Refactor submission instructions

LICENSE

@@ -0,0 +1,201 @@
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README.md

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+# RAFT Submission Template
+
+Welcome to the RAFT benchmark! RAFT is a few-shot classification benchmark that tests language models:
+
+- across multiple domains (lit review, tweets, customer interaction, etc.)
+- on economically valuable classification tasks (someone inherently cares about the task)
+- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)
+
+This repository can be used to generate a template so you can submit your predictions for evaluation on [the leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard).
+
+## Quickstart
+
+### 1. Create an account and repository on the Hugging Face Hub
+
+First create an account on the Hugging Face Hub and you can sign up [here](https://huggingface.co/join) if you haven't already!
+
+### 2. Create a template repository on your machine
+
+The next step is to create a template repository on your local machine that contains various files and a CLI to help you validate and submit your predictions. You can run the following commands to create the repository:
+
+```bash
+# Install the following libraries if you don't have them
+pip install cookiecutter huggingface-hub==0.0.16
+# Create the template repository
+cookiecutter https://github.com/oughtinc/raft_submission.git
+```
+
+This will ask you to specify your Hugging Face Hub username, password, and the name of the repository:
+
+```
+hf_hub_username [<huggingface>]:
+hf_hub_password [<password>]:
+repo_name [<my-raft-submissions>]:
+```
+
+This will trigger the following steps:
+
+1. Create a private dataset repository on the Hugging Face Hub under `{hf_hub_username}/{repo_name}`
+2. Clone the repository to your local machine
+3. Add various template files and commit them locally to the repository
+
+The resulting repository should have the following structure:
+
+```
+my-raft-submission
+├── LICENSE
+├── README.md                           <- The top-level README with submission instructions
+├── cli.py                              <- The CLI for installing dependencies, validating predictions, and making submissions
+├── data
+│   ├── ade_corpus_v2                   <- The predictions for each task are stored in their corresponding directory
+│   │   ├── predictions.csv             <- A CSV file of the predictions with `ID` and `Label` columns
+│   │   └── task.json                   <- Configuration file for loading the predictions. Do not edit!
+│   ├── banking_77
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── neurips_impact_statement_risks
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── one_stop_english
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── overruling
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── semiconductor_org_types
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── systematic_review_inclusion
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── tai_safety_research
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── terms_of_service
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   ├── tweet_eval_hate
+│   │   ├── predictions.csv
+│   │   └── task.json
+│   └── twitter_complaints
+│       ├── predictions.csv
+│       └── task.json
+├── my-raft-submission.py               <- Script to load predictions during evaluation. Do not edit!
+└── requirements.txt                    <- The requirements file for the submissions
+```
+
+### 3. Install the dependencies
+
+The final step is to install the project's dependencies. We recommend creating a Python virtual environment for the project, e.g. with Anaconda:
+
+```bash
+# Navigate to the template repository
+cd my-raft-submissions
+# Create and activate a virtual environment
+conda create -n raft python=3.8 && conda activate raft
+# Install dependencies
+python cli.py install
+```
+
+That's it! You're now all set to start generating predictions - see the instructions below on how to submit them to the Hub.
+
+## Submitting to the leaderboard
+
+To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
+
+1. Generate predictions on the unlabeled test set of each task
+2. Validate the predictions are compatible with the evaluation framework
+3. Push the predictions to the Hub!
+
+See the instructions below for more details.
+
+### Rules
+
+1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week.
+2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
+3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
+4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
+
+
+### Submission file format
+
+For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
+
+* ID (int)
+* Label (string)
+
+See the dummy predictions in the `data` folder for examples with the expected format. Each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
+
+```
+data
+├── ade_corpus_v2
+│   ├── predictions.csv
+│   └── task.json
+├── banking_77
+│   ├── predictions.csv
+│   └── task.json
+├── neurips_impact_statement_risks
+│   ├── predictions.csv
+│   └── task.json
+├── one_stop_english
+│   ├── predictions.csv
+│   └── task.json
+├── overruling
+│   ├── predictions.csv
+│   └── task.json
+├── semiconductor_org_types
+│   ├── predictions.csv
+│   └── task.json
+├── systematic_review_inclusion
+│   ├── predictions.csv
+│   └── task.json
+├── tai_safety_research
+│   ├── predictions.csv
+│   └── task.json
+├── terms_of_service
+│   ├── predictions.csv
+│   └── task.json
+├── tweet_eval_hate
+│   ├── predictions.csv
+│   └── task.json
+└── twitter_complaints
+    ├── predictions.csv
+    └── task.json
+```
+
+### Validate your submission
+
+To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
+
+```
+python cli.py validate
+```
+
+If everything is correct, you should see the following message:
+
+```
+All submission files validated! ✨ 🚀 ✨
+Now you can make a submission 🤗
+```
+
+### Push your submission to the Hugging Face Hub!
+
+The final step is to commit your files and push them to the Hub:
+
+```
+python cli.py submit
+```
+
+If there are no errors, you should see the following message:
+
+```
+Submission successful! 🎉 🥳 🎉
+Your submission will be evaulated on Sunday 05 September 2021 ⏳
+```
+
+where the evaluation is run every Sunday and your results will be visible on the leaderboard.

