Add coordinate_structure_constraint_object_extraction data files

README.md

@@ -268,10 +268,10 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 173050
+    num_bytes: 171655
     num_examples: 1000
-  download_size: 366045
-  dataset_size: 173050
+  download_size: 51584
+  dataset_size: 171655
 - config_name: determiner_noun_agreement_1
   features:
   - name: sentence_good
@@ -1929,6 +1929,10 @@ configs:
   data_files:
   - split: train
     path: coordinate_structure_constraint_complex_left_branch/train-*
+- config_name: coordinate_structure_constraint_object_extraction
+  data_files:
+  - split: train
+    path: coordinate_structure_constraint_object_extraction/train-*
 ---
 
 # Dataset Card for "blimp"

coordinate_structure_constraint_object_extraction/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55d830a519e53f2400d3c436f751295a83a123f46084f247ce802ebec9585816
+size 51584

dataset_infos.json

@@ -551,62 +551,50 @@
         "features": {
             "sentence_good": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "sentence_bad": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "field": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "linguistics_term": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "UID": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "simple_LM_method": {
                 "dtype": "bool",
-                "id": null,
                 "_type": "Value"
             },
             "one_prefix_method": {
                 "dtype": "bool",
-                "id": null,
                 "_type": "Value"
             },
             "two_prefix_method": {
                 "dtype": "bool",
-                "id": null,
                 "_type": "Value"
             },
             "lexically_identical": {
                 "dtype": "bool",
-                "id": null,
                 "_type": "Value"
             },
             "pair_id": {
                 "dtype": "int32",
-                "id": null,
                 "_type": "Value"
             }
         },
-        "supervised_keys": null,
         "builder_name": "blimp",
+        "dataset_name": "blimp",
         "config_name": "coordinate_structure_constraint_object_extraction",
         "version": {
             "version_str": "0.1.0",
-            "description": null,
-            "datasets_version_to_prepare": null,
             "major": 0,
             "minor": 1,
             "patch": 0
@@ -614,20 +602,14 @@
         "splits": {
             "train": {
                 "name": "train",
-                "num_bytes": 173050,
+                "num_bytes": 171655,
                 "num_examples": 1000,
-                "dataset_name": "blimp"
-            }
-        },
-        "download_checksums": {
-            "https://raw.githubusercontent.com/alexwarstadt/blimp/master/data/coordinate_structure_constraint_object_extraction.jsonl": {
-                "num_bytes": 366045,
-                "checksum": "d01c27a7a893f3ec071a7913efa504d297671d86a987f26ddb565de914db2fd7"
+                "dataset_name": null
             }
         },
-        "download_size": 366045,
-        "dataset_size": 173050,
-        "size_in_bytes": 539095
+        "download_size": 51584,
+        "dataset_size": 171655,
+        "size_in_bytes": 223239
     },
     "determiner_noun_agreement_1": {
         "description": "\nBLiMP is a challenge set for evaluating what language models (LMs) know about\nmajor grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each\ncontaining 1000 minimal pairs isolating specific contrasts in syntax,\nmorphology, or semantics. The data is automatically generated according to\nexpert-crafted grammars.\n",