Linuxdex/my-raft-submission

---
benchmark: raft
type: prediction
submission_name: AG-tt
---

# RAFT submissions for my-raft-submission

## 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. Here is a simple example that creates a majority-class baseline:

```python
from pathlib import Path
import pandas as pd
from collections import Counter
from datasets import load_dataset, get_dataset_config_names

tasks = get_dataset_config_names("ought/raft")

for task in tasks:
# Load dataset
raft_subset = load_dataset("ought/raft", task)
# Compute majority class over training set
counter = Counter(raft_subset["train"]["Label"])
majority_class = counter.most_common(1)[0][0]
# Load predictions file
preds = pd.read_csv(f"data/{task}/predictions.csv")
# Convert label IDs to label names
preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
# Save predictions
preds.to_csv(f"data/{task}/predictions.csv", index=False)
```

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:

```
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.

原始信息汇总

数据集概述

数据集类型

• 类型：预测

提交信息

• 提交名称：AG-tt

数据集结构

• 每个任务对应一个CSV文件，命名为predictions.csv。
• 文件包含两列：ID（整数类型）和Label（字符串类型）。
• 每个CSV文件存储在对应任务的子文件夹中，子文件夹位于data目录下。

提交规则

1. 每周仅评估一次提交。
2. 允许使用其他数据集进行迁移或元学习。
3. 允许使用未标记的测试数据。
4. 允许系统通过自动化网络搜索等方式获取互联网信息。

提交流程

1. 在每个任务的未标记测试集上生成预测。
2. 验证预测与评估框架的兼容性。
3. 将预测推送到Hugging Face Hub。

验证与提交

• 使用命令python cli.py validate验证提交文件格式。
• 使用命令python cli.py submit提交文件至Hugging Face Hub。