cookiecutter.json

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+{
+    "hf_hub_username": "huggingface",
+    "hf_hub_password": "password",
+    "repo_name": "my-raft-submission"
+}

hooks/post_gen_project.py

@@ -0,0 +1,5 @@
+import subprocess
+
+subprocess.call("git pull origin main".split())
+subprocess.call(["git", "add", "."])
+subprocess.call(["git", "commit", "-m", "Add template files"])

hooks/pre_gen_project.py

@@ -0,0 +1,16 @@
+from huggingface_hub import HfApi, HfFolder, Repository
+
+hf_user = "{{cookiecutter.hf_hub_username}}"
+hf_password = "{{cookiecutter.hf_hub_password}}"
+huggingface_token = HfApi().login(username=hf_user, password=hf_password)
+HfFolder.save_token(huggingface_token)
+
+repo_url = HfApi().create_repo(
+    token=huggingface_token,
+    name="{{cookiecutter.repo_name}}",
+    organization=hf_user,
+    exist_ok=True,
+    private=True,
+)
+
+model_repo = Repository(local_dir=".", clone_from=repo_url, use_auth_token=huggingface_token)

requirements.txt

@@ -0,0 +1,4 @@
+datasets>=1.11.0
+typer
+cookiecutter
+huggingface-hub==0.0.16

validation.py

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+from pathlib import Path
+
+from datasets import load_dataset
+
+
+def main():
+    tasks = ["safety_or_not"]
+
+    # check that all the expected files exist
+    prediction_files = list(Path(".").glob("*.csv"))
+    mismatched_files = set(tasks).symmetric_difference(set([f.stem for f in prediction_files]))
+    if mismatched_files:
+        raise ValueError(f"Incorrect number of files! Expected {len(tasks)} files, but got {len(prediction_files)}.")
+
+    # check we can load the dataset for each task
+    load_errors = []
+    for task in tasks:
+        try:
+            dset = load_dataset("../raft_submission", task)
+        except Exception as e:
+            load_errors.append(e)
+
+    if load_errors:
+        raise ValueError(f"Could not load predictions! Errors: {load_errors}")
+
+
+if __name__ == "__main__":
+    main()

{{cookiecutter.repo_name}}/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
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+      including but not limited to software source code, documentation
+      source, and configuration files.
+
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+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
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+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
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+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
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+      communication on electronic mailing lists, source code control systems,
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+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
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+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
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+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
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+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
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+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
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+          within a display generated by the Derivative Works, if and
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+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
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+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
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+      origin of the Work and reproducing the content of the NOTICE file.
+
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+      agreed to in writing, Licensor provides the Work (and each
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+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

