Update pipeline word heuristic to work with whitespace in token offsets (#18402)

* Update pipeline word heuristic to work with whitespace in token offsets

This change checks for whitespace in the input string at either the
character preceding the token or in the first character of the token.
This works with tokenizers that return offsets excluding whitespace
between words or with offsets including whitespace.

fixes #18111

starting

* Use smaller model, ensure expected tokenization

* Re-run CI (please squash)

tests/pipelines/test_pipelines_token_classification.py

@@ -535,6 +535,20 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
             [{"entity_group": "PER", "score": 0.35, "word": "Ramazotti", "start": 0, "end": 13}],
         )
 
+    @require_torch
+    @slow
+    def test_aggregation_strategy_offsets_with_leading_space(self):
+        sentence = "We're from New York"
+        model_name = "brandon25/deberta-base-finetuned-ner"
+        ner = pipeline("ner", model=model_name, ignore_labels=[], aggregation_strategy="max")
+        self.assertEqual(
+            nested_simplify(ner(sentence)),
+            [
+                {"entity_group": "O", "score": 1.0, "word": " We're from", "start": 0, "end": 10},
+                {"entity_group": "LOC", "score": 1.0, "word": " New York", "start": 10, "end": 19},
+            ],
+        )
+
     @require_torch
     def test_gather_pre_entities(self):
         model_name = "sshleifer/tiny-dbmdz-bert-large-cased-finetuned-conll03-english"
@@ -580,6 +594,41 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
             ],
         )
 
+    @require_torch
+    def test_word_heuristic_leading_space(self):
+        model_name = "hf-internal-testing/tiny-random-deberta-v2"
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+        token_classifier = pipeline(task="ner", model=model_name, tokenizer=tokenizer, framework="pt")
+
+        sentence = "I play the theremin"
+
+        tokens = tokenizer(
+            sentence,
+            return_attention_mask=False,
+            return_tensors="pt",
+            return_special_tokens_mask=True,
+            return_offsets_mapping=True,
+        )
+        offset_mapping = tokens.pop("offset_mapping").cpu().numpy()[0]
+        special_tokens_mask = tokens.pop("special_tokens_mask").cpu().numpy()[0]
+        input_ids = tokens["input_ids"].numpy()[0]
+        scores = np.array([[1, 0] for _ in input_ids])  # values irrelevant for heuristic
+
+        pre_entities = token_classifier.gather_pre_entities(
+            sentence,
+            input_ids,
+            scores,
+            offset_mapping,
+            special_tokens_mask,
+            aggregation_strategy=AggregationStrategy.FIRST,
+        )
+
+        # ensure expected tokenization and correct is_subword values
+        self.assertEqual(
+            [(entity["word"], entity["is_subword"]) for entity in pre_entities],
+            [("▁I", False), ("▁play", False), ("▁the", False), ("▁there", False), ("min", True)],
+        )
+
     @require_tf
     def test_tf_only(self):
         model_name = "hf-internal-testing/tiny-random-bert-tf-only"  # This model only has a TensorFlow version