fix text script

cas.py

@@ -168,95 +168,122 @@ class CAS(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, datadir):
         key = 0
-        for file in ["CAS_neg.txt", "CAS_spec.txt"]:
-            filepath = os.path.join(datadir, file)
-            label = "negation" if "neg" in file else "speculation"
-            id_docs = []
-            id_words = []
-            words = []
-            lemmas = []
-            POS_tags = []
+        # for file in ["CAS_neg.txt", "CAS_spec.txt"]:
+        file = 'CAS_neg.txt'
+        filepath = os.path.join(datadir, file)
+        filepath2 = os.path.join(datadir, 'CAS_spec.txt')
+        label = "negation" if "neg" in file else "speculation"
+        id_docs = []
+        id_docs_2 = []
+        id_words = []
+        words = []
+        lemmas = []
+        POS_tags = []
+        NER_tags = []
+        NER_tags_2 = []
 
-            with open(filepath) as f:
-                for line in f.readlines():
-                    line_content = line.split("\t")
-                    if len(line_content) > 1:
-                        id_docs.append(line_content[0])
-                        id_words.append(line_content[1])
-                        words.append(line_content[2])
-                        lemmas.append(line_content[3])
-                        POS_tags.append(line_content[4])
+        with open(filepath) as f:
+            for line in f.readlines():
+                line_content = line.split("\t")
+                if len(line_content) > 1:
+                    id_docs.append(line_content[0])
+                    id_words.append(line_content[1])
+                    words.append(line_content[2])
+                    lemmas.append(line_content[3])
+                    POS_tags.append(line_content[4])
+                    NER_tags.append(line_content[5].strip())
 
-            dic = {
-                "id_docs": np.array(list(map(int, id_docs))),
-                "id_words": id_words,
-                "words": words,
-                "lemmas": lemmas,
-                "POS_tags": POS_tags,
-            }
-            if self.config.schema == "source":
-                for doc_id in set(dic["id_docs"]):
-                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
-                    text = [dic["words"][id] for id in idces]
-                    text_lemmas = [dic["lemmas"][id] for id in idces]
-                    POS_tags_ = [dic["POS_tags"][id] for id in idces]
-                    yield key, {
-                        "id": key,
-                        "document_id": doc_id,
-                        "text": text,
-                        "lemmas": text_lemmas,
-                        "POS_tags": POS_tags_,
-                        "labels": [label],
-                    }
-                    key += 1
-            elif self.config.schema == "bigbio_text":
-                for doc_id in set(dic["id_docs"]):
-                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
-                    text = " ".join([dic["words"][id] for id in idces])
-                    yield key, {
-                        "id": key,
-                        "document_id": doc_id,
-                        "text": text,
-                        "labels": [label],
+        with open(filepath2) as f:
+            for line in f.readlines():
+                line_content = line.split("\t")
+                if len(line_content) > 1:
+                    id_docs_2.append(line_content[0])
+                    NER_tags_2.append(line_content[5].strip())
+
+        dic = {
+            "id_docs": np.array(list(map(int, id_docs))),
+            "id_words": id_words,
+            "words": words,
+            "lemmas": lemmas,
+            "POS_tags": POS_tags,
+            "NER_tags": NER_tags
+        }
+        dic2 = {
+            "id_docs": np.array(list(map(int, id_docs_2))),
+            "NER_tags": NER_tags_2
+        }
+        if self.config.schema == "source":
+            for doc_id in set(dic["id_docs"]):
+                idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                text = [dic["words"][id] for id in idces]
+                text_lemmas = [dic["lemmas"][id] for id in idces]
+                POS_tags_ = [dic["POS_tags"][id] for id in idces]
+                yield key, {
+                    "id": key,
+                    "document_id": doc_id,
+                    "text": text,
+                    "lemmas": text_lemmas,
+                    "POS_tags": POS_tags_,
+                    "labels": [label],
+                }
+                key += 1
+        elif self.config.schema == "bigbio_text":
+            for doc_id in set(dic["id_docs"]):
+                idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                idces_2 = np.argwhere(dic2["id_docs"] == doc_id)[:, 0]
+                
+                text = " ".join([dic["words"][id] for id in idces])
+                label_tokens = [dic["NER_tags"][id] for id in idces]
+                label2_tokens = [dic2["NER_tags"][id] for id in idces_2]
+                label_ = []
+                if not all(l == '***' for l in label_tokens):
+                    label_.append("negation")
+                if not all(l == '***' for l in label2_tokens):
+                    label_.append("speculation")
+                yield key, {
+                    "id": key,
+                    "document_id": doc_id,
+                    "text": text,
+                    "labels": label_,
+                }
+                key += 1
+        elif self.config.schema == "bigbio_kb":
+            for doc_id in set(dic["id_docs"]):
+                idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                text = [dic["words"][id] for id in idces]
+                POS_tags_ = [dic["POS_tags"][id] for id in idces]
+
+                data = {
+                    "id": str(key),
+                    "document_id": doc_id,
+                    "passages": [],
+                    "entities": [],
+                    "relations": [],
+                    "events": [],
+                    "coreferences": [],
+                }
+                key += 1
+
+                data["passages"] = [
+                    {
+                        "id": str(key + i),
+                        "type": "sentence",
+                        "text": [text[i]],
+                        "offsets": [[i, i + 1]],
                     }
-                    key += 1
-            elif self.config.schema == "bigbio_kb":
-                for doc_id in set(dic["id_docs"]):
-                    idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
-                    text = [dic["words"][id] for id in idces]
-                    POS_tags_ = [dic["POS_tags"][id] for id in idces]
+                    for i in range(len(text))
+                ]
+                key += len(text)
 
-                    data = {
-                        "id": str(key),
-                        "document_id": doc_id,
-                        "passages": [],
-                        "entities": [],
-                        "relations": [],
-                        "events": [],
-                        "coreferences": [],
+                for i in range(len(text)):
+                    entity = {
+                        "id": key,
+                        "type": "POS_tag",
+                        "text": [POS_tags_[i]],
+                        "offsets": [[i, i + 1]],
+                        "normalized": [],
                     }
+                    data["entities"].append(entity)
                     key += 1
 
-                    data["passages"] = [
-                        {
-                            "id": str(key + i),
-                            "type": "sentence",
-                            "text": [text[i]],
-                            "offsets": [[i, i + 1]],
-                        }
-                        for i in range(len(text))
-                    ]
-                    key += len(text)
-
-                    for i in range(len(text)):
-                        entity = {
-                            "id": key,
-                            "type": "POS_tag",
-                            "text": [POS_tags_[i]],
-                            "offsets": [[i, i + 1]],
-                            "normalized": [],
-                        }
-                        data["entities"].append(entity)
-                        key += 1
-
                     yield key, data