{{cookiecutter.repo_name}}/README.md

@@ -0,0 +1,94 @@
+---
+benchmark: raft
+type: prediction
+---
+
+# RAFT submissions for {{cookiecutter.repo_name}}
+
+## Submitting to the leaderboard
+
+To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
+
+1. Generate predictions on the unlabeled test set of each task
+2. Validate the predictions are compatible with the evaluation framework
+3. Push the predictions to the Hub!
+
+See the instructions below for more details.
+
+### Rules
+
+1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. 
+2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
+3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
+4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
+
+### Submission file format
+
+For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
+
+* ID (int)
+* Label (string)
+
+See the dummy predictions in the `data` folder for examples with the expected format. Each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
+
+```
+data
+├── ade_corpus_v2
+│   ├── predictions.csv
+│   └── task.json
+├── banking_77
+│   ├── predictions.csv
+│   └── task.json
+├── neurips_impact_statement_risks
+│   ├── predictions.csv
+│   └── task.json
+├── one_stop_english
+│   ├── predictions.csv
+│   └── task.json
+├── overruling
+│   ├── predictions.csv
+│   └── task.json
+├── semiconductor_org_types
+│   ├── predictions.csv
+│   └── task.json
+├── systematic_review_inclusion
+│   ├── predictions.csv
+│   └── task.json
+├── tai_safety_research
+│   ├── predictions.csv
+│   └── task.json
+├── terms_of_service
+│   ├── predictions.csv
+│   └── task.json
+├── tweet_eval_hate
+│   ├── predictions.csv
+│   └── task.json
+└── twitter_complaints
+    ├── predictions.csv
+    └── task.json
+```
+
+### Validate your submission
+
+To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
+
+```
+python cli.py validate
+```
+
+If everything is correct, you should see the following message:
+
+```
+All submission files validated! ✨ 🚀 ✨
+Now you can make a submission 🤗
+```
+
+### Push your submission to the Hugging Face Hub!
+
+The final step is to commit your files and push them to the Hub:
+
+```bash
+git add .
+git commit -m "Some commit message"
+git push -f origin HEAD:main
+```

{{cookiecutter.repo_name}}/cli.py

@@ -0,0 +1,115 @@
+import datetime
+import subprocess
+from pathlib import Path
+
+import pandas as pd
+import typer
+from click.utils import echo
+from datasets import get_dataset_config_names, load_dataset
+
+CSV_SCHEMA = {
+    "banking_77": (5000, 2),
+    "overruling": (2350, 2),
+    "semiconductor_org_types": (449, 2),
+    "ade_corpus_v2": (5000, 2),
+    "twitter_complaints": (3399, 2),
+    "neurips_impact_statement_risks": (150, 2),
+    "systematic_review_inclusion": (2244, 2),
+    "terms_of_service": (5000, 2),
+    "tai_safety_research": (1639, 2),
+    "one_stop_english": (518, 2),
+    "tweet_eval_hate": (2966, 2),
+}
+
+app = typer.Typer()
+
+
+@app.command()
+def install():
+    typer.echo("Installing dependencies ...")
+    try:
+        p = subprocess.run(
+            "pip install --upgrade pip".split(),
+            stderr=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            check=True,
+            encoding="utf-8",
+        )
+    except subprocess.CalledProcessError as exc:
+        raise EnvironmentError(exc.stderr)
+
+    try:
+        p = subprocess.run(
+            "pip install --upgrade -r requirements.txt".split(),
+            stderr=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            check=True,
+            encoding="utf-8",
+        )
+    except subprocess.CalledProcessError as exc:
+        raise EnvironmentError(exc.stderr)
+
+    typer.echo("Success!")
+
+
+@app.command()
+def validate():
+    tasks = get_dataset_config_names("ought/raft")
+
+    # Check that all the expected files exist
+    prediction_files = list(Path("data").rglob("*.csv"))
+    mismatched_files = set(tasks).symmetric_difference(set([f.parent.name for f in prediction_files]))
+    if mismatched_files:
+        raise ValueError(f"Incorrect number of files! Expected {len(tasks)} files, but got {len(prediction_files)}.")
+
+    # Check all files have the expected shape (number of rows, number of columns)
+    # TODO(lewtun): Add a check for the IDs per file
+    shape_errors = []
+    column_errors = []
+    for prediction_file in prediction_files:
+        df = pd.read_csv(prediction_file)
+        incorrect_shape = df.shape != CSV_SCHEMA[prediction_file.parent.name]
+        if incorrect_shape:
+            shape_errors.append(prediction_file)
+        incorrect_columns = sorted(df.columns) != ["ID", "Label"]
+        if incorrect_columns:
+            column_errors.append(prediction_file)
+
+    if shape_errors:
+        raise ValueError(f"Incorrect CSV shapes in files: {shape_errors}")
+
+    if column_errors:
+        raise ValueError(f"Incorrect CSV columns in files: {column_errors}")
+
+    # Check we can load the dataset for each task
+    load_errors = []
+    for task in tasks:
+        try:
+            _ = load_dataset("../{{cookiecutter.repo_name}}", task)
+        except Exception as e:
+            load_errors.append(e)
+
+    if load_errors:
+        raise ValueError(f"Could not load predictions! Errors: {load_errors}")
+
+    typer.echo("All submission files validated! ✨ 🚀 ✨")
+    typer.echo("Now you can make a submission 🤗")
+
+
+@app.command()
+def submit():
+    subprocess.call("git pull origin main".split())
+    subprocess.call(["git", "add", "."])
+    subprocess.call(["git", "commit", "-m", "Submission"])
+    subprocess.call(["git", "push"])
+
+    today = datetime.date.today()
+    # MON = 0, SUN = 6 -> SUN = 0 .. SAT = 6
+    idx = (today.weekday() + 1) % 7
+    sun = today + datetime.timedelta(7 - idx)
+    typer.echo("Submission successful! 🎉 🥳 🎉")
+    typer.echo(f"Your submission will be evaulated on {sun:%A %d %B %Y} ⏳")
+
+
+if __name__ == "__main__":
+    app()

