Add 'twitter_complaints' config data files

.gitattributes

@@ -25,3 +25,4 @@ systematic_review_inclusion/test-00000-of-00001.parquet filter=lfs diff=lfs merg
 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
+twitter_complaints/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -320,6 +320,28 @@ dataset_info:
     num_examples: 2966
   download_size: 300542
   dataset_size: 447536
+- config_name: twitter_complaints
+  features:
+  - name: Tweet text
+    dtype: string
+  - name: ID
+    dtype: int32
+  - name: Label
+    dtype:
+      class_label:
+        names:
+          '0': Unlabeled
+          '1': complaint
+          '2': no complaint
+  splits:
+  - name: train
+    num_bytes: 5348
+    num_examples: 50
+  - name: test
+    num_bytes: 369564
+    num_examples: 3399
+  download_size: 270136
+  dataset_size: 374912
 configs:
 - config_name: ade_corpus_v2
   data_files:
@@ -376,6 +398,12 @@ configs:
     path: tweet_eval_hate/train-*
   - split: test
     path: tweet_eval_hate/test-*
+- config_name: twitter_complaints
+  data_files:
+  - split: train
+    path: twitter_complaints/train-*
+  - split: test
+    path: twitter_complaints/test-*
 ---
 
 # Dataset Card for RAFT

dataset_infos.json

@@ -577,34 +577,26 @@
         "features": {
             "Tweet text": {
                 "dtype": "string",
-                "id": null,
                 "_type": "Value"
             },
             "ID": {
                 "dtype": "int32",
-                "id": null,
                 "_type": "Value"
             },
             "Label": {
-                "num_classes": 3,
                 "names": [
                     "Unlabeled",
                     "complaint",
                     "no complaint"
                 ],
-                "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": "twitter_complaints",
         "version": {
             "version_str": "1.1.0",
-            "description": null,
             "major": 1,
             "minor": 1,
             "patch": 0
@@ -612,111 +604,20 @@
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                 "name": "train",
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+                "num_bytes": 5348,
                 "num_examples": 50,
-                "dataset_name": "raft"
+                "dataset_name": null
             },
             "test": {
                 "name": "test",
-                "num_bytes": 369568,
+                "num_bytes": 369564,
                 "num_examples": 3399,
-                "dataset_name": "raft"
-            }
-        },
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+                "dataset_name": null
             }
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-        "download_size": 9752165,
-        "post_processing_size": null,
-        "dataset_size": 374920,
-        "size_in_bytes": 10127085
+        "download_size": 270136,
+        "dataset_size": 374912,
+        "size_in_bytes": 645048
     },
     "semiconductor_org_types": {
         "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",

twitter_complaints/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+size 263862