Add 'one_stop_english' config data files

.gitattributes

@@ -22,3 +22,5 @@ terms_of_service/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 neurips_impact_statement_risks/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 overruling/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 systematic_review_inclusion/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
+one_stop_english/train-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
+one_stop_english/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -169,6 +169,29 @@ dataset_info:
     num_examples: 150
   download_size: 163355
   dataset_size: 267736
+- config_name: one_stop_english
+  features:
+  - name: Article
+    dtype: string
+  - name: ID
+    dtype: int32
+  - name: Label
+    dtype:
+      class_label:
+        names:
+          '0': Unlabeled
+          '1': advanced
+          '2': elementary
+          '3': intermediate
+  splits:
+  - name: train
+    num_bytes: 201444
+    num_examples: 50
+  - name: test
+    num_bytes: 2090429
+    num_examples: 516
+  download_size: 1409192
+  dataset_size: 2291873
 - config_name: overruling
   features:
   - name: Sentence
@@ -294,6 +317,12 @@ configs:
     path: neurips_impact_statement_risks/train-*
   - split: test
     path: neurips_impact_statement_risks/test-*
+- config_name: one_stop_english
+  data_files:
+  - split: train
+    path: one_stop_english/train-*
+  - split: test
+    path: one_stop_english/test-*
 - config_name: overruling
   data_files:
   - split: train

dataset_infos.json

@@ -476,35 +476,27 @@
         "features": {
             "Article": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "ID": {
                 "dtype": "int32",
-                "id": null,
                 "_type": "Value"
             },
             "Label": {
-                "num_classes": 4,
                 "names": [
                     "Unlabeled",
                     "advanced",
                     "elementary",
                     "intermediate"
                 ],
-                "names_file": null,
-                "id": null,
                 "_type": "ClassLabel"
             }
         },
-        "post_processed": null,
-        "supervised_keys": null,
-        "task_templates": null,
-        "builder_name": "raft",
+        "builder_name": "parquet",
+        "dataset_name": "raft",
         "config_name": "one_stop_english",
         "version": {
             "version_str": "1.1.0",
-            "description": null,
             "major": 1,
             "minor": 1,
             "patch": 0
@@ -512,111 +504,20 @@
         "splits": {
             "train": {
                 "name": "train",
-                "num_bytes": 201448,
+                "num_bytes": 201444,
                 "num_examples": 50,
-                "dataset_name": "raft"
+                "dataset_name": null
             },
             "test": {
                 "name": "test",
-                "num_bytes": 2090433,
+                "num_bytes": 2090429,
                 "num_examples": 516,
-                "dataset_name": "raft"
-            }
-        },
-        "download_checksums": {
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-                "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"
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+                "dataset_name": null
             }
         },
-        "download_size": 9752165,
-        "post_processing_size": null,
-        "dataset_size": 2291881,
-        "size_in_bytes": 12044046
+        "download_size": 1409192,
+        "dataset_size": 2291873,
+        "size_in_bytes": 3701065
     },
     "tweet_eval_hate": {
         "description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n",

one_stop_english/test-00000-of-00001.parquet

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one_stop_english/train-00000-of-00001.parquet

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