Raise error if `stride` is too high in `TokenClassificationPipeline` (#22942)

* Raise error if `stride` is too high

* Clarify use of `stride`

src/transformers/pipelines/token_classification.py

@@ -69,7 +69,8 @@ class AggregationStrategy(ExplicitEnum):
         stride (`int`, *optional*):
             If stride is provided, the pipeline is applied on all the text. The text is split into chunks of size
             model_max_length. Works only with fast tokenizers and `aggregation_strategy` different from `NONE`. The
-            value of this argument defines the number of overlapping tokens between chunks.
+            value of this argument defines the number of overlapping tokens between chunks. In other words, the model
+            will shift forward by `tokenizer.model_max_length - stride` tokens each step.
         aggregation_strategy (`str`, *optional*, defaults to `"none"`):
             The strategy to fuse (or not) tokens based on the model prediction.
 
@@ -191,6 +192,10 @@ class TokenClassificationPipeline(ChunkPipeline):
         if ignore_labels is not None:
             postprocess_params["ignore_labels"] = ignore_labels
         if stride is not None:
+            if stride >= self.tokenizer.model_max_length:
+                raise ValueError(
+                    "`stride` must be less than `tokenizer.model_max_length` (or even lower if the tokenizer adds special tokens)"
+                )
             if aggregation_strategy == AggregationStrategy.NONE:
                 raise ValueError(
                     "`stride` was provided to process all the text but `aggregation_strategy="