From 0c41765df4b1d79debdbe0293bc30cf65d0714b8 Mon Sep 17 00:00:00 2001
From: Shauray Singh <39147312+shauray8@users.noreply.github.com>
Date: Thu, 20 Jul 2023 19:39:40 +0530
Subject: [PATCH] [DOCS] Example for `LogitsProcessor` class (#24848)

* make docs

* fixup

* resolved

* remove debugs

* Revert "fixup"

This reverts commit 5e0f636aae0bf8707bc8bdaa6a9427fbf66834ed.

* prev (ignore)

* fixup broke some files

* remove files

* reverting modeling_reformer

* lang fix
---
 src/transformers/generation/logits_process.py | 28 ++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index 8a3c8ec7aa1..195dfcdc592 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -193,12 +193,38 @@ class TemperatureLogitsWarper(LogitsWarper):
 
 class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
     r"""
-    [`LogitsProcessor`] enforcing an exponential penalty on repeated sequences.
+    [`LogitsProcessor`] that prevents the repetition of previous tokens through an exponential penalty. This technique
+    shares some similarities with coverage mechanisms and other aimed at reducing repetition. During the text
+    generation process, the probability distribution for the next token is determined using a formula that incorporates
+    token scores based on their occurrence in the generated sequence. Tokens with higher scores are less likely to be
+    selected. The formula can be seen in the original [paper](https://arxiv.org/pdf/1909.05858.pdf). According to the
+    paper a penalty of around 1.2 yields a good balance between truthful generation and lack of repetition.
 
     Args:
         repetition_penalty (`float`):
             The parameter for repetition penalty. 1.0 means no penalty. See [this
             paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+
+    Examples:
+
+    ```py
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    >>> # Initializing the model and tokenizer for it
+    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    >>> inputs = tokenizer(["I'm not going to"], return_tensors="pt")
+
+    >>> # This shows a normal generate without any specific parameters
+    >>> summary_ids = model.generate(inputs["input_ids"], max_length=20)
+    >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0])
+    I'm not going to lie, I'm not going to lie. I'm not going to lie
+
+    >>> # This generates a penalty for repeated tokens
+    >>> penalized_ids = model.generate(inputs["input_ids"], max_length=20, repetition_penalty=1.2)
+    >>> print(tokenizer.batch_decode(biased_ids, skip_special_tokens=True)[0])
+    I'm not going to lie, I was really excited about this. It's a great game
+    ```
     """
 
     def __init__(self, penalty: float):