Fix and improve documentation for LEDForConditionalGeneration (#12303)

* Replace conditional generation example (fixes #12268)

* Replace model in summarization example with finetuned checkpoint, adapt example text

* Fix typo in new summarization example

* Fix docstring formatting, add missing import statement to example

src/transformers/models/led/modeling_led.py

@@ -1436,17 +1436,43 @@ LED_START_DOCSTRING = r"""
 LED_GENERATION_EXAMPLE = r"""
     Summarization example::
 
-        >>> from transformers import LEDTokenizer, LEDForConditionalGeneration, LEDConfig
+        >>> import torch
+        >>> from transformers import LEDTokenizer, LEDForConditionalGeneration
 
-        >>> model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')
-        >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384')
+        >>> model = LEDForConditionalGeneration.from_pretrained('allenai/led-large-16384-arxiv')
+        >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-large-16384-arxiv')
 
-        >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
-        >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
+        >>> ARTICLE_TO_SUMMARIZE = '''Transformers (Vaswani et al., 2017) have achieved state-of-the-art
+        ... results in a wide range of natural language tasks including generative
+        ... language modeling (Dai et al., 2019; Radford et al., 2019) and discriminative
+        ... language understanding (Devlin et al., 2019). This success is partly due to
+        ... the self-attention component which enables the network to capture contextual
+        ... information from the entire sequence. While powerful, the memory and computational
+        ... requirements of self-attention grow quadratically with sequence length, making
+        ... it infeasible (or very expensive) to process long sequences.
+        ...
+        ... To address this limitation, we present Longformer, a modified Transformer
+        ... architecture with a self-attention operation that scales linearly with the
+        ... sequence length, making it versatile for processing long documents (Fig 1). This
+        ... is an advantage for natural language tasks such as long document classification,
+        ... question answering (QA), and coreference resolution, where existing approaches
+        ... partition or shorten the long context into smaller sequences that fall within the
+        ... typical 512 token limit of BERT-style pretrained models. Such partitioning could
+        ... potentially result in loss of important cross-partition information, and to
+        ... mitigate this problem, existing methods often rely on complex architectures to
+        ... address such interactions. On the other hand, our proposed Longformer is able to
+        ... build contextual representations of the entire context using multiple layers of
+        ... attention, reducing the need for task-specific architectures.'''
+        >>> inputs = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
+
+        >>> # Global attention on the first token (cf. Beltagy et al. 2020)
+        >>> global_attention_mask = torch.zeros_like(inputs)
+        >>> global_attention_mask[:, 0] = 1
 
         >>> # Generate Summary
-        >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
-        >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
+        >>> summary_ids = model.generate(inputs, global_attention_mask=global_attention_mask,
+        ...                              num_beams=3, max_length=32, early_stopping=True)
+        >>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
 """
 
 LED_INPUTS_DOCSTRING = r"""
@@ -2305,13 +2331,9 @@ class LEDForConditionalGeneration(LEDPreTrainedModel):
 
             >>> model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')
             >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
-            >>> logits = model(input_ids).logits
 
-            >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
-            >>> probs = logits[0, masked_index].softmax(dim=0)
-            >>> values, predictions = probs.topk(5)
-
-            >>> tokenizer.decode(predictions).split()
+            >>> prediction = model.generate(input_ids)[0]
+            >>> print(tokenizer.decode(prediction, skip_special_tokens=True))
         """
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict