|
--- |
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dataset_info: |
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features: |
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- name: label |
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dtype: string |
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- name: identifier |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 71882488 |
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num_examples: 82603 |
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- name: test |
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num_bytes: 405966 |
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num_examples: 1219 |
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download_size: 31200331 |
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dataset_size: 72288454 |
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source_datasets: |
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- Tonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent |
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- Tonic/climate-guard-synthetic_data_qwen_toxic_agent |
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- Tonic/climate-guard-thinking_data_nocomment_intern_toxic_agent |
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- Tonic/climate-guard-thinking_data_nocomment_phi4_toxic_agent |
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- Tonic/climate-guard-thinking_data_nocomment_yi_toxic_agent |
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- Tonic/climate-guard-synthetic_data_nocomment_yi_toxic_agent |
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- climate_fever |
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- QuotaClimat/frugalaichallenge-text-train |
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- takara-ai/QuotaClimat |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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license: mit |
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task_categories: |
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- text-classification |
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- zero-shot-classification |
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language: |
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- en |
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tags: |
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- climate |
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pretty_name: Climate Guard Toxic Agent |
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size_categories: |
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- 10K<n<100K |
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--- |
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|
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# Climate Guard - Toxic Agent - Dataset |
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|
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## Dataset Description |
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This dataset is a comprehensive consolidation of multiple climate-related datasets, focusing on climate disinformation and factual climate information. It combines and standardizes data from various high-quality sources to create a robust resource for climate-related text classification tasks. |
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|
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### Dataset Sources |
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The dataset incorporates data from the following sources: |
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- Tonic Climate Guard Series: |
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- [climate-guard-thinking_data_nocomment_qwen_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-thinking_data_nocomment_qwen_toxic_agent) |
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- [climate-guard-synthetic_data_qwen_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-synthetic_data_qwen_toxic_agent) |
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- [climate-guard-thinking_data_nocomment_intern_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-thinking_data_nocomment_intern_toxic_agent) |
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- [climate-guard-thinking_data_nocomment_phi4_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-thinking_data_nocomment_phi4_toxic_agent) |
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- [climate-guard-thinking_data_nocomment_yi_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-thinking_data_nocomment_yi_toxic_agent) |
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- [climate-guard-synthetic_data_yi_toxic_agent](https://huggingface.co/datasets/Tonic/climate-guard-synthetic_data_yi_toxic_agent) |
