tensor([0., 0., 0., 1.]),
+        'n': torch.tensor([0., 0., 0., 0.]),
+        'A': torch.tensor([1., 0., 0., 0.]),
+        'C': torch.tensor([0., 1., 0., 0.]),
+        'G': torch.tensor([0., 0., 1., 0.]),
+        'T': torch.tensor([0., 0., 0., 1.]),
+        'N': torch.tensor([0., 0., 0., 0.])
+    }
+
+    def encode_sequence(seq_str):
+        one_hot_list = []
+        for char in seq_str:
+            one_hot_vector = one_hot_map.get(char, torch.tensor([0.25, 0.25, 0.25, 0.25]))
+            one_hot_list.append(one_hot_vector)
+        return torch.stack(one_hot_list)
+
+    if isinstance(sequences, list):
+        return torch.stack([encode_sequence(seq) for seq in sequences])
+    else:
+        return encode_sequence(sequences)
+
+sequences = ["A"*196608, "G"*196608]
+one_hot_encoding = encode_sequences(sequences)
+preds = model(one_hot_encoding)
+print(preds['logits'])
+```
 
 ## Training data