Add 'terms_of_service' config data files

.gitattributes

@@ -18,3 +18,4 @@ data/ filter=lfs diff=lfs merge=lfs -text
 tai_safety_research/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
 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

README.md

@@ -177,6 +177,28 @@ dataset_info:
     num_examples: 1639
   download_size: 948201
   dataset_size: 1689786
+- config_name: terms_of_service
+  features:
+  - name: Sentence
+    dtype: string
+  - name: ID
+    dtype: int32
+  - name: Label
+    dtype:
+      class_label:
+        names:
+          '0': Unlabeled
+          '1': not potentially unfair
+          '2': potentially unfair
+  splits:
+  - name: train
+    num_bytes: 10948
+    num_examples: 50
+  - name: test
+    num_bytes: 961820
+    num_examples: 5000
+  download_size: 541547
+  dataset_size: 972768
 configs:
 - config_name: ade_corpus_v2
   data_files:
@@ -197,6 +219,12 @@ configs:
   - split: test
     path: tai_safety_research/test-*
   default: true
+- config_name: terms_of_service
+  data_files:
+  - split: train
+    path: terms_of_service/train-*
+  - split: test
+    path: terms_of_service/test-*
 ---
 
 # Dataset Card for RAFT

dataset_infos.json

@@ -182,34 +182,26 @@
         "features": {
             "Sentence": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "ID": {
                 "dtype": "int32",
-                "id": null,
                 "_type": "Value"
             },
             "Label": {
-                "num_classes": 3,
                 "names": [
                     "Unlabeled",
                     "not potentially unfair",
                     "potentially unfair"
                 ],
-                "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": "terms_of_service",
         "version": {
             "version_str": "1.1.0",
-            "description": null,
             "major": 1,
             "minor": 1,
             "patch": 0
@@ -217,111 +209,20 @@
         "splits": {
             "train": {
                 "name": "train",
-                "num_bytes": 10952,
+                "num_bytes": 10948,
                 "num_examples": 50,
-                "dataset_name": "raft"
+                "dataset_name": null
             },
             "test": {
                 "name": "test",
-                "num_bytes": 961824,
+                "num_bytes": 961820,
                 "num_examples": 5000,
-                "dataset_name": "raft"
-            }
-        },
-        "download_checksums": {
-            "data/ade_corpus_v2/train.csv": {
-                "num_bytes": 7788,
-                "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"
-            },
-            "data/ade_corpus_v2/test_unlabeled.csv": {
-                "num_bytes": 661890,
-                "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"
-            },
-            "data/banking_77/train.csv": {
-                "num_bytes": 3914,
-                "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"
-            },
-            "data/banking_77/test_unlabeled.csv": {
-                "num_bytes": 327152,
-                "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"
-            },
-            "data/terms_of_service/train.csv": {
-                "num_bytes": 11528,
-                "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"
-            },
-            "data/terms_of_service/test_unlabeled.csv": {
-                "num_bytes": 917272,
-                "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"
-            },
-            "data/tai_safety_research/train.csv": {
-                "num_bytes": 54758,
-                "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"
-            },
-            "data/tai_safety_research/test_unlabeled.csv": {
-                "num_bytes": 1594375,
-                "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"
-            },
-            "data/neurips_impact_statement_risks/train.csv": {
-                "num_bytes": 70008,
-                "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"
-            },
-            "data/neurips_impact_statement_risks/test_unlabeled.csv": {
-                "num_bytes": 196429,
-                "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"
-            },
-            "data/overruling/train.csv": {
-                "num_bytes": 7585,
-                "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"
-            },
-            "data/overruling/test_unlabeled.csv": {
-                "num_bytes": 412483,
-                "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"
-            },
-            "data/systematic_review_inclusion/train.csv": {
-                "num_bytes": 52491,
-                "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"
-            },
-            "data/systematic_review_inclusion/test_unlabeled.csv": {
-                "num_bytes": 2309265,
-                "checksum": "20710c15f7fda010a3a86a0eda52c5ceb6a709b626610d19e1fc353c42c2d955"
-            },
-            "data/one_stop_english/train.csv": {
-                "num_bytes": 201489,
-                "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"
-            },
-            "data/one_stop_english/test_unlabeled.csv": {
-                "num_bytes": 2085747,
-                "checksum": "31e299f91925647744e998f221c0f66c2719df297a20d36d993c8892616a4a53"
-            },
-            "data/tweet_eval_hate/train.csv": {
-                "num_bytes": 7642,
-                "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"
-            },
-            "data/tweet_eval_hate/test_unlabeled.csv": {
-                "num_bytes": 412052,
-                "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"
-            },
-            "data/twitter_complaints/train.csv": {
-                "num_bytes": 5376,
-                "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"
-            },
-            "data/twitter_complaints/test_unlabeled.csv": {
-                "num_bytes": 336272,
-                "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"
-            },
-            "data/semiconductor_org_types/train.csv": {
-                "num_bytes": 8120,
-                "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"
-            },
-            "data/semiconductor_org_types/test_unlabeled.csv": {
-                "num_bytes": 68529,
-                "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"
+                "dataset_name": null
             }
         },
-        "download_size": 9752165,
-        "post_processing_size": null,
-        "dataset_size": 972776,
-        "size_in_bytes": 10724941
+        "download_size": 541547,
+        "dataset_size": 972768,
+        "size_in_bytes": 1514315
     },
     "tai_safety_research": {
         "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",

terms_of_service/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cfff8c8315b738997130336ffb913e1ab897021f4f4e5d1a38d089a972b0e0f
+size 532667