Generate: fix `ExponentialDecayLengthPenalty` doctest (#27485)

fix exponential doctest

src/transformers/generation/logits_process.py

@@ -1327,22 +1327,26 @@ class ExponentialDecayLengthPenalty(LogitsProcessor):
     ```python
     >>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
 
-    >>> set_seed(1)
     >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
     >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
 
     >>> text = "Just wanted to let you know, I"
     >>> inputs = tokenizer(text, return_tensors="pt")
 
-    >>> # Generate sequences without exponential penalty. We want short sentences, so we limit max_length=30
-    >>> # see that the answer tends to end abruptly
+    >>> # Let's consider that we want short sentences, so we limit `max_length=30`. However, we observe that the answer
+    >>> # tends to end abruptly.
+    >>> set_seed(1)
     >>> outputs = model.generate(**inputs, do_sample=True, temperature=0.9, max_length=30, pad_token_id=50256)
     >>> print(tokenizer.batch_decode(outputs)[0])
-    Just wanted to let you know, I'm not even a lawyer. I'm a man. I have no real knowledge of politics. I'm a
-
-    >>> # Generate sequences with exponential penalty, we add the exponential_decay_length_penalty=(start_index, decay_factor)
-    >>> # We see that instead of cutting at max_tokens, the output comes to an end before (at 25 tokens) and with more meaning
-    >>> # What happens is that starting from `start_index` the EOS token score will be increased by decay_factor exponentially
+    Just wanted to let you know, I received a link to an ebook, the book How To Start A Social Network which was
+    published in 2010. Although
+
+    >>> # To promote the appearance of the EOS token at the right time, we add the `exponential_decay_length_penalty =
+    >>> # (start_index, decay_factor)`. Instead of cutting at max_tokens, the output comes to an end before and usually
+    >>> # with more meaning. What happens is that starting from `start_index` the EOS token score will be increased
+    >>> # by `decay_factor` exponentially. However, if you set a high decay factor, you may also end up with abruptly
+    >>> # ending sequences.
+    >>> set_seed(1)
     >>> outputs = model.generate(
     ...     **inputs,
     ...     do_sample=True,
@@ -1352,19 +1356,22 @@ class ExponentialDecayLengthPenalty(LogitsProcessor):
     ...     exponential_decay_length_penalty=(15, 1.6),
     ... )
     >>> print(tokenizer.batch_decode(outputs)[0])
-    Just wanted to let you know, I've got a very cool t-shirt educating people on how to use the Internet<|endoftext|>
+    Just wanted to let you know, I received a link to an ebook, the book How To Start A Social Network
+    which<|endoftext|>
 
-    >>> # Generate sequences with smaller decay_factor, still improving the hard cutoff mid-sentence
+    >>> # With a small decay factor, you will have a higher chance of getting a meaningful sequence.
+    >>> set_seed(1)
     >>> outputs = model.generate(
     ...     **inputs,
     ...     do_sample=True,
     ...     temperature=0.9,
     ...     max_length=30,
     ...     pad_token_id=50256,
-    ...     exponential_decay_length_penalty=(15, 1.05),
+    ...     exponential_decay_length_penalty=(15, 1.01),
     ... )
     >>> print(tokenizer.batch_decode(outputs)[0])
-    Just wanted to let you know, I've been working on it for about 6 months and now it's in Alpha.<|endoftext|>
+    Just wanted to let you know, I received a link to an ebook, the book How To Start A Social Network which was
+    published in 2010.<|endoftext|>
     ```
     """