{{cookiecutter.repo_name}}/data/ade_corpus_v2/task.json

@@ -0,0 +1 @@
+{"name": "ade_corpus_v2", "description": "", "data_columns": ["Sentence", "ID"], "label_columns": {"Label": ["ADE-related", "not ADE-related"]}}

{{cookiecutter.repo_name}}/data/banking_77/task.json

@@ -0,0 +1 @@
+{"name": "banking_77", "description": "", "data_columns": ["Query", "ID"], "label_columns": {"Label": ["Refund_not_showing_up", "activate_my_card", "age_limit", "apple_pay_or_google_pay", "atm_support", "automatic_top_up", "balance_not_updated_after_bank_transfer", "balance_not_updated_after_cheque_or_cash_deposit", "beneficiary_not_allowed", "cancel_transfer", "card_about_to_expire", "card_acceptance", "card_arrival", "card_delivery_estimate", "card_linking", "card_not_working", "card_payment_fee_charged", "card_payment_not_recognised", "card_payment_wrong_exchange_rate", "card_swallowed", "cash_withdrawal_charge", "cash_withdrawal_not_recognised", "change_pin", "compromised_card", "contactless_not_working", "country_support", "declined_card_payment", "declined_cash_withdrawal", "declined_transfer", "direct_debit_payment_not_recognised", "disposable_card_limits", "edit_personal_details", "exchange_charge", "exchange_rate", "exchange_via_app", "extra_charge_on_statement", "failed_transfer", "fiat_currency_support", "get_disposable_virtual_card", "get_physical_card", "getting_spare_card", "getting_virtual_card", "lost_or_stolen_card", "lost_or_stolen_phone", "order_physical_card", "passcode_forgotten", "pending_card_payment", "pending_cash_withdrawal", "pending_top_up", "pending_transfer", "pin_blocked", "receiving_money", "request_refund", "reverted_card_payment?", "supported_cards_and_currencies", "terminate_account", "top_up_by_bank_transfer_charge", "top_up_by_card_charge", "top_up_by_cash_or_cheque", "top_up_failed", "top_up_limits", "top_up_reverted", "topping_up_by_card", "transaction_charged_twice", "transfer_fee_charged", "transfer_into_account", "transfer_not_received_by_recipient", "transfer_timing", "unable_to_verify_identity", "verify_my_identity", "verify_source_of_funds", "verify_top_up", "virtual_card_not_working", "visa_or_mastercard", "why_verify_identity", "wrong_amount_of_cash_received", "wrong_exchange_rate_for_cash_withdrawal"]}}