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- [QuotaClimat/frugalaichallenge-text-train](https://huggingface.co/datasets/QuotaClimat/frugalaichallenge-text-train) |
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- [Climate FEVER dataset](https://huggingface.co/datasets/climate_fever) |
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- [Quota Climat dataset (12% random sample)](https://huggingface.co/datasets/takara-ai/QuotaClimat) |
|
|
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<details> |
|
<summary><h3>Data Processing - 👇🏻Click to expand</h3></summary> |
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The dataset underwent several processing steps to ensure quality and consistency: |
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|
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1. **Text Cleaning**: |
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- Removed responses starting with apology phrases ("I'm sorry", "I am sorry", "I apologize", "Given the directive") |
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- Cleaned text between "---" markers |
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- Standardized text formatting |
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|
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2. **Label Standardization**: |
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- Maintained consistent label format across all sources |
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- Special handling for '0_not_relevant' labels from specific sources |
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|
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3. **Source Tracking**: |
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- Added source identifiers to track data origin |
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- Preserved dataset provenance information |
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|
|
</details> |
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|
|
## Dataset Structure |
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|
|
The dataset is split into training and testing sets with the following features: |
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|
|
```python |
|
DatasetDict({ |
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'train': Dataset({ |
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features: ['identifier', 'text', 'label'], |
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num_examples: <num_examples> |
|
}), |
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'test': Dataset({ |
|
features: ['identifier', 'text', 'label'], |
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num_examples: <num_examples> |
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}) |
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}) |
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``` |
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|
|
### Features: |
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|
|
- **identifier**: String identifying the source dataset |
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- **text**: The main text content |
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- **label**: Classification label |
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|
|
### Labels: |
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|
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- `0_not_relevant`: No relevant claim detected or claims that don't fit other categories |
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- `1_not_happening`: Claims denying the occurrence of global warming and its effects - Global warming is not happening. Climate change is NOT leading to melting ice (such as glaciers, sea ice, and permafrost), increased extreme weather, or rising sea levels. Cold weather also shows that climate change is not happening |
|
- `2_not_human`: Claims denying human responsibility in climate change - Greenhouse gases from humans are not the causing climate change. |
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- `3_not_bad`: Claims minimizing or denying negative impacts of climate change - The impacts of climate change will not be bad and might even be beneficial. |
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- `4_solutions_harmful_unnecessary`: Claims against climate solutions - Climate solutions are harmful or unnecessary |
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- `5_science_is_unreliable`: Claims questioning climate science validity - Climate science is uncertain, unsound, unreliable, or biased. |
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- `6_proponents_biased`: Claims attacking climate scientists and activists - Climate scientists and proponents of climate action are alarmist, biased, wrong, hypocritical, corrupt, and/or politically motivated. |
