diff --git a/docs/source/en/internal/generation_utils.md b/docs/source/en/internal/generation_utils.md
index 946940cb0194..eb25ddb63297 100644
--- a/docs/source/en/internal/generation_utils.md
+++ b/docs/source/en/internal/generation_utils.md
@@ -428,13 +428,11 @@ A [`Constraint`] can be used to force the generation to include specific tokens
     - __call__
 
 [[autodoc]] BayesianDetectorConfig
-    - __call__
 
 [[autodoc]] BayesianDetectorModel
-    - __call__
+    - forward
 
 [[autodoc]] SynthIDTextWatermarkingConfig
-    - __call__
 
 [[autodoc]] SynthIDTextWatermarkDetector
     - __call__
diff --git a/src/transformers/generation/configuration_utils.py b/src/transformers/generation/configuration_utils.py
index c460a19885af..3c204481b042 100644
--- a/src/transformers/generation/configuration_utils.py
+++ b/src/transformers/generation/configuration_utils.py
@@ -1471,8 +1471,8 @@ class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
     ```python
     >>> from transformers import AutoModelForCausalLM, AutoTokenizer, SynthIDTextWatermarkingConfig
 
-    >>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b-it')
-    >>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b-it')
+    >>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
+    >>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')
 
     >>> # SynthID Text configuration
     >>> watermarking_config = SynthIDTextWatermarkingConfig(
@@ -1481,11 +1481,11 @@ class SynthIDTextWatermarkingConfig(BaseWatermarkingConfig):
     ... )
 
     >>> # Generation with watermarking
-    >>> tokenized_prompts = tokenizer(["your prompts here"])
+    >>> tokenized_prompts = tokenizer(["Once upon a time, "], return_tensors="pt", padding=True)
     >>> output_sequences = model.generate(
-    ...     **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True,
+    ...     **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True, max_new_tokens=10
     ... )
-    >>> watermarked_text = tokenizer.batch_decode(output_sequences)
+    >>> watermarked_text = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
     ```
     """
 
diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index fde95c7a8565..9d244191da81 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -2565,8 +2565,8 @@ class SynthIDTextWatermarkLogitsProcessor(LogitsProcessor):
     ```python
     >>> from transformers import AutoModelForCausalLM, AutoTokenizer, SynthIDTextWatermarkingConfig
 
-    >>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b-it')
-    >>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b-it')
+    >>> tokenizer = AutoTokenizer.from_pretrained('google/gemma-2-2b', padding_side="left")
+    >>> model = AutoModelForCausalLM.from_pretrained('google/gemma-2-2b')
 
     >>> # SynthID Text configuration
     >>> watermarking_config = SynthIDTextWatermarkingConfig(
@@ -2575,11 +2575,11 @@ class SynthIDTextWatermarkLogitsProcessor(LogitsProcessor):
     ... )
 
     >>> # Generation with watermarking
-    >>> tokenized_prompts = tokenizer(["your prompts here"])
+    >>> tokenized_prompts = tokenizer(["Once upon a time, "], return_tensors="pt", padding=True)
     >>> output_sequences = model.generate(
-    ...     **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True,
+    ...     **tokenized_prompts, watermarking_config=watermarking_config, do_sample=True, max_new_tokens=10
     ... )
-    >>> watermarked_text = tokenizer.batch_decode(output_sequences)
+    >>> watermarked_text = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
     ```
     """