{{cookiecutter.repo_name}}/data/neurips_impact_statement_risks/predictions.csv

@@ -0,0 +1,151 @@
+ID,Label
+50,doesn't mention a harmful application
+51,mentions a harmful application
+52,doesn't mention a harmful application
+53,mentions a harmful application
+54,mentions a harmful application
+55,doesn't mention a harmful application
+56,doesn't mention a harmful application
+57,doesn't mention a harmful application
+58,doesn't mention a harmful application
+59,mentions a harmful application
+60,mentions a harmful application
+61,doesn't mention a harmful application
+62,doesn't mention a harmful application
+63,mentions a harmful application
+64,mentions a harmful application
+65,doesn't mention a harmful application
+66,doesn't mention a harmful application
+67,mentions a harmful application
+68,doesn't mention a harmful application
+69,doesn't mention a harmful application
+70,mentions a harmful application
+71,mentions a harmful application
+72,doesn't mention a harmful application
+73,doesn't mention a harmful application
+74,mentions a harmful application
+75,mentions a harmful application
+76,mentions a harmful application
+77,doesn't mention a harmful application
+78,mentions a harmful application
+79,mentions a harmful application
+80,doesn't mention a harmful application
+81,doesn't mention a harmful application
+82,doesn't mention a harmful application
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+85,doesn't mention a harmful application
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+104,mentions a harmful application
+105,mentions a harmful application
+106,mentions a harmful application
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+114,doesn't mention a harmful application
+115,mentions a harmful application
+116,mentions a harmful application
+117,mentions a harmful application
+118,doesn't mention a harmful application
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+120,doesn't mention a harmful application
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+123,mentions a harmful application
+124,doesn't mention a harmful application
+125,doesn't mention a harmful application
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+127,doesn't mention a harmful application
+128,mentions a harmful application
+129,mentions a harmful application
+130,doesn't mention a harmful application
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+133,doesn't mention a harmful application
+134,mentions a harmful application
+135,mentions a harmful application
+136,doesn't mention a harmful application
+137,mentions a harmful application
+138,mentions a harmful application
+139,mentions a harmful application
+140,doesn't mention a harmful application
+141,mentions a harmful application
+142,doesn't mention a harmful application
+143,mentions a harmful application
+144,doesn't mention a harmful application
+145,mentions a harmful application
+146,mentions a harmful application
+147,doesn't mention a harmful application
+148,mentions a harmful application
+149,mentions a harmful application
+150,doesn't mention a harmful application
+151,mentions a harmful application
+152,doesn't mention a harmful application
+153,mentions a harmful application
+154,doesn't mention a harmful application
+155,mentions a harmful application
+156,doesn't mention a harmful application
+157,mentions a harmful application
+158,mentions a harmful application
+159,mentions a harmful application
+160,doesn't mention a harmful application
+161,mentions a harmful application
+162,doesn't mention a harmful application
+163,mentions a harmful application
+164,doesn't mention a harmful application
+165,doesn't mention a harmful application
+166,doesn't mention a harmful application
+167,doesn't mention a harmful application
+168,doesn't mention a harmful application
+169,doesn't mention a harmful application
+170,doesn't mention a harmful application
+171,doesn't mention a harmful application
+172,mentions a harmful application
+173,doesn't mention a harmful application
+174,mentions a harmful application
+175,doesn't mention a harmful application
+176,doesn't mention a harmful application
+177,mentions a harmful application
+178,mentions a harmful application
+179,doesn't mention a harmful application
+180,mentions a harmful application
+181,doesn't mention a harmful application
+182,doesn't mention a harmful application
+183,mentions a harmful application
+184,mentions a harmful application
+185,mentions a harmful application
+186,doesn't mention a harmful application
+187,doesn't mention a harmful application
+188,mentions a harmful application
+189,mentions a harmful application
+190,doesn't mention a harmful application
+191,mentions a harmful application
+192,doesn't mention a harmful application
+193,mentions a harmful application
+194,doesn't mention a harmful application
+195,doesn't mention a harmful application
+196,doesn't mention a harmful application
+197,mentions a harmful application
+198,mentions a harmful application
+199,doesn't mention a harmful application

