Add 'neurips_impact_statement_risks' config data files

.gitattributes

@@ -19,3 +19,4 @@ tai_safety_research/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -t
 ade_corpus_v2/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 banking_77/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 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

README.md

@@ -143,6 +143,32 @@ dataset_info:
     num_examples: 5000
   download_size: 214821
   dataset_size: 380138
+- config_name: neurips_impact_statement_risks
+  features:
+  - name: Paper title
+    dtype: string
+  - name: Paper link
+    dtype: string
+  - name: Impact statement
+    dtype: string
+  - name: ID
+    dtype: int32
+  - name: Label
+    dtype:
+      class_label:
+        names:
+          '0': Unlabeled
+          '1': doesn't mention a harmful application
+          '2': mentions a harmful application
+  splits:
+  - name: train
+    num_bytes: 69037
+    num_examples: 50
+  - name: test
+    num_bytes: 198699
+    num_examples: 150
+  download_size: 163355
+  dataset_size: 267736
 - config_name: tai_safety_research
   features:
   - name: Title
@@ -212,6 +238,12 @@ configs:
     path: banking_77/train-*
   - split: test
     path: banking_77/test-*
+- config_name: neurips_impact_statement_risks
+  data_files:
+  - split: train
+    path: neurips_impact_statement_risks/train-*
+  - split: test
+    path: neurips_impact_statement_risks/test-*
 - config_name: tai_safety_research
   data_files:
   - split: train

dataset_infos.json

@@ -306,44 +306,34 @@
         "features": {
             "Paper title": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "Paper link": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "Impact statement": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "ID": {
                 "dtype": "int32",
-                "id": null,
                 "_type": "Value"
             },
             "Label": {
-                "num_classes": 3,
                 "names": [
                     "Unlabeled",
                     "doesn't mention a harmful application",
                     "mentions a harmful application"
                 ],
-                "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": "neurips_impact_statement_risks",
         "version": {
             "version_str": "1.1.0",
-            "description": null,
             "major": 1,
             "minor": 1,
             "patch": 0
@@ -351,111 +341,20 @@
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+                "num_bytes": 69037,
                 "num_examples": 50,
-                "dataset_name": "raft"
+                "dataset_name": null
             },
             "test": {
                 "name": "test",
-                "num_bytes": 198711,
+                "num_bytes": 198699,
                 "num_examples": 150,
-                "dataset_name": "raft"
-            }
-        },
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-        "post_processing_size": null,
-        "dataset_size": 267760,
-        "size_in_bytes": 10019925
+        "download_size": 163355,
+        "dataset_size": 267736,
+        "size_in_bytes": 431091
     },
     "overruling": {
         "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",

neurips_impact_statement_risks/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 118917