Datasets:

rfernand
/

templatic_generation_tasks

@@ -1,391 +1,392 @@
----
-configs:
-- config_name: "10_shot_rlw"
-  data_files:
-  - split: dev
-    path: "10_shot_rlw/dev.*"
-  - split: ood_cons_count_10
-    path: "10_shot_rlw/ood_cons_count_10.*"
-  - split: ood_cons_count_3
-    path: "10_shot_rlw/ood_cons_count_3.*"
-  - split: ood_cons_count_5
-    path: "10_shot_rlw/ood_cons_count_5.*"
-  - split: ood_cons_count_7
-    path: "10_shot_rlw/ood_cons_count_7.*"
-  - split: ood_cons_len_10
-    path: "10_shot_rlw/ood_cons_len_10.*"
-  - split: ood_cons_len_3
-    path: "10_shot_rlw/ood_cons_len_3.*"
-  - split: ood_cons_len_5
-    path: "10_shot_rlw/ood_cons_len_5.*"
-  - split: ood_cons_len_7
-    path: "10_shot_rlw/ood_cons_len_7.*"
-  - split: ood_lexical
-    path: "10_shot_rlw/ood_lexical.*"
-  - split: test
-    path: "10_shot_rlw/test.*"
-  - split: train
-    path: "10_shot_rlw/train.*"
-- config_name: "1_shot_eng"
-  data_files:
-  - split: dev
-    path: "1_shot_eng/dev.*"
-  - split: ood_cons_count_3
-    path: "1_shot_eng/ood_cons_count_3.*"
-  - split: ood_cons_count_5
-    path: "1_shot_eng/ood_cons_count_5.*"
-  - split: ood_cons_len_3
-    path: "1_shot_eng/ood_cons_len_3.*"
-  - split: ood_cons_len_5
-    path: "1_shot_eng/ood_cons_len_5.*"
-  - split: ood_lexical
-    path: "1_shot_eng/ood_lexical.*"
-  - split: other_tasks_id
-    path: "1_shot_eng/other_tasks_id.*"
-  - split: other_tasks_ood
-    path: "1_shot_eng/other_tasks_ood.*"
-  - split: test
-    path: "1_shot_eng/test.*"
-  - split: train
-    path: "1_shot_eng/train.*"
-- config_name: "1_shot_rlw"
-  data_files:
-  - split: dev
-    path: "1_shot_rlw/dev.*"
-  - split: ood_cons_count_10
-    path: "1_shot_rlw/ood_cons_count_10.*"
-  - split: ood_cons_count_3
-    path: "1_shot_rlw/ood_cons_count_3.*"
-  - split: ood_cons_count_5
-    path: "1_shot_rlw/ood_cons_count_5.*"
-  - split: ood_cons_count_7
-    path: "1_shot_rlw/ood_cons_count_7.*"
-  - split: ood_cons_len_10
-    path: "1_shot_rlw/ood_cons_len_10.*"
-  - split: ood_cons_len_3
-    path: "1_shot_rlw/ood_cons_len_3.*"
-  - split: ood_cons_len_5
-    path: "1_shot_rlw/ood_cons_len_5.*"
-  - split: ood_cons_len_7
-    path: "1_shot_rlw/ood_cons_len_7.*"
-  - split: ood_lexical
-    path: "1_shot_rlw/ood_lexical.*"
-  - split: test
-    path: "1_shot_rlw/test.*"
-  - split: train
-    path: "1_shot_rlw/train.*"
-- config_name: "1_shot_rlw_10x"
-  data_files:
-  - split: dev
-    path: "1_shot_rlw_10x/dev.*"
-  - split: ood_cons_count_10
-    path: "1_shot_rlw_10x/ood_cons_count_10.*"
-  - split: ood_cons_count_3
-    path: "1_shot_rlw_10x/ood_cons_count_3.*"
-  - split: ood_cons_count_5
-    path: "1_shot_rlw_10x/ood_cons_count_5.*"
-  - split: ood_cons_count_7
-    path: "1_shot_rlw_10x/ood_cons_count_7.*"
-  - split: ood_cons_len_10
-    path: "1_shot_rlw_10x/ood_cons_len_10.*"
-  - split: ood_cons_len_3
-    path: "1_shot_rlw_10x/ood_cons_len_3.*"
-  - split: ood_cons_len_5
-    path: "1_shot_rlw_10x/ood_cons_len_5.*"
-  - split: ood_cons_len_7
-    path: "1_shot_rlw_10x/ood_cons_len_7.*"
-  - split: ood_lexical
-    path: "1_shot_rlw_10x/ood_lexical.*"
-  - split: test
-    path: "1_shot_rlw_10x/test.*"
-  - split: train
-    path: "1_shot_rlw_10x/train.*"
