set msa as list; label as tensor

fold_prediction-rag.py → fold_prediction_rag.py

@@ -149,11 +149,12 @@ _CITATION = """
 
 # You can copy an official description
 _DESCRIPTION = """
+Fold class prediction is a scientific classification task that assigns protein sequences to one of 1,195 known folds. The primary application of this task lies in the identification of novel remote homologs among proteins of interest, such as emerging antibiotic-resistant genes and industrial enzymes. The study of protein fold holds great significance in fields like proteomics and structural biology, as it facilitates the analysis of folding patterns, leading to the discovery of remote homologies and advancements in disease research. 
 """
 
-_HOMEPAGE = "xxxxx"
+_HOMEPAGE = "https://huggingface.co/datasets/genbio-ai/fold_prediction_rag"
 
-_LICENSE = "xxxxx"
+_LICENSE = "Apache license 2.0"
 
 class DownStreamConfig(datasets.BuilderConfig):
     """BuilderConfig for downstream taks dataset."""
@@ -176,7 +177,7 @@ class DownStreamTasks(datasets.GeneratorBasedBuilder):
             {
                 "seq":         datasets.Value("string"),
                 "label":       datasets.Value("int32"),
-                "msa":         datasets.Value("string"),
+                "msa":         datasets.Sequence(datasets.Value("string")),
                 "str_emb":     datasets.Array2D(shape=(None, 384), dtype='float32'),
             }
         )
@@ -264,3 +265,16 @@ class DownStreamTasks(datasets.GeneratorBasedBuilder):
                 "msa":     "|".join(msa),
                 "str_emb": str_emb
             }
+
+    def _as_dataset(
+        self,
+        split: Optional[datasets.Split] = None,
+        **kwargs
+    ) -> datasets.Dataset:
+        dataset = super()._as_dataset(split=split, **kwargs)
+        dataset.set_format(
+            type="numpy",
+            columns=["str_emb"],
+            output_all_columns=True
+        )
+        return dataset