upload large-scale datasets

README.md

@@ -9,6 +9,14 @@ configs:
   - split: test
     path: "test-small.jsonl"
   default: true
+- config_name: large
+  data_files:
+  - split: train
+    path: "train-large.parquet"
+  - split: val
+    path: "val-large.parquet"
+  - split: test
+    path: "test-large.parquet"
 ---
 
 # ExcelFormer Benchmark
@@ -22,7 +30,8 @@ import numpy as np
 
 # process train split, similar to other splits
 data = {}
-datasets = load_dataset('jyansir/excelformer')
+datasets = load_dataset('jyansir/excelformer') # load 96 small-scale datasets in default
+# datasets = load_dataset('jyansir/excelformer', 'large') # load 21 large-scale datasets with specification
 dataset = datasets['train'].to_dict()
 for table_name, table, task in zip(dataset['dataset_name'], dataset['table'], dataset['task']):
     data[table_name] = {

test-large.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:625c6eb3587f1da14259ff9a224741efad34bc37df4c0c5b35dfb21e2de2bd5b
+size 101485910

train-large.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ba0356eb7402cdde34420f3213c4a4d990066fdeda08c79a28a2bfa0a47a286
+size 324802925

val-large.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57c040b74c7570f8901f37cf738682eefae78e5fffe764dc29dc3b7b42a77bda
+size 81188829