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--- |
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benchmark: raft |
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type: prediction |
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submission_name: none |
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--- |
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# RAFT submissions for {{cookiecutter.repo_name}} |
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## Submitting to the leaderboard |
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To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps: |
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1. Generate predictions on the unlabeled test set of each task |
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2. Validate the predictions are compatible with the evaluation framework |
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3. Push the predictions to the Hub! |
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See the instructions below for more details. |
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### Rules |
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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. |
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2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed. |
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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. |
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4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches. |
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### Submission file format |
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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: |
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* ID (int) |
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* Label (string) |
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See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline: |
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```python |
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from pathlib import Path |
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import pandas as pd |
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from collections import Counter |
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from datasets import load_dataset, get_dataset_config_names |
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tasks = get_dataset_config_names("ought/raft") |
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for task in tasks: |
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# Load dataset |
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raft_subset = load_dataset("ought/raft", task) |
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# Compute majority class over training set |
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counter = Counter(raft_subset["train"]["Label"]) |
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majority_class = counter.most_common(1)[0][0] |
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# Load predictions file |
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preds = pd.read_csv(f"data/{task}/predictions.csv") |
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# Convert label IDs to label names |
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preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class) |
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# Save predictions |
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preds.to_csv(f"data/{task}/predictions.csv", index=False) |
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``` |
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As you can see in the example, 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: |
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``` |
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data |
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├── ade_corpus_v2 |
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│ ├── predictions.csv |
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│ └── task.json |
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├── banking_77 |
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│ ├── predictions.csv |
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│ └── task.json |
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├── neurips_impact_statement_risks |
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│ ├── predictions.csv |
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│ └── task.json |
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├── one_stop_english |
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│ ├── predictions.csv |
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│ └── task.json |
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├── overruling |
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│ ├── predictions.csv |
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│ └── task.json |
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├── semiconductor_org_types |
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│ ├── predictions.csv |
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│ └── task.json |
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├── systematic_review_inclusion |
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│ ├── predictions.csv |
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│ └── task.json |
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├── tai_safety_research |
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│ ├── predictions.csv |
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│ └── task.json |
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├── terms_of_service |
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│ ├── predictions.csv |
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│ └── task.json |
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├── tweet_eval_hate |
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│ ├── predictions.csv |
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│ └── task.json |
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└── twitter_complaints |
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├── predictions.csv |
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└── task.json |
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``` |
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### Validate your submission |
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To ensure that your submission files are correctly formatted, run the following command from the root of the repository: |
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``` |
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python cli.py validate |
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``` |
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If everything is correct, you should see the following message: |
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``` |
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All submission files validated! ✨ 🚀 ✨ |
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Now you can make a submission 🤗 |
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``` |
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### Push your submission to the Hugging Face Hub! |
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The final step is to commit your files and push them to the Hub: |
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``` |
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python cli.py submit |
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``` |
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If there are no errors, you should see the following message: |
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``` |
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Submission successful! 🎉 🥳 🎉 |
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Your submission will be evaulated on Sunday 05 September 2021 ⏳ |
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``` |
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where the evaluation is run every Sunday and your results will be visible on the leaderboard. |