-- config_name: "2_shot_rlw"
-  data_files:
-  - split: dev
-    path: "2_shot_rlw/dev.*"
-  - split: ood_cons_count_10
-    path: "2_shot_rlw/ood_cons_count_10.*"
-  - split: ood_cons_count_3
-    path: "2_shot_rlw/ood_cons_count_3.*"
-  - split: ood_cons_count_5
-    path: "2_shot_rlw/ood_cons_count_5.*"
-  - split: ood_cons_count_7
-    path: "2_shot_rlw/ood_cons_count_7.*"
-  - split: ood_cons_len_10
-    path: "2_shot_rlw/ood_cons_len_10.*"
-  - split: ood_cons_len_3
-    path: "2_shot_rlw/ood_cons_len_3.*"
-  - split: ood_cons_len_5
-    path: "2_shot_rlw/ood_cons_len_5.*"
-  - split: ood_cons_len_7
-    path: "2_shot_rlw/ood_cons_len_7.*"
-  - split: ood_lexical
-    path: "2_shot_rlw/ood_lexical.*"
-  - split: test
-    path: "2_shot_rlw/test.*"
-  - split: train
-    path: "2_shot_rlw/train.*"
-- config_name: "3_shot_rlw"
-  data_files:
-  - split: dev
-    path: "3_shot_rlw/dev.*"
-  - split: ood_cons_count_10
-    path: "3_shot_rlw/ood_cons_count_10.*"
-  - split: ood_cons_count_3
-    path: "3_shot_rlw/ood_cons_count_3.*"
-  - split: ood_cons_count_5
-    path: "3_shot_rlw/ood_cons_count_5.*"
-  - split: ood_cons_count_7
-    path: "3_shot_rlw/ood_cons_count_7.*"
-  - split: ood_cons_len_10
-    path: "3_shot_rlw/ood_cons_len_10.*"
-  - split: ood_cons_len_3
-    path: "3_shot_rlw/ood_cons_len_3.*"
-  - split: ood_cons_len_5
-    path: "3_shot_rlw/ood_cons_len_5.*"
-  - split: ood_cons_len_7
-    path: "3_shot_rlw/ood_cons_len_7.*"
-  - split: ood_lexical
-    path: "3_shot_rlw/ood_lexical.*"
-  - split: test
-    path: "3_shot_rlw/test.*"
-  - split: train
-    path: "3_shot_rlw/train.*"
-- config_name: "5_shot_rlw"
-  data_files:
-  - split: dev
-    path: "5_shot_rlw/dev.*"
-  - split: ood_cons_count_10
-    path: "5_shot_rlw/ood_cons_count_10.*"
-  - split: ood_cons_count_3
-    path: "5_shot_rlw/ood_cons_count_3.*"
-  - split: ood_cons_count_5
-    path: "5_shot_rlw/ood_cons_count_5.*"
-  - split: ood_cons_count_7
-    path: "5_shot_rlw/ood_cons_count_7.*"
-  - split: ood_cons_len_10
-    path: "5_shot_rlw/ood_cons_len_10.*"
-  - split: ood_cons_len_3
-    path: "5_shot_rlw/ood_cons_len_3.*"
-  - split: ood_cons_len_5
-    path: "5_shot_rlw/ood_cons_len_5.*"
-  - split: ood_cons_len_7
-    path: "5_shot_rlw/ood_cons_len_7.*"
-  - split: ood_lexical
-    path: "5_shot_rlw/ood_lexical.*"
-  - split: test
-    path: "5_shot_rlw/test.*"
-  - split: train
-    path: "5_shot_rlw/train.*"
-annotations_creators:
-- machine-generated
-language:
-- en
-language_creators:
-- machine-generated
-license:
-- other
-multilinguality:
-- monolingual
-pretty_name: Templatic Generation Tasks for In-Context Learning Research
-size_categories:
-- 10K<n<100K
-- 1K<n<10K
-- n<1K
-source_datasets:
-- original
-tags:
-- seq2seq
-task_categories:
-- text2text-generation
-task_ids: []
----
-# Dataset Card for Active/Passive/Logical Transforms
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Dataset Subsets (Tasks)](#data-tasks)
-  - [Dataset Splits](#data-splits)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:** [Roland Fernandez](mailto:[email protected])
-### Dataset Summary
-This dataset is a synthetic dataset containing a set of templatic generation tasks using both English and random 2-letter words.
