Add bg data files

README.md

@@ -118,16 +118,16 @@ dataset_info:
           '2': contradiction
   splits:
   - name: train
-    num_bytes: 125973545
+    num_bytes: 125973225
     num_examples: 392702
   - name: test
-    num_bytes: 1573042
+    num_bytes: 1573034
     num_examples: 5010
   - name: validation
-    num_bytes: 774069
+    num_bytes: 774061
     num_examples: 2490
-  download_size: 483963712
-  dataset_size: 128320656
+  download_size: 66117878
+  dataset_size: 128320320
 - config_name: de
   features:
   - name: premise
@@ -462,6 +462,14 @@ configs:
     path: ar/test-*
   - split: validation
     path: ar/validation-*
+- config_name: bg
+  data_files:
+  - split: train
+    path: bg/train-*
+  - split: test
+    path: bg/test-*
+  - split: validation
+    path: bg/validation-*
 ---
 
 # Dataset Card for "xnli"

bg/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:333a5e84e09415f6a80dd285c4aa64d164bf721237b3e3d34892ce72066c92c1
+size 447341

bg/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df65135e5813d4a42b3fd8e018ebfaecd981f7c313bbcfd7288e2019f0f4296c
+size 65447048

bg/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfd1a75d8b1c82b97d857aa91cd9bf08e75d1ea58ab43109c2644e82079ac981
+size 223489

dataset_infos.json

@@ -64,29 +64,23 @@
         "features": {
             "premise": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "hypothesis": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "label": {
-                "num_classes": 3,
                 "names": [
                     "entailment",
                     "neutral",
                     "contradiction"
                 ],
-                "names_file": null,
-                "id": null,
                 "_type": "ClassLabel"
             }
         },
-        "post_processed": null,
-        "supervised_keys": null,
         "builder_name": "xnli",
+        "dataset_name": "xnli",
         "config_name": "bg",
         "version": {
             "version_str": "1.1.0",
@@ -98,37 +92,26 @@
         "splits": {
             "train": {
                 "name": "train",
-                "num_bytes": 125973545,
+                "num_bytes": 125973225,
                 "num_examples": 392702,
-                "dataset_name": "xnli"
+                "dataset_name": null
             },
             "test": {
                 "name": "test",
-                "num_bytes": 1573042,
+                "num_bytes": 1573034,
                 "num_examples": 5010,
-                "dataset_name": "xnli"
+                "dataset_name": null
             },
             "validation": {
                 "name": "validation",
-                "num_bytes": 774069,
+                "num_bytes": 774061,
                 "num_examples": 2490,
-                "dataset_name": "xnli"
-            }
-        },
-        "download_checksums": {
-            "https://dl.fbaipublicfiles.com/XNLI/XNLI-MT-1.0.zip": {
-                "num_bytes": 466098360,
-                "checksum": "f732517ba2fb1d550e9f3c2aabaef6017c91ee2dcec90e878f138764d224db05"
-            },
-            "https://dl.fbaipublicfiles.com/XNLI/XNLI-1.0.zip": {
-                "num_bytes": 17865352,
-                "checksum": "4ba1d5e1afdb7161f0f23c66dc787802ccfa8a25a3ddd3b165a35e50df346ab1"
+                "dataset_name": null
             }
         },
-        "download_size": 483963712,
-        "post_processing_size": null,
-        "dataset_size": 128320656,
-        "size_in_bytes": 612284368
+        "download_size": 66117878,
+        "dataset_size": 128320320,
+        "size_in_bytes": 194438198
     },
     "de": {
         "description": "XNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n",