diff --git a/docs/source/llm_finetuning.mdx b/docs/source/llm_finetuning.mdx
index 902c024636..9576384d4c 100644
--- a/docs/source/llm_finetuning.mdx
+++ b/docs/source/llm_finetuning.mdx
@@ -33,7 +33,7 @@ For Reward Trainer, the data should be in the following format:
 
 | text                                                          | rejected_text                                                     |
 |---------------------------------------------------------------|-------------------------------------------------------------------|
-| human: hello \n bot: hi nice to meet you                      | humna: hello \n bot: leave me alone                               |
+| human: hello \n bot: hi nice to meet you                      | human: hello \n bot: leave me alone                               |
 | human: how are you \n bot: I am fine                          | human: how are you \n bot: I am not fine                          |
 | human: What is your name? \n bot: My name is Mary             | human: What is your name? \n bot: Whats it to you?                |
 | human: Which is the best programming language? \n bot: Python | human: Which is the best programming language? \n bot: Javascript |
diff --git a/docs/source/seq2seq.mdx b/docs/source/seq2seq.mdx
index 3459cd041e..7ab2c2279a 100644
--- a/docs/source/seq2seq.mdx
+++ b/docs/source/seq2seq.mdx
@@ -1,6 +1,6 @@
 # Seq2Seq
 
-Seq2Seq is a task that involves converting a sequence of words into another sequence of words. 
+Seq2Seq is a task that involves converting a sequence of words into another sequence of words.
 It is used in machine translation, text summarization, and question answering.
 
 ## Data Format
diff --git a/src/autotrain/trainers/tabular/utils.py b/src/autotrain/trainers/tabular/utils.py
index c3629ff693..788a268535 100644
--- a/src/autotrain/trainers/tabular/utils.py
+++ b/src/autotrain/trainers/tabular/utils.py
@@ -159,7 +159,7 @@ def calculate(self, y_true, y_pred):
             else:
                 if metric_name == "rmsle":
                     temp_pred = copy.deepcopy(y_pred)
-                    temp_pred = np.clip(y_pred, 0, None)
+                    temp_pred = np.clip(temp_pred, 0, None)
                     metrics[metric_name] = metric_func(y_true, temp_pred)
                 else:
                     metrics[metric_name] = metric_func(y_true, y_pred)