Adding the builder script for the all_annotations view

usb.py

@@ -23,7 +23,7 @@ Paper can be found here : https://arxiv.org/abs/2305.14296
 class USBConfig(datasets.BuilderConfig):
     def __init__(
         self,
-        text_features,
+        featurespec,
         label_column,
         citation=CITATION_BLOB,
         data_url="processed_data.tar.gz",
@@ -32,7 +32,7 @@ class USBConfig(datasets.BuilderConfig):
         **kwargs,
     ):
         super(USBConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
-        self.text_features = text_features
+        self.featurespec = featurespec
         self.label_column = label_column
         
         self.citation = citation
@@ -50,51 +50,57 @@ class USB(datasets.GeneratorBasedBuilder):
         USBConfig(
             name="topicbased_summarization",
             description="Generate a short summary of the given article covering the given topic",
-            text_features={"summ_idx": "int", "input_lines": "listsent", "topic_name": "sent", "output_lines":"listsent"},
+            featurespec={"summ_idx": "int", "input_lines": "listsent", "topic_name": "sent", "output_lines":"listsent"},
             label_column="output_lines",
         ),
         USBConfig(
             name="fixing_factuality",
             description="Given a summary sentence (claim) and presented evidence from the article, edit the summary to remove unsupported or contradicting facts",
-            text_features={"summ_idx": "int", "input_lines": "listsent", "initial_summary": "sent", "fixed_summary":"sent"},
+            featurespec={"summ_idx": "int", "input_lines": "listsent", "initial_summary": "sent", "fixed_summary":"sent"},
             label_column="fixed_summary",
         ),
         USBConfig(
             name="unsupported_span_prediction",
             description="Given a summary sentence (claim) and presented evidence from the article, mark the parts of the summary which are not supported by the evidence by surrounding them with [] and [/] tags.",
-            text_features={"summ_idx": "int", "input_lines": "listsent", "summary": "sent", "annotated_summary":"sent"},
+            featurespec={"summ_idx": "int", "input_lines": "listsent", "summary": "sent", "annotated_summary":"sent"},
             label_column="annotated_summary",
         ),
         USBConfig(
             name="evidence_extraction",
             description="Given an article and its summary, for each summary sentence, produce a minimal list of sentences from the article which provide sufficient evidence for all facts in the summary sentence.",
-            text_features={"input_lines": "listsent", "summary_lines": "listsent", "evidence_labels":"listlistint"},
+            featurespec={"input_lines": "listsent", "summary_lines": "listsent", "evidence_labels":"listlistint"},
             label_column="evidence_labels",
         ),
         USBConfig(
             name="multisentence_compression",
             description="Given a list of sentences from an article, generate a single sentence summary of the presented cluster of sentences.",
-            text_features={"summ_idx": "int", "input_lines": "listsent", "output_lines": "listsent"},
+            featurespec={"summ_idx": "int", "input_lines": "listsent", "output_lines": "listsent"},
             label_column="output_lines",
         ),
         USBConfig(
             name="extractive_summarization",
             description="Given an article, generate an extractive summary by producing a subset o the article's sentences",
-            text_features={"input_lines": "listsent", "labels": "listint"},
+            featurespec={"input_lines": "listsent", "labels": "listint"},
             label_column="labels",
         ),
         USBConfig(
             name="abstractive_summarization",
             description="Given an article, generate its abstractive summary",
-            text_features={"input_lines": "listsent", "output_lines": "listsent"},
+            featurespec={"input_lines": "listsent", "output_lines": "listsent"},
             label_column="output_lines",
         ),
         USBConfig(
             name="factuality_classification",
             description="Given a summary sentence (claim) and presented evidence from the article, predict whether all facts of the claim are supported by and in agreement with the presented evidence, or not.",
-            text_features={"summ_idx": "int", "input_lines": "listsent", "summary_sent": "sent", "label":"int"},
+            featurespec={"summ_idx": "int", "input_lines": "listsent", "summary_sent": "sent", "label":"int"},
             label_column="label",
         ),
+        USBConfig(
+            name="all_annotations",
+            description="All annotations collected in the creation of USB dataset in one place.",
+            featurespec={},
+            label_column=None,
+        ),
     ]
 
     def _split_generators(self, dl_manager):
@@ -136,8 +142,13 @@ class USB(datasets.GeneratorBasedBuilder):
         features = {}
         features["id"] = datasets.Value("string")
         features["domain"] = datasets.Value("string")
+
+        if self.config.name=="all_annotations":
+            # handle this as a special case
+            features["source"] = datasets.Sequence({"txt": datasets.Value("string"), "section_name": datasets.Value("string"), "section_index": datasets.Value("int32"), "is_header":datasets.Value("bool")})
+            features["summary"] = datasets.Sequence({"pre_edit": datasets.Value("string"), "post_edit": datasets.Value("string"), "evidence": datasets.Sequence(datasets.Value("int32"))})
         
-        for (text_feature,dtype) in self.config.text_features.items():
+        for (feature_name,dtype) in self.config.featurespec.items():
             hf_dtype = None
             if dtype=="int":
                 hf_dtype = datasets.Value("int32")
@@ -152,7 +163,7 @@ class USB(datasets.GeneratorBasedBuilder):
             else:
                 raise NotImplementedError
 
-            features[text_feature] = hf_dtype
+            features[feature_name] = hf_dtype
 
         return datasets.DatasetInfo(
             description=DESCRIPTION_BLOB,