-### Supported Tasks and Leaderboards
-[TBD]
-### Languages
-All data is in English or random 2-letter words.
-## Dataset Structure
-The dataset consists of several subsets, or tasks. Each task contains a train split, a dev split, and a
-test split, and multiple out-of-distribution splits.
-Each sample in a split contains a source string, a target string, and an annotation string (describing the sample).
-### Dataset Subsets (Tasks)
-The dataset consists of the following tasks:
-```
-- 1_shot_rlw              (1 example input/output pair, a test input, and the gold output, all using random 2-letter words)
-- 1_shot_eng              (same as 1_shot_rlw but using English words).
-- 1_shot_rlw_10x          (same as 1_shot_rlw, but with 10x the training samples)
-- 2_shot_rlw              (2 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
-- 3_shot_rlw              (3 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
-- 5_shot_rlw              (5 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
-- 10_shot_rtw             (10 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
-```
-### Data Splits
-Most tasks have the following splits:
-  - train
-  - dev
-  - test
-  - ood_lexical
-  - ood_cons_count_3
-  - ood_cons_count_5
-  - ood_cons_count_7
-  - ood_cons_count_10
-  - ood_cons_len_3
-  - ood_cons_len_5
-  - ood_cons_len_7
-  - ood_cons_len_10
-Here is a table showing how the number of examples varies by split (for most tasks):
-| Dataset Split | Number of Instances in Split                |
-| ------------- | ------------------------------------------- |
-| train         | 280,000                                     |
-| dev           |  35,000                                     |
-| test          |  35,000                                     |
-| ood_*         |  84,000                                     |
-### Data Instances
-Each sample consits of a source, target, and annotation string (all tab separated).
-Here is an example from the *train* split of the *1_shot_eng* task:
-```
-{
-  'raw': 'Q any mouse ) ; bear A any mouse & . Q road ) ; building A	road & .	{"cons_count": "Q2A1", "cons_len": "Q21.Q11"}'
-  'source': 'Q any mouse ) ; bear A any mouse & . Q road ) ; building A',
-  'target': 'road & .',
-  'annotation': '{"cons_count": "Q2A1", "cons_len": "Q21.Q11"}'
-}
-```
-### Data Fields
-- `source`: the string containing the N-shot examples and the test cue
-- `target`: the string containing the desired (gold) output
-- `annotation`: the string describing the example (as a python or JSON dictionary)
-## Dataset Creation
-### Curation Rationale
-We wanted a dataset that would test in-context (and from scratch) learning of abstract, semantic-free symbolic transformations,
-based on a random template for each example.  The dataset is designed to test 3 types of out of distribution generalization:
-  - lexical - known words used in new contexts (relative to train split)
-  - length - train split uses constituents of 1, 2, or 4 words; OOD splits use 3, 5, 7, or 10 words
-  - count - train split uses 1, 2, or 4 constituents; OOD splits use 3, 5, 7, or 10 constituents
-### Source Data
-[N/A]
-#### Initial Data Collection and Normalization
-[N/A]
-#### Who are the source language producers?
-The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.
-### Annotations
-Besides the source and target strings, each sample contains an annotation string that describes the sample.
-#### Annotation process
-The annotation columns were generated from each sample template.
-#### Who are the annotators?
-[N/A]
-### Personal and Sensitive Information
-No names or other sensitive information are included in the data.
-## Considerations for Using the Data
-### Social Impact of Dataset
-The purpose of this dataset is to research how LLM and from-scratch model can learn to solve templatic generation tasks.
-### Discussion of Biases
-[TBD]
-### Other Known Limitations
-[TBD]
-## Additional Information
-The internal name of this dataset is nc_tgt_v11.  Also see DATASET_INFO.md and GRAMMAR.md files.
-### Dataset Curators
-The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.
-### Licensing Information
-This dataset is released under the [Permissive 2.0 license](https://cdla.dev/permissive-2-0/).