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- `7_fossil_fuels_needed`: Claims promoting fossil fuel necessity - We need fossil fuels for economic growth, prosperity, and to maintain our standard of living. |
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|
|
|
|
 |
|
|
|
 |
|
|
|
 |
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|
|
|
|
## Usage |
|
|
|
```python |
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from datasets import load_dataset |
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|
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# Load the dataset |
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dataset = load_dataset("Tonic/Climate-Guard-Toxic-Agent") |
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|
|
# Access training data |
|
train_data = dataset['train'] |
|
|
|
# Access test data |
|
test_data = dataset['test'] |
|
``` |
|
|
|
<details> |
|
<summary><h3> Dataset Statistics - 👇🏻Click to expand </h3> </summary> |
|
|
|
```json |
|
{ |
|
"basic_stats": { |
|
"total_samples": { |
|
"train": 82603, |
|
"test": 1219 |
|
}, |
|
"label_distribution": { |
|
"train": { |
|
"3_not_bad": 11011, |
|
"4_solutions_harmful_unnecessary": 11597, |
|
"5_science_is_unreliable": 14609, |
|
"6_proponents_biased": 8494, |
|
"7_fossil_fuels_needed": 10585, |
|
"1_not_happening": 11380, |
|
"2_not_human": 11772, |
|
"0_not_relevant": 3155 |
|
}, |
|
"test": { |
|
"6_proponents_biased": 139, |
|
"2_not_human": 137, |
|
"3_not_bad": 97, |
|
"1_not_happening": 154, |
|
"5_science_unreliable": 160, |
|
"4_solutions_harmful_unnecessary": 160, |
|
"7_fossil_fuels_needed": 65, |
|
"0_not_relevant": 307 |
|
} |
|
}, |
|
"source_distribution": { |
|
"train": { |
|
"climate-guard-thinking_data_nocomment_qwen_toxic_agent": 1546, |
|
"climate-guard-synthetic_data_qwen_toxic_agent": 32209, |
|
"climate-guard-thinking_data_nocomment_intern_toxic_agent": 3297, |
|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": 28510, |
|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": 1687, |
|
"climate-guard-synthetic_data_yi_toxic_agent": 3789, |
|
"frugal_challenge_train": 1311, |
|
"climate_fever": 654, |
|
"quota_climat": 9600 |
|
}, |
|
"test": { |
|
"frugal_challenge_test": 1219 |
|
} |
|
}, |
|
"label_source_incidence": { |
|
"train": { |
|
"counts": { |
|
"3_not_bad": { |
|
"climate-guard-thinking_data_nocomment_qwen_toxic_agent": 207, |
|
"climate-guard-synthetic_data_qwen_toxic_agent": 4116, |
|
"climate-guard-thinking_data_nocomment_intern_toxic_agent": 478, |
|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": 4332, |
|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": 218, |
|
"climate-guard-synthetic_data_yi_toxic_agent": 496, |
|
"quota_climat": 1164 |
|
}, |
|
"4_solutions_harmful_unnecessary": { |
|
"climate-guard-thinking_data_nocomment_qwen_toxic_agent": 223, |
|
"climate-guard-synthetic_data_qwen_toxic_agent": 4760, |
|
"climate-guard-thinking_data_nocomment_intern_toxic_agent": 473, |
|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": 4112, |
|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": 236, |
|
"climate-guard-synthetic_data_yi_toxic_agent": 557, |
|
"quota_climat": 1236 |
|
}, |
|
"5_science_is_unreliable": { |
|
"climate-guard-thinking_data_nocomment_qwen_toxic_agent": 300, |
|
"climate-guard-synthetic_data_qwen_toxic_agent": 5454, |
|
"climate-guard-thinking_data_nocomment_intern_toxic_agent": 604, |
|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": 6091, |
|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": 318, |
|
"climate-guard-synthetic_data_yi_toxic_agent": 656, |
|
"quota_climat": 1186 |
|
}, |
|
"6_proponents_biased": { |
|
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|
"climate-guard-synthetic_data_qwen_toxic_agent": 4535, |
|
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|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": 1389, |
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"climate-guard-thinking_data_nocomment_yi_toxic_agent": 234, |
|
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|
"quota_climat": 1236 |
|
}, |
|
"7_fossil_fuels_needed": { |
|
"climate-guard-thinking_data_nocomment_qwen_toxic_agent": 205, |
|
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|
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"climate-guard-thinking_data_nocomment_phi4_toxic_agent": 4143, |
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"climate-guard-thinking_data_nocomment_yi_toxic_agent": 217, |
|
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|
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|
}, |
|
"1_not_happening": { |
|
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|
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|
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|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": 3976, |