{{cookiecutter.repo_name}}/data/neurips_impact_statement_risks/task.json

@@ -0,0 +1 @@
+{"name": "neurips_impact_statement_risks", "description": "", "data_columns": ["Paper title", "Paper link", "Impact statement", "ID"], "label_columns": {"Label": ["doesn't mention a harmful application", "mentions a harmful application"]}}

{{cookiecutter.repo_name}}/data/one_stop_english/predictions.csv

@@ -0,0 +1,519 @@
+ID,Label
+50,advanced
+51,elementary
+52,advanced
+53,intermediate
+54,elementary
+55,elementary
+56,advanced
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+564,advanced
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+566,elementary
+567,intermediate

{{cookiecutter.repo_name}}/data/one_stop_english/task.json

@@ -0,0 +1 @@
+{"name": "one_stop_english", "description": "", "data_columns": ["Article", "ID"], "label_columns": {"Label": ["advanced", "elementary", "intermediate"]}}

{{cookiecutter.repo_name}}/data/overruling/predictions.csv

@@ -0,0 +1,2351 @@
+ID,Label
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+904,not overruling
+905,overruling
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+907,overruling
+908,overruling
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+914,overruling
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+962,not overruling
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+964,overruling
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+1028,overruling
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+1075,overruling
+1076,not overruling
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+1197,overruling
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+1200,overruling
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+1206,overruling
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+1346,overruling
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{{cookiecutter.repo_name}}/data/overruling/task.json

@@ -0,0 +1 @@
+{"name": "overruling", "description": "", "data_columns": ["Sentence", "ID"], "label_columns": {"Label": ["not overruling", "overruling"]}}

{{cookiecutter.repo_name}}/data/semiconductor_org_types/predictions.csv

@@ -0,0 +1,450 @@
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{{cookiecutter.repo_name}}/data/semiconductor_org_types/task.json

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{{cookiecutter.repo_name}}/data/systematic_review_inclusion/task.json

@@ -0,0 +1 @@
+{"name": "systematic_review_inclusion", "description": "", "data_columns": ["Title", "Abstract", "Authors", "Journal", "ID"], "label_columns": {"Label": ["included", "not included"]}}

{{cookiecutter.repo_name}}/data/tai_safety_research/predictions.csv

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{{cookiecutter.repo_name}}/data/tai_safety_research/task.json

@@ -0,0 +1 @@
+{"name": "tai_safety_research", "description": "", "data_columns": ["Title", "Abstract Note", "Url", "Publication Year", "Item Type", "Author", "Publication Title", "ID"], "label_columns": {"Label": ["TAI safety research", "not TAI safety research"]}}

{{cookiecutter.repo_name}}/data/terms_of_service/task.json

@@ -0,0 +1 @@
+{"name": "terms_of_service", "description": "", "data_columns": ["Sentence", "ID"], "label_columns": {"Label": ["not potentially unfair", "potentially unfair"]}}

{{cookiecutter.repo_name}}/data/tweet_eval_hate/predictions.csv

@@ -0,0 +1,2967 @@
+ID,Label
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{{cookiecutter.repo_name}}/data/tweet_eval_hate/task.json

@@ -0,0 +1 @@
+{"name": "tweet_eval_hate", "description": "", "data_columns": ["Tweet", "ID"], "label_columns": {"Label": ["hate speech", "not hate speech"]}}

{{cookiecutter.repo_name}}/data/twitter_complaints/predictions.csv

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{{cookiecutter.repo_name}}/data/twitter_complaints/task.json

