return targets

dataquality/__init__.py

@@ -31,7 +31,7 @@
 """
 
 
-__version__ = "1.4.0"
+__version__ = "1.4.1"
 
 import sys
 from typing import Any, List, Optional

dataquality/loggers/data_logger/seq2seq/formatters.py

@@ -43,7 +43,7 @@ def format_text(
         tokenizer: PreTrainedTokenizerFast,
         max_tokens: Optional[int],
         split_key: str,
-    ) -> Tuple[AlignedTokenData, List[List[str]], Optional[List[str]]]:
+    ) -> Tuple[AlignedTokenData, List[List[str]], List[str]]:
         """Tokenize and align the `text` samples
 
         `format_text` tokenizes and computes token alignments for
@@ -83,10 +83,10 @@ def format_text(
             Aligned token data for *just* target tokens, based on `text`
         token_label_str: List[List[str]]
             The target tokens (as strings) - see `Seq2SeqDataLogger.token_label_str`
-        label_strs: Optional[List[str]]
+        targets: List[str]
             The decoded response tokens - i.e. the string representation of the
             Targets for each sample. Note that this is only computed for
-            Decoder-Only models.
+            Decoder-Only models. Returns [] for Encoder-Decoder
         """
         pass
 
@@ -221,7 +221,7 @@ def format_text(
         tokenizer: PreTrainedTokenizerFast,
         max_tokens: Optional[int],
         split_key: str,
-    ) -> Tuple[AlignedTokenData, List[List[str]], Optional[List[str]]]:
+    ) -> Tuple[AlignedTokenData, List[List[str]], List[str]]:
         """Further validation for Encoder-Decoder
 
         For Encoder-Decoder we need to:
@@ -266,7 +266,7 @@ def format_text(
         id_to_tokens = dict(zip(ids, token_label_ids))
         self.logger_config.id_to_tokens[split_key].update(id_to_tokens)
 
-        return batch_aligned_data, token_label_str, None
+        return batch_aligned_data, token_label_str, []
 
     @torch.no_grad()
     def generate_sample(
@@ -430,7 +430,7 @@ def format_text(
         # Empty initialization
         batch_aligned_data = AlignedTokenData([], [])
         token_label_str = []
-        str_labels = []
+        targets = []
 
         # Decode then re-tokenize just the response labels to get correct offsets
         for token_label_ids in tqdm(
@@ -456,7 +456,7 @@ def format_text(
                 max_input_tokens,
             )
             batch_aligned_data.append(response_aligned_data)
-            str_labels.append(response_str)
+            targets.append(response_str)
 
         # Save the tokenized response labels for each samples
         id_to_tokens = dict(zip(ids, tokenized_labels))
@@ -471,7 +471,7 @@ def format_text(
             id_to_formatted_prompt_length
         )
 
-        return batch_aligned_data, token_label_str, str_labels
+        return batch_aligned_data, token_label_str, targets
 
     @torch.no_grad()
     def generate_sample(

dataquality/loggers/data_logger/seq2seq/seq2seq_base.py

@@ -118,14 +118,14 @@ def validate_and_format(self) -> None:
         label_len = len(self.labels)
         text_len = len(self.texts)
         id_len = len(self.ids)
-        if label_len > 0:
+        if label_len > 0:  # Encoder-Decoder case
             assert id_len == text_len == label_len, (
                 "IDs, texts, and labels must be the same length, got "
                 f"({id_len} ids, {text_len} texts, {label_len} labels)"
             )
-        else:
+        else:  # Decoder-Only case
             assert id_len == text_len, (
-                "IDs and textsmust be the same length, got "
+                "IDs and texts must be the same length, got "
                 f"({id_len} ids, {text_len} texts)"
             )
         assert self.logger_config.tokenizer, (
@@ -150,7 +150,7 @@ def validate_and_format(self) -> None:
         (
             batch_aligned_token_data,
             token_label_str,
-            labels,
+            targets,
         ) = self.formatter.format_text(
             text=texts,
             ids=self.ids,
@@ -161,8 +161,8 @@ def validate_and_format(self) -> None:
         self.token_label_offsets = batch_aligned_token_data.token_label_offsets
         self.token_label_positions = batch_aligned_token_data.token_label_positions
         self.token_label_str = token_label_str
-        if labels is not None:
-            self.labels = labels
+        if len(targets) > 0:  # For Decoder-Only we update the 'targets' here
+            self.labels = targets
 
     def _get_input_df(self) -> DataFrame:
         df_dict = {