add summarization

QR-AN.py

@@ -92,6 +92,11 @@ class QRANDataset(datasets.GeneratorBasedBuilder):
             name="qran_full",
             version=datasets.Version("1.0.0"),
             description="QRAN Dataset: A classification task of French Parliament questions-answers",
+        ),
+        QRANConfig(
+            name="qran_generation",
+            version=datasets.Version("1.0.0"),
+            description="QRAN Dataset: A generation task of French Parliament questions-answers",
         )
     ]
 
@@ -103,16 +108,14 @@ class QRANDataset(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "text": datasets.Value("string"),
-                    #"question": datasets.Value("string"),
-                    #"answer": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
                     #"label_name": datasets.Value("string"),
                     "label": datasets.features.ClassLabel(names=_LABELS),
                 }
             ),
             supervised_keys=None,
             citation=_CITATION,
-            task_templates=[TextClassification(
-                text_column="text", label_column="label")],
         )
 
     def _split_generators(self, dl_manager):
@@ -140,6 +143,9 @@ class QRANDataset(datasets.GeneratorBasedBuilder):
                 answer, question = data["answer"], data["question"]
                 label = self._LABELS_DICT[data["label_name"]]
 
+                if self.config.name == "qran_generation":
+                    yield id_, {"question": question, "answer": answer}
+
                 if self.config.name == "qran_answer":
                     text = answer
                 elif self.config.name == "qran_question":

README.md

@@ -1,18 +1,27 @@
 ---
 languages: fr
-task_categories: text-classification
 task_ids:
 - multi-class-classification
 - topic-classification
+- summarization
+task_categories:
+- text-classification
+- conditional-text-generation
 size_categories: 10K<n<100K
 ---
 
-**QR-AN Dataset: a classification dataset of french Parliament questions-answers.** 
+**QR-AN Dataset: a classification and generation dataset of french Parliament questions-answers.** 
 
 This is a dataset for theme/topic classification, made of questions and answers from https://www2.assemblee-nationale.fr/recherche/resultats_questions . \
 
 It contains 188 unbalanced classes, 80k questions-answers divided into 3 splits: train (60k), val (10k) and test (10k). \
 
+Can be used for generation with 'qran-generation'
+This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
+```python
+"ccdv/cass-summarization": ("question", "answer")
+```
+
 Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script:
 ```
 export MODEL_NAME=camembert-base