@@ -0,0 +1 @@
+{"name": "twitter_complaints", "description": "", "data_columns": ["Tweet text", "ID"], "label_columns": {"Label": ["complaint", "no complaint"]}}

{{cookiecutter.repo_name}}/requirements.txt

@@ -0,0 +1,2 @@
+datasets>=1.11.0
+typer

{{cookiecutter.repo_name}}/{{cookiecutter.repo_name}}.py

@@ -0,0 +1,304 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import csv
+
+import datasets
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+# This gets all folders within the directory named `data`
+# DATA_DIRS = next(os.walk('data'))[1]
+DATA_DIR_URL = "data/"  # "https://huggingface.co/datasets/ought/raft/resolve/main/data/"
+# print([p for p in DATA_DIR_PATH.iterdir() if p.is_dir()])
+TASKS = {
+    "ade_corpus_v2": {
+        "name": "ade_corpus_v2",
+        "description": "",
+        "data_columns": ["Sentence", "ID"],
+        "label_columns": {"Label": ["ADE-related", "not ADE-related"]},
+    },
+    "banking_77": {
+        "name": "banking_77",
+        "description": "",
+        "data_columns": ["Query", "ID"],
+        "label_columns": {
+            "Label": [
+                "Refund_not_showing_up",
+                "activate_my_card",
+                "age_limit",
+                "apple_pay_or_google_pay",
+                "atm_support",
+                "automatic_top_up",
+                "balance_not_updated_after_bank_transfer",
+                "balance_not_updated_after_cheque_or_cash_deposit",
+                "beneficiary_not_allowed",
+                "cancel_transfer",
+                "card_about_to_expire",
+                "card_acceptance",
+                "card_arrival",
+                "card_delivery_estimate",
+                "card_linking",
+                "card_not_working",
+                "card_payment_fee_charged",
+                "card_payment_not_recognised",
+                "card_payment_wrong_exchange_rate",
+                "card_swallowed",
+                "cash_withdrawal_charge",
+                "cash_withdrawal_not_recognised",
+                "change_pin",
+                "compromised_card",
+                "contactless_not_working",
+                "country_support",
+                "declined_card_payment",
+                "declined_cash_withdrawal",
+                "declined_transfer",
+                "direct_debit_payment_not_recognised",
+                "disposable_card_limits",
+                "edit_personal_details",
+                "exchange_charge",
+                "exchange_rate",
+                "exchange_via_app",
+                "extra_charge_on_statement",
+                "failed_transfer",
+                "fiat_currency_support",
+                "get_disposable_virtual_card",
+                "get_physical_card",
+                "getting_spare_card",
+                "getting_virtual_card",
+                "lost_or_stolen_card",
+                "lost_or_stolen_phone",
+                "order_physical_card",
+                "passcode_forgotten",
+                "pending_card_payment",
+                "pending_cash_withdrawal",
+                "pending_top_up",
+                "pending_transfer",
+                "pin_blocked",
+                "receiving_money",
+                "request_refund",
+                "reverted_card_payment?",
+                "supported_cards_and_currencies",
+                "terminate_account",
+                "top_up_by_bank_transfer_charge",
+                "top_up_by_card_charge",
+                "top_up_by_cash_or_cheque",
+                "top_up_failed",
+                "top_up_limits",
+                "top_up_reverted",
+                "topping_up_by_card",
+                "transaction_charged_twice",
+                "transfer_fee_charged",
+                "transfer_into_account",
+                "transfer_not_received_by_recipient",
+                "transfer_timing",
+                "unable_to_verify_identity",
+                "verify_my_identity",
+                "verify_source_of_funds",
+                "verify_top_up",
+                "virtual_card_not_working",
+                "visa_or_mastercard",
+                "why_verify_identity",
+                "wrong_amount_of_cash_received",
+                "wrong_exchange_rate_for_cash_withdrawal",
+            ]
+        },
+    },
+    "terms_of_service": {
+        "name": "terms_of_service",
