Add the BIO format for the named entities.

MASSIVE.py

@@ -285,6 +285,10 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                     "intent": datasets.features.ClassLabel(names=_INTENTS),
                     "utt": datasets.Value("string"),
                     "annot_utt": datasets.Value("string"),
+                    "annot_utt_bio": datasets.Sequence({
+                        "word": datasets.Value("string"),
+                        "tag": datasets.Value("string"),
+                    }),
                     "worker_id": datasets.Value("string"),
                     "slot_method": datasets.Sequence({
                         "slot": datasets.Value("string"),
@@ -337,6 +341,55 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
             ),
         ]
 
+    def _getBioFormat(self, text):
+
+        tags, words = [], []
+
+        bio_mode = False
+        cpt_bio = 0
+        current_tag = None
+        
+        split_iter = iter(text.split(" "))
+
+        for s in split_iter:
+
+            if s.startswith("["):
+                current_tag = s.strip("[")
+                bio_mode = True
+                cpt_bio += 1
+                next(split_iter)
+                continue
+
+            elif s.endswith("]"):
+                bio_mode = False
+                if cpt_bio == 1:
+                    prefix = "B-"
+                else:
+                    prefix = "I-"
+                token = prefix + current_tag
+                word = s.strip("]")
+                current_tag = None
+                cpt_bio = 0
+
+            else:
+
+                if bio_mode == True:
+                    if cpt_bio == 1:
+                        prefix = "B-"
+                    else:
+                        prefix = "I-"
+                    token = prefix + current_tag
+                    word = s
+                    cpt_bio += 1
+                else:
+                    token = "O"
+                    word = s
+            
+            tags.append(token)
+            words.append(word)
+
+        return [{"word": w, "tag": t} for w, t in zip(words, tags)]
+
     def _generate_examples(self, filepath, split, lang):
 
         filepath = filepath + "/1.0/data/" + lang + ".jsonl"
@@ -391,6 +444,7 @@ class MASSIVE(datasets.GeneratorBasedBuilder):
                 "intent": data["intent"],
                 "utt": data["utt"],
                 "annot_utt": data["annot_utt"],
+                "annot_utt_bio": self._getBioFormat(data["annot_utt"]),
                 "worker_id": data["worker_id"],
                 "slot_method": slot_method,
                 "judgments": judgments,

README.md

@@ -258,44 +258,39 @@ print(dataset[0])
 
 ```json
 {
-  "id": "0",
+  "id": "1",
   "locale": "fr-FR",
-  "partition": "test",
-  "scenario": "alarm",
-  "intent": "alarm_set",
-  "utt": "réveille-moi à cinq heures du matin cette semaine",
-  "annot_utt": "réveille-moi à [time : cinq heures du matin] [date : cette semaine]",
+  "partition": "train",
+  "scenario": 16,
+  "intent": 48,
+  "utt": "réveille-moi à neuf heures du matin le vendredi",
+  "annot_utt": "réveille-moi à [time : neuf heures du matin] le [date : vendredi]",
+  "annot_utt_bio": {
+    "word": [
+      "réveille-moi",
+      "à",
+      "neuf",
+      "heures",
+      "du",
+      "matin",
+      "le",
+      "vendredi"
+    ],
+    "tag": ["O", "O", "B-time", "I-time", "I-time", "I-time", "O", "B-date"]
+  },
   "worker_id": "22",
-  "slot_method": [
-    { "slot": "time", "method": "translation" },
-    { "slot": "date", "method": "translation" }
-  ],
-  "judgments": [
-    {
-      "worker_id": "22",
-      "intent_score": 1,
-      "slots_score": 1,
-      "grammar_score": 4,
-      "spelling_score": 2,
-      "language_identification": "target"
-    },
-    {
-      "worker_id": "8",
-      "intent_score": 1,
-      "slots_score": 1,
-      "grammar_score": 4,
-      "spelling_score": 2,
-      "language_identification": "target"
-    },
-    {
-      "worker_id": "0",
-      "intent_score": 1,
-      "slots_score": 1,
-      "grammar_score": 4,
-      "spelling_score": 2,
-      "language_identification": "target"
-    }
-  ]
+  "slot_method": {
+    "slot": ["time", "date"],
+    "method": ["translation", "translation"]
+  },
+  "judgments": {
+    "worker_id": ["11", "22", "0"],
+    "intent_score": [2, 1, 1],
+    "slots_score": [1, 1, 1],
+    "grammar_score": [3, 4, 4],
+    "spelling_score": [2, 2, 2],
+    "language_identification": ["target", "target", "target"]
+  }
 }
 ```
 

test_MASSIVE.py

@@ -1,14 +1,19 @@
-from datasets import load_dataset
+from datasets import load_dataset, set_caching_enabled
 
-# source = "MASSIVE.py"
-source = "qanastek/MASSIVE"
+set_caching_enabled(False)
 
+source = "MASSIVE.py"
+# source = "qanastek/MASSIVE"
+
+dataset = load_dataset(source, "fr-FR")
 # dataset = load_dataset(source, "fr-FR", download_mode="force_redownload")
 # print(dataset)
 
-dataset = load_dataset(source, "en-US", download_mode="force_redownload")
+# dataset = load_dataset(source, "en-US")
+# dataset = load_dataset(source, "en-US", download_mode="force_redownload")
 # , split="train"
 print(dataset)
 
 # print(dataset[0])
-print(dataset["train"][0])
+f = dataset["train"][0]
+print(f)