|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": 236, |
|
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|
"quota_climat": 1227 |
|
}, |
|
"2_not_human": { |
|
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|
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|
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|
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|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": 228, |
|
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|
"quota_climat": 1220 |
|
}, |
|
"0_not_relevant": { |
|
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|
"climate_fever": 654, |
|
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|
} |
|
}, |
|
"percentages": { |
|
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|
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|
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|
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|
}, |
|
"4_solutions_harmful_unnecessary": { |
|
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|
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|
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"climate-guard-thinking_data_nocomment_yi_toxic_agent": 2.0350090540657066, |
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|
"quota_climat": 10.657928774683107 |
|
}, |
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
} |
|
} |
|
}, |
|
"test": { |
|
"counts": { |
|
"6_proponents_biased": { |
|
"frugal_challenge_test": 139 |
|
}, |
|
"2_not_human": { |
|
"frugal_challenge_test": 137 |
|
}, |
|
"3_not_bad": { |
|
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|
}, |
|
"1_not_happening": { |
|
"frugal_challenge_test": 154 |
|
}, |
|
"5_science_unreliable": { |
|
"frugal_challenge_test": 160 |
|
}, |
|
"4_solutions_harmful_unnecessary": { |
|
"frugal_challenge_test": 160 |
|
}, |
|
"7_fossil_fuels_needed": { |
|
"frugal_challenge_test": 65 |
|
}, |
|
"0_not_relevant": { |
|
"frugal_challenge_test": 307 |
|
} |
|
}, |
|
"percentages": { |
|
"6_proponents_biased": { |
|
"frugal_challenge_test": 100.0 |
|
}, |
|
"2_not_human": { |
|
"frugal_challenge_test": 100.0 |
|
}, |
|
"3_not_bad": { |
|
"frugal_challenge_test": 100.0 |
|
}, |
|
"1_not_happening": { |
|
"frugal_challenge_test": 100.0 |
|
}, |
|
"5_science_unreliable": { |
|
"frugal_challenge_test": 100.0 |
|
}, |
|
"4_solutions_harmful_unnecessary": { |
|
"frugal_challenge_test": 100.0 |
|
}, |
|
"7_fossil_fuels_needed": { |
|
"frugal_challenge_test": 100.0 |
|
}, |
|
"0_not_relevant": { |
|
"frugal_challenge_test": 100.0 |
|
} |
|
} |
|
} |
|
} |
|
}, |
|
"text_stats": { |
|
"train": { |
|
"avg_length": 111.46446254978633, |
|
"median_length": 77.0, |
|
"std_length": 114.89517560291323, |
|
"min_length": 0, |
|
"max_length": 965, |
|
"total_words": 9207299 |
|
}, |
|
"test": { |
|
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|
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|
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|
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|
"max_length": 454, |
|
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|
} |
|
}, |
|
"vocabulary_stats": { |
|
"train": { |
|
"vocabulary_size": 70216, |
|
"total_tokens": 9207299, |
|
"unique_tokens_ratio": 0.007626123578695554 |
|
}, |
|
"test": { |
|
"vocabulary_size": 10676, |
|
"total_tokens": 56970, |
|
"unique_tokens_ratio": 0.18739687554853432 |
|
} |
|
}, |
|
"label_patterns": { |
|
"train": { |
|
"dominant_sources_per_label": { |
|
"3_not_bad": { |
|
"main_source": "climate-guard-thinking_data_nocomment_phi4_toxic_agent", |
|
"percentage": 39.34247570611207 |
|
}, |
|
"4_solutions_harmful_unnecessary": { |
|
"main_source": "climate-guard-synthetic_data_qwen_toxic_agent", |
|
"percentage": 41.04509787013883 |
|
}, |
|
"5_science_is_unreliable": { |
|
"main_source": "climate-guard-thinking_data_nocomment_phi4_toxic_agent", |
|
"percentage": 41.693476623998905 |
|
}, |
|
"6_proponents_biased": { |
|
"main_source": "climate-guard-synthetic_data_qwen_toxic_agent", |
|
"percentage": 53.390628679067575 |
|
}, |
|
"7_fossil_fuels_needed": { |
|
"main_source": "climate-guard-thinking_data_nocomment_phi4_toxic_agent", |
|
"percentage": 39.140292867264996 |
|
}, |
|
"1_not_happening": { |
|
"main_source": "climate-guard-synthetic_data_qwen_toxic_agent", |
|
"percentage": 41.30052724077329 |
|
}, |
|
"2_not_human": { |
|
"main_source": "climate-guard-synthetic_data_qwen_toxic_agent", |
|
"percentage": 39.627930682976555 |
|
}, |
|
"0_not_relevant": { |
|
"main_source": "frugal_challenge_train", |
|
"percentage": 41.55309033280507 |
|
} |
|
}, |
|
"label_diversity_per_source": { |
|
"climate-guard-thinking_data_nocomment_qwen_toxic_agent": { |
|
"unique_labels": 7, |
|
"entropy": 1.9322811905174009 |
|
}, |
|
"climate-guard-synthetic_data_qwen_toxic_agent": { |
|
"unique_labels": 7, |
|
"entropy": 1.9412569930894747 |
|
}, |
|
