Add 'tweet_eval_hate' config data files

.gitattributes

@@ -24,3 +24,4 @@ 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
+tweet_eval_hate/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -298,6 +298,28 @@ dataset_info:
     num_examples: 5000
   download_size: 541547
   dataset_size: 972768
+- config_name: tweet_eval_hate
+  features:
+  - name: Tweet
+    dtype: string
+  - name: ID
+    dtype: int32
+  - name: Label
+    dtype:
+      class_label:
+        names:
+          '0': Unlabeled
+          '1': hate speech
+          '2': not hate speech
+  splits:
+  - name: train
+    num_bytes: 7488
+    num_examples: 50
+  - name: test
+    num_bytes: 440048
+    num_examples: 2966
+  download_size: 300542
+  dataset_size: 447536
 configs:
 - config_name: ade_corpus_v2
   data_files:
@@ -348,6 +370,12 @@ configs:
     path: terms_of_service/train-*
   - split: test
     path: terms_of_service/test-*
+- config_name: tweet_eval_hate
+  data_files:
+  - split: train
+    path: tweet_eval_hate/train-*
+  - split: test
+    path: tweet_eval_hate/test-*
 ---
 
 # Dataset Card for RAFT

dataset_infos.json

@@ -527,34 +527,26 @@
         "features": {
             "Tweet": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "ID": {
                 "dtype": "int32",
-                "id": null,
                 "_type": "Value"
             },
             "Label": {
-                "num_classes": 3,
                 "names": [
                     "Unlabeled",
                     "hate speech",
                     "not hate speech"
                 ],
-                "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": "tweet_eval_hate",
         "version": {
             "version_str": "1.1.0",
-            "description": null,
             "major": 1,
             "minor": 1,
             "patch": 0
@@ -562,111 +554,20 @@
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                 "name": "train",
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+                "num_bytes": 7488,
                 "num_examples": 50,
-                "dataset_name": "raft"
+                "dataset_name": null
             },
             "test": {
                 "name": "test",
-                "num_bytes": 440052,
+                "num_bytes": 440048,
                 "num_examples": 2966,
-                "dataset_name": "raft"
-            }
-        },
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+                "dataset_name": null
             }
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-        "download_size": 9752165,
-        "post_processing_size": null,
-        "dataset_size": 447544,
-        "size_in_bytes": 10199709
+        "download_size": 300542,
+        "dataset_size": 447536,
+        "size_in_bytes": 748078
     },
     "twitter_complaints": {
         "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",

tweet_eval_hate/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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