-### Citation Information
-[TBD]
-### Contributions
-Thanks to [The Neurocompositional AI group at Microsoft Research](https://www.microsoft.com/en-us/research/project/neurocompositional-ai/) for creating and adding this dataset.

+---
+configs:
+- config_name: "10_shot_rlw"
+  data_files:
+  - split: dev
+    path: "10_shot_rlw/dev.*"
+  - split: ood_cons_count_10
+    path: "10_shot_rlw/ood_cons_count_10.*"
+  - split: ood_cons_count_3
+    path: "10_shot_rlw/ood_cons_count_3.*"
+  - split: ood_cons_count_5
+    path: "10_shot_rlw/ood_cons_count_5.*"
+  - split: ood_cons_count_7
+    path: "10_shot_rlw/ood_cons_count_7.*"
+  - split: ood_cons_len_10
+    path: "10_shot_rlw/ood_cons_len_10.*"
+  - split: ood_cons_len_3
+    path: "10_shot_rlw/ood_cons_len_3.*"
+  - split: ood_cons_len_5
+    path: "10_shot_rlw/ood_cons_len_5.*"
+  - split: ood_cons_len_7
+    path: "10_shot_rlw/ood_cons_len_7.*"
+  - split: ood_lexical
+    path: "10_shot_rlw/ood_lexical.*"
+  - split: test
+    path: "10_shot_rlw/test.*"
+  - split: train
+    path: "10_shot_rlw/train.*"
+- config_name: "1_shot_eng"
+  data_files:
+  - split: dev
+    path: "1_shot_eng/dev.*"
+  - split: ood_cons_count_3
+    path: "1_shot_eng/ood_cons_count_3.*"
+  - split: ood_cons_count_5
+    path: "1_shot_eng/ood_cons_count_5.*"
+  - split: ood_cons_len_3
+    path: "1_shot_eng/ood_cons_len_3.*"
+  - split: ood_cons_len_5
+    path: "1_shot_eng/ood_cons_len_5.*"
+  - split: ood_lexical
+    path: "1_shot_eng/ood_lexical.*"
+  - split: other_tasks_id
+    path: "1_shot_eng/other_tasks_id.*"
+  - split: other_tasks_ood
+    path: "1_shot_eng/other_tasks_ood.*"
+  - split: test
+    path: "1_shot_eng/test.*"
+  - split: train
+    path: "1_shot_eng/train.*"
+- config_name: "1_shot_rlw"
+  data_files:
+  - split: dev
+    path: "1_shot_rlw/dev.*"
+  - split: ood_cons_count_10
+    path: "1_shot_rlw/ood_cons_count_10.*"
+  - split: ood_cons_count_3
+    path: "1_shot_rlw/ood_cons_count_3.*"
+  - split: ood_cons_count_5
+    path: "1_shot_rlw/ood_cons_count_5.*"
+  - split: ood_cons_count_7
+    path: "1_shot_rlw/ood_cons_count_7.*"
+  - split: ood_cons_len_10
+    path: "1_shot_rlw/ood_cons_len_10.*"
+  - split: ood_cons_len_3
+    path: "1_shot_rlw/ood_cons_len_3.*"
+  - split: ood_cons_len_5
+    path: "1_shot_rlw/ood_cons_len_5.*"
+  - split: ood_cons_len_7
+    path: "1_shot_rlw/ood_cons_len_7.*"
+  - split: ood_lexical
+    path: "1_shot_rlw/ood_lexical.*"
+  - split: test
+    path: "1_shot_rlw/test.*"
+  - split: train
+    path: "1_shot_rlw/train.*"
+- config_name: "1_shot_rlw_10x"
+  data_files:
+  - split: dev
+    path: "1_shot_rlw_10x/dev.*"
+  - split: ood_cons_count_10
+    path: "1_shot_rlw_10x/ood_cons_count_10.*"
+  - split: ood_cons_count_3
+    path: "1_shot_rlw_10x/ood_cons_count_3.*"
+  - split: ood_cons_count_5
+    path: "1_shot_rlw_10x/ood_cons_count_5.*"
+  - split: ood_cons_count_7
+    path: "1_shot_rlw_10x/ood_cons_count_7.*"
+  - split: ood_cons_len_10
+    path: "1_shot_rlw_10x/ood_cons_len_10.*"
+  - split: ood_cons_len_3
+    path: "1_shot_rlw_10x/ood_cons_len_3.*"
+  - split: ood_cons_len_5
+    path: "1_shot_rlw_10x/ood_cons_len_5.*"
+  - split: ood_cons_len_7
+    path: "1_shot_rlw_10x/ood_cons_len_7.*"
+  - split: ood_lexical
+    path: "1_shot_rlw_10x/ood_lexical.*"
+  - split: test
+    path: "1_shot_rlw_10x/test.*"