+        "description": "",
+        "data_columns": ["Sentence", "ID"],
+        "label_columns": {"Label": ["not potentially unfair", "potentially unfair"]},
+    },
+    "tai_safety_research": {
+        "name": "tai_safety_research",
+        "description": "",
+        "data_columns": [
+            "Title",
+            "Abstract Note",
+            "Url",
+            "Publication Year",
+            "Item Type",
+            "Author",
+            "Publication Title",
+            "ID",
+        ],
+        "label_columns": {"Label": ["TAI safety research", "not TAI safety research"]},
+    },
+    "neurips_impact_statement_risks": {
+        "name": "neurips_impact_statement_risks",
+        "description": "",
+        "data_columns": ["Paper title", "Paper link", "Impact statement", "ID"],
+        "label_columns": {"Label": ["doesn't mention a harmful application", "mentions a harmful application"]},
+    },
+    "overruling": {
+        "name": "overruling",
+        "description": "",
+        "data_columns": ["Sentence", "ID"],
+        "label_columns": {"Label": ["not overruling", "overruling"]},
+    },
+    "systematic_review_inclusion": {
+        "name": "systematic_review_inclusion",
+        "description": "",
+        "data_columns": ["Title", "Abstract", "Authors", "Journal", "ID"],
+        "label_columns": {"Label": ["included", "not included"]},
+    },
+    "one_stop_english": {
+        "name": "one_stop_english",
+        "description": "",
+        "data_columns": ["Article", "ID"],
+        "label_columns": {"Label": ["advanced", "elementary", "intermediate"]},
+    },
+    "tweet_eval_hate": {
+        "name": "tweet_eval_hate",
+        "description": "",
+        "data_columns": ["Tweet", "ID"],
+        "label_columns": {"Label": ["hate speech", "not hate speech"]},
+    },
+    "twitter_complaints": {
+        "name": "twitter_complaints",
+        "description": "",
+        "data_columns": ["Tweet text", "ID"],
+        "label_columns": {"Label": ["complaint", "no complaint"]},
+    },
+    "semiconductor_org_types": {
+        "name": "semiconductor_org_types",
+        "description": "",
+        "data_columns": ["Paper title", "Organization name", "ID"],
+        "label_columns": {"Label": ["company", "research institute", "university"]},
+    },
+}
+
+_URLs = {s: {"test": f"{DATA_DIR_URL}{s}/predictions.csv"} for s in TASKS}
+
+
+class RaftSubmission(datasets.GeneratorBasedBuilder):
+    """RAFT Dataset dummy predictions."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+
+    # TODO: Load task jsons
+
+    # tasks = {}
+    # for sd in DATA_DIRS:
+    #     with open(os.path.join('data', sd, 'task.json')) as f:
+    #         task_data = json.load(f)
+    #     tasks[sd] = task_data
+
+    BUILDER_CONFIGS = []
+    for key in TASKS:
+        td = TASKS[key]
+        name = td["name"]
+        description = td["description"]
+        BUILDER_CONFIGS.append(datasets.BuilderConfig(name=name, version=VERSION, description=description))
+
+    DEFAULT_CONFIG_NAME = (
+        "tai_safety_research"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    )
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        DEFAULT_LABEL_NAME = "Unlabeled"
+
+        task = TASKS[self.config.name]
+        data_columns = {"ID": datasets.Value("string")}
+
+        label_columns = {}
+        for label_name in task["label_columns"]:
+            labels = [DEFAULT_LABEL_NAME] + task["label_columns"][label_name]
+            label_columns[label_name] = datasets.ClassLabel(len(labels), labels)
+
+        # Merge dicts
+        features = datasets.Features(**data_columns, **label_columns)
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_dir = dl_manager.download_and_extract(_URLs)
+        dataset = self.config.name
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir[dataset]["test"], "split": "test"}
+            )
+        ]
+
+    def _generate_examples(
+        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        with open(filepath, encoding="utf-8") as f:
+            csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
+            column_names = next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                # dicts don't have inherent ordering in python, right??
+                yield id_, {name: value for name, value in zip(column_names, row)}