"climate-guard-thinking_data_nocomment_intern_toxic_agent": { |
|
"unique_labels": 7, |
|
"entropy": 1.9376010166020219 |
|
}, |
|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": { |
|
"unique_labels": 7, |
|
"entropy": 1.8879859048798708 |
|
}, |
|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": { |
|
"unique_labels": 7, |
|
"entropy": 1.9375508611483394 |
|
}, |
|
"climate-guard-synthetic_data_yi_toxic_agent": { |
|
"unique_labels": 7, |
|
"entropy": 1.941023858626436 |
|
}, |
|
"frugal_challenge_train": { |
|
"unique_labels": 1, |
|
"entropy": 0.0 |
|
}, |
|
"climate_fever": { |
|
"unique_labels": 1, |
|
"entropy": 0.0 |
|
}, |
|
"quota_climat": { |
|
"unique_labels": 8, |
|
"entropy": 2.0790581410796753 |
|
} |
|
}, |
|
"source_bias_analysis": { |
|
"climate-guard-thinking_data_nocomment_qwen_toxic_agent": { |
|
"kl_divergence": 0.0395716559443841 |
|
}, |
|
"climate-guard-synthetic_data_qwen_toxic_agent": { |
|
"kl_divergence": 0.045281501914864145 |
|
}, |
|
"climate-guard-thinking_data_nocomment_intern_toxic_agent": { |
|
"kl_divergence": 0.039965634146765544 |
|
}, |
|
"climate-guard-thinking_data_nocomment_phi4_toxic_agent": { |
|
"kl_divergence": 0.06259067672088119 |
|
}, |
|
"climate-guard-thinking_data_nocomment_yi_toxic_agent": { |
|
"kl_divergence": 0.044481091436281824 |
|
}, |
|
"climate-guard-synthetic_data_yi_toxic_agent": { |
|
"kl_divergence": 0.04597417615615136 |
|
}, |
|
"frugal_challenge_train": { |
|
"kl_divergence": 3.265057483962074 |
|
}, |
|
"climate_fever": { |
|
"kl_divergence": 3.265057483962074 |
|
}, |
|
"quota_climat": { |
|
"kl_divergence": 0.07482175184545027 |
|
} |
|
} |
|
}, |
|
"test": { |
|
"dominant_sources_per_label": { |
|
"6_proponents_biased": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
}, |
|
"2_not_human": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
}, |
|
"3_not_bad": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
}, |
|
"1_not_happening": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
}, |
|
"5_science_unreliable": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
}, |
|
"4_solutions_harmful_unnecessary": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
}, |
|
"7_fossil_fuels_needed": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
}, |
|
"0_not_relevant": { |
|
"main_source": "frugal_challenge_test", |
|
"percentage": 100.0 |
|
} |
|
}, |
|
"label_diversity_per_source": { |
|
"frugal_challenge_test": { |
|
"unique_labels": 8, |
|
"entropy": 1.9926606322233085 |
|
} |
|
}, |
|
"source_bias_analysis": { |
|
"frugal_challenge_test": { |
|
"kl_divergence": 0.0 |
|
} |
|
} |
|
} |
|
} |
|
} |
|
``` |
|
</details> |
|
|
|
## Intended Uses |
|
|
|
This dataset is designed for: |
|
- Training climate disinformation detection models |
|
- Developing fact-checking systems |
|
- Analyzing climate-related discourse patterns |
|
- Research in climate communication |
|
|
|
## Limitations |
|
|
|
- The dataset may contain some inherent biases from source datasets |
|
- Some sources are synthetic or AI-generated data |
|
- Language is primarily English |
|
- Coverage may vary across different types of climate disinformation |
|
|
|
## Citation |
|
|
|
If you use this dataset, please cite both this consolidated version and the original source datasets: |
|
|
|
```bibtex |
|
@dataset{consolidated_climate_dataset, |
|
author = {Joseph Pollack}, |
|
title = {Climate Guard Toxic Agent - Dataset}, |
|
year = {2024}, |
|
publisher = {Hugging Face}, |
|
url = {https://huggingface.co/datasets/Tonic/Climate-Guard-Toxic-Agent} |
|
} |
|
``` |
|
|
|
```bibtex |
|
@article{coan2021computer, |
|
title={Computer-assisted classification of contrarian claims about climate change}, |
|
author={Coan, Travis G and Boussalis, Constantine and Cook, John and others}, |
|
journal={Scientific Reports}, |
|
volume={11}, |
|
number={22320}, |
|
year={2021}, |
|
publisher={Nature Publishing Group}, |
|
doi={10.1038/s41598-021-01714-4} |
|
} |
|
``` |
|
|
|
## License |
|
|
|
This dataset is released under the same licenses as its source datasets. Please refer to individual source datasets for specific license information. |
|
|
|
## Contact |
|
|
|
For questions or issues regarding this dataset, [please open a community comment or descriptive PR](https://huggingface.co/datasets/Tonic/Climate-Guard-Toxic-Agent/discussions). |
|
|
|
## Acknowledgments |
|
|
|
We thank the creators and maintainers of all source datasets used in this consolidation: |
|
- The Tonic AI team |
|
- QuotaClimat team |
|
- Climate FEVER dataset creators |
|
- Takara AI team |
|
- All other contributors to the source datasets |
|
|
|
## Updates and Maintenance |
|
|
|
This dataset will be periodically updated to: |
|
- Fix any identified issues |
|
- Include new relevant source datasets |
|
- Improve data quality and consistency |
|
|
|
Last updated: [Current Date] |
|
Version: 1.0.0 |