+  - split: train
+    path: "1_shot_rlw_10x/train.*"
+- config_name: "2_shot_rlw"
+  data_files:
+  - split: dev
+    path: "2_shot_rlw/dev.*"
+  - split: ood_cons_count_10
+    path: "2_shot_rlw/ood_cons_count_10.*"
+  - split: ood_cons_count_3
+    path: "2_shot_rlw/ood_cons_count_3.*"
+  - split: ood_cons_count_5
+    path: "2_shot_rlw/ood_cons_count_5.*"
+  - split: ood_cons_count_7
+    path: "2_shot_rlw/ood_cons_count_7.*"
+  - split: ood_cons_len_10
+    path: "2_shot_rlw/ood_cons_len_10.*"
+  - split: ood_cons_len_3
+    path: "2_shot_rlw/ood_cons_len_3.*"
+  - split: ood_cons_len_5
+    path: "2_shot_rlw/ood_cons_len_5.*"
+  - split: ood_cons_len_7
+    path: "2_shot_rlw/ood_cons_len_7.*"
+  - split: ood_lexical
+    path: "2_shot_rlw/ood_lexical.*"
+  - split: test
+    path: "2_shot_rlw/test.*"
+  - split: train
+    path: "2_shot_rlw/train.*"
+- config_name: "3_shot_rlw"
+  data_files:
+  - split: dev
+    path: "3_shot_rlw/dev.*"
+  - split: ood_cons_count_10
+    path: "3_shot_rlw/ood_cons_count_10.*"
+  - split: ood_cons_count_3
+    path: "3_shot_rlw/ood_cons_count_3.*"
+  - split: ood_cons_count_5
+    path: "3_shot_rlw/ood_cons_count_5.*"
+  - split: ood_cons_count_7
+    path: "3_shot_rlw/ood_cons_count_7.*"
+  - split: ood_cons_len_10
+    path: "3_shot_rlw/ood_cons_len_10.*"
+  - split: ood_cons_len_3
+    path: "3_shot_rlw/ood_cons_len_3.*"
+  - split: ood_cons_len_5
+    path: "3_shot_rlw/ood_cons_len_5.*"
+  - split: ood_cons_len_7
+    path: "3_shot_rlw/ood_cons_len_7.*"
+  - split: ood_lexical
+    path: "3_shot_rlw/ood_lexical.*"
+  - split: test
+    path: "3_shot_rlw/test.*"
+  - split: train
+    path: "3_shot_rlw/train.*"
+- config_name: "5_shot_rlw"
+  data_files:
+  - split: dev
+    path: "5_shot_rlw/dev.*"
+  - split: ood_cons_count_10
+    path: "5_shot_rlw/ood_cons_count_10.*"
+  - split: ood_cons_count_3
+    path: "5_shot_rlw/ood_cons_count_3.*"
+  - split: ood_cons_count_5
+    path: "5_shot_rlw/ood_cons_count_5.*"
+  - split: ood_cons_count_7
+    path: "5_shot_rlw/ood_cons_count_7.*"
+  - split: ood_cons_len_10
+    path: "5_shot_rlw/ood_cons_len_10.*"
+  - split: ood_cons_len_3
+    path: "5_shot_rlw/ood_cons_len_3.*"
+  - split: ood_cons_len_5
+    path: "5_shot_rlw/ood_cons_len_5.*"
+  - split: ood_cons_len_7
+    path: "5_shot_rlw/ood_cons_len_7.*"
+  - split: ood_lexical
+    path: "5_shot_rlw/ood_lexical.*"
+  - split: test
+    path: "5_shot_rlw/test.*"
+  - split: train
+    path: "5_shot_rlw/train.*"
+annotations_creators:
+- machine-generated
+language:
+- en
+language_creators:
+- machine-generated
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: Templatic Generation Tasks for In-Context Learning Research
+size_categories:
+- 10K<n<100K
+- 1K<n<10K
+- n<1K
+source_datasets:
+- original
+tags:
+- seq2seq
+task_categories:
+- text2text-generation
+task_ids: []
+---
+# Dataset Card for Active/Passive/Logical Transforms
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Dataset Subsets (Tasks)](#data-tasks)
+  - [Dataset Splits](#data-splits)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+## Dataset Description
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:** [Roland Fernandez](mailto:[email protected])
+### Dataset Summary
+This dataset is a synthetic dataset containing a set of templatic generation tasks using both English and random 2-letter words.
