remove pie-utils from dependencies and update pytorch-ie

requirements.txt

@@ -1,10 +1,6 @@
 # --------- pytorch-ie --------- #
-pytorch-ie>=0.28.0,<0.29.0
+pytorch-ie>=0.28.0,<0.30.0
 pie-datasets>=0.3.1,<0.4.0
-# pie-utils provides some useful helper methods for pytorch-ie,
-# e.g. document processors or span utils (convert span annotations
-# to sequence encodings such as BIO, IO or BIOUL, and back).
-git+https://github.com/ArneBinder/[email protected]
 
 # --------- hydra --------- #
 hydra-core>=1.3.0

src/pipeline/ner_re_pipeline.py

@@ -4,7 +4,6 @@
 from functools import partial
 from typing import Callable, Dict, List, Optional, Sequence, TypeVar
 
-from pie_utils.document.processors import CandidateRelationAdder
 from pytorch_ie import AutoPipeline
 from pytorch_ie.core import Document
 
@@ -170,12 +169,12 @@ def __call__(self, documents: Sequence[Document], inplace: bool = False):
                     layer_names=["entities"],
                     **self.processor_kwargs.get("use_predicted_entities", {}),
                 ),
-                "create_candidate_relations": partial(
-                    process_documents,
-                    processor=CandidateRelationAdder(
-                        **self.processor_kwargs.get("create_candidate_relations", {})
-                    ),
-                ),
+                # "create_candidate_relations": partial(
+                #    process_documents,
+                #    processor=CandidateRelationAdder(
+                #        **self.processor_kwargs.get("create_candidate_relations", {})
+                #    ),
+                # ),
                 "re_pipeline": AutoPipeline.from_pretrained(
                     self.re_model_path, **self.processor_kwargs.get("re_pipeline", {})
                 ),