diff --git a/wtpsplit/extract.py b/wtpsplit/extract.py
index 98a544f5..15339b2a 100644
--- a/wtpsplit/extract.py
+++ b/wtpsplit/extract.py
@@ -58,7 +58,6 @@ def __call__(self, hashed_ids, attention_mask, language_ids=None):
 
         return {"logits": logits}
 
-# TODO: comment
 def extract(
     batch_of_texts,
     model,
@@ -69,6 +68,15 @@ def extract(
     pad_last_batch=False,
     verbose=False,
 ):
+    """
+    Computes logits for the given batch of texts by:
+        1. slicing the texts into chunks of size `block_size`.
+        2. passing every chunk through the model forward.
+        3. stitching predictings back together by averaging chunk logits.
+
+    ad 1.: text is sliced into partially overlapping chunks by moving forward by a `stride` parameter (think conv1d).
+    """
+
     text_lengths = [len(text) for text in batch_of_texts]
     # reduce block size if possible
     block_size = min(block_size, max(text_lengths))
@@ -77,13 +85,22 @@ def extract(
     downsampling_rate = getattr(model.config, "downsampling_rate", 1)
     block_size = math.ceil(block_size / downsampling_rate) * downsampling_rate
 
+    # total number of forward passes
     num_chunks = sum(math.ceil(max(length - block_size, 0) / stride) + 1 for length in text_lengths)
+    
+    # preallocate a buffer for all input hashes & attention masks
     input_hashes = np.zeros((num_chunks, block_size, model.config.num_hash_functions), dtype=np.int64)
     attention_mask = np.zeros((num_chunks, block_size), dtype=np.float32)
+
+    # locs keep track of the location of every chunk with a 3-tuple (text_idx, char_start, char_end) that indexes
+    # back into the batch_of_texts
     locs = np.zeros((num_chunks, 3), dtype=np.int32)
 
+    # this is equivalent to (but faster than) np.array([ord(c) for c in "".join(batch_of_texts)]) 
     codec = "utf-32-le" if sys.byteorder == "little" else "utf-32-be"
     ordinals = np.frombuffer(bytearray("".join(batch_of_texts), encoding=codec), dtype=np.int32)
+    
+    # hash encode all ids 
     flat_hashed_ids = hash_encode(ordinals, 
                                   num_hashes=model.config.num_hash_functions, 
                                   num_buckets=model.config.num_hash_buckets)
@@ -92,6 +109,7 @@ def extract(
 
     for i in range(len(batch_of_texts)):
         for j in range(0, text_lengths[i], stride):
+            # for every chunk, assign input hashes, attention mask and loc
             start, end = j, j + block_size
             done = False
 
@@ -114,6 +132,7 @@ def extract(
     assert current_chunk == num_chunks
     n_batches = math.ceil(len(input_hashes) / batch_size)
 
+    # containers for the final logits
     all_logits = [
         np.zeros(
             (
@@ -124,6 +143,7 @@ def extract(
         )
         for length in text_lengths
     ]
+    # container for the number of chunks that any character was part of (to average chunk predictions)
     all_counts = [np.zeros(length, dtype=np.int16) for length in text_lengths]
 
     uses_lang_adapters = getattr(model.config, "language_adapter", "off") == "on"
@@ -141,6 +161,7 @@ def extract(
     else:
         language_ids = None
 
+    # forward passes through all chunks
     for batch_idx in tqdm(range(n_batches), disable=not verbose):
         start, end = batch_idx * batch_size, min(len(input_hashes), (batch_idx + 1) * batch_size)
 
@@ -166,6 +187,7 @@ def extract(
             all_logits[original_idx][start_char_idx:end_char_idx] += logits[i - start, : end_char_idx - start_char_idx]
             all_counts[original_idx][start_char_idx:end_char_idx] += 1
 
+    # so far, logits are summed, so we average them here
     all_logits = [(logits / counts[:, None]).astype(np.float16) for logits, counts in zip(all_logits, all_counts)]
 
     return all_logits