Anon126/my-raft-submission

---
benchmark: raft
type: prediction
submission_name: none
---

# 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

RAFT 提交数据集

提交规则

1. 每周仅评估一次提交，以防止过度拟合公共排行榜。
2. 允许使用其他数据集进行迁移学习或元学习，包括在其他语料库上进一步预训练。
3. 允许使用未标记的测试数据，例如，使用任务的未标记数据进行进一步预训练是允许的。
4. 允许系统通过自动网络搜索等方式增强信息。

提交文件格式

对于RAFT中的每个任务，应创建一个名为predictions.csv的CSV文件，包含模型在未标记测试集上的预测。每个文件应包含以下两列：

• ID（整数）
• Label（字符串）

示例代码如下：

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: # 加载数据集 raft_subset = load_dataset("ought/raft", task) # 计算训练集上的多数类 counter = Counter(raft_subset["train"]["Label"]) majority_class = counter.most_common(1)[0][0] # 加载预测文件 preds = pd.read_csv(f"data/{task}/predictions.csv") # 将标签ID转换为标签名称 preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class) # 保存预测 preds.to_csv(f"data/{task}/predictions.csv", index=False)

每个predictions.csv文件应存储在任务的子文件夹中，最终结构如下：

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

验证提交

运行以下命令验证提交文件格式是否正确：

python cli.py validate

如果格式正确，将显示以下消息：

All submission files validated! ✨ 🚀 ✨ Now you can make a submission 🤗

提交到Hugging Face Hub

最后一步是提交文件到Hub：

python cli.py submit

如果没有错误，将显示以下消息：

Submission successful! 🎉 🥳 🎉 Your submission will be evaluated on Sunday 05 September 2021 ⏳

评估将在每周日进行，结果将在排行榜上可见。