+### Supported Tasks and Leaderboards
+[TBD]
+### Languages
+All data is in English or random 2-letter words.
+## Dataset Structure
+The dataset consists of several subsets, or tasks. Each task contains a train split, a dev split, and a
+test split, and multiple out-of-distribution splits.
+Each sample in a split contains a source string, a target string, and an annotation string (describing the sample).
+### Dataset Subsets (Tasks)
+The dataset consists of the following tasks:
+```
+- 1_shot_rlw              (1 example input/output pair, a test input, and the gold output, all using random 2-letter words)
+- 1_shot_eng              (same as 1_shot_rlw but using English words).
+- 1_shot_rlw_10x          (same as 1_shot_rlw, but with 10x the training samples)
+- 2_shot_rlw              (2 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
+- 3_shot_rlw              (3 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
+- 5_shot_rlw              (5 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
+- 10_shot_rtw             (10 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
+```
+### Data Splits
+Most tasks have the following splits:
+  - train
+  - dev
+  - test
+  - ood_lexical
+  - ood_cons_count_3
+  - ood_cons_count_5
+  - ood_cons_count_7
+  - ood_cons_count_10
+  - ood_cons_len_3
+  - ood_cons_len_5
+  - ood_cons_len_7
+  - ood_cons_len_10
+Here is a table showing how the number of examples varies by split (for most tasks):
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| train         | 280,000                                     |
+| dev           |  35,000                                     |
+| test          |  35,000                                     |
+| ood_*         |  84,000                                     |
+### Data Instances
+Each sample consits of a source, target, and annotation string (all tab separated).
+Here is an example from the *train* split of the *1_shot_eng* task:
+```
+{
+  'raw': 'Q any mouse ) ; bear A any mouse & . Q road ) ; building A	road & .	{"cons_count": "Q2A1", "cons_len": "Q21.Q11"}'
+  'source': 'Q any mouse ) ; bear A any mouse & . Q road ) ; building A',
+  'target': 'road & .',
+  'annotation': '{"cons_count": "Q2A1", "cons_len": "Q21.Q11"}'
+}
+```
+### Data Fields
+- `source`: the string containing the N-shot examples and the test cue
+- `target`: the string containing the desired (gold) output
+- `annotation`: the string describing the example (as a python or JSON dictionary)
+## Dataset Creation
+### Curation Rationale
+We wanted a dataset that would test in-context (and from scratch) learning of abstract, semantic-free symbolic transformations,
+based on a random template for each example.  The dataset is designed to test 3 types of out of distribution generalization:
+  - lexical - known words used in new contexts (relative to train split)
+  - length - train split uses constituents of 1, 2, or 4 words; OOD splits use 3, 5, 7, or 10 words
+  - count - train split uses 1, 2, or 4 constituents; OOD splits use 3, 5, 7, or 10 constituents
+### Source Data
+[N/A]
+#### Initial Data Collection and Normalization
+[N/A]
+#### Who are the source language producers?
+The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.
+### Annotations
+Besides the source and target strings, each sample contains an annotation string that describes the sample.
+#### Annotation process
+The annotation columns were generated from each sample template.
+#### Who are the annotators?
+[N/A]
+### Personal and Sensitive Information
+No names or other sensitive information are included in the data.
+## Considerations for Using the Data
+### Social Impact of Dataset
+The purpose of this dataset is to research how LLM and from-scratch model can learn to solve templatic generation tasks.
+### Discussion of Biases
+[TBD]
+### Other Known Limitations
+[TBD]
+## Additional Information
+The internal name of this dataset is nc_tgt_v11.  Also see DATASET_INFO.md and GRAMMAR.md files.
+### Dataset Curators
+The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.
+### Licensing Information
+This dataset is released under the [Permissive 2.0 license](https://cdla.dev/permissive-2-0/).
+### Citation Information
+[TBD]
+### Contributions
+Thanks to [The Neurocompositional AI group at Microsoft Research](https://www.microsoft.com/en-us/research/project/neurocompositional-ai/) for creating and adding this dataset.