size_categories:10K<n<100K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: query_id
    dtype: int64
  - name: causal_type
    dtype: string
  - name: source
    dtype: string
  - name: query
    dtype: string
  - name: shortened_query
    dtype: string
  - name: is_causal_raw
    dtype: string
  - name: is_causal
    dtype: bool
  - name: domain_class
    dtype: string
  - name: action_class
    dtype: string
  - name: is_subjective
    dtype: bool
  - name: bloom_taxonomy
    dtype: string
  - name: user_needs
    dtype: string
  - name: uniqueness
    dtype: string
  - name: generation
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_text_generation_0
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_text_generation_0
      dtype: string
    - name: statistics_text_generation_0
      struct:
      - name: input_tokens
        dtype: int64
      - name: output_tokens
        dtype: int64
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 47638272
    num_examples: 13500
  download_size: 18759974
  dataset_size: 47638272
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for deepseek-r1-qwen-7b-cq-answers-full

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-7b-cq-answers-full/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dmitriihook/deepseek-r1-qwen-7b-cq-answers-full/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "action_class": null,
    "bloom_taxonomy": "Remembering",
    "causal_type": null,
    "distilabel_metadata": {
        "raw_input_text_generation_0": [
            {
                "content": "You will be give a long answer. You need to extract a final decision from it. Write only \u00271\u0027 or \u00272\u0027. \nIf the final answer was \u0027causal\u0027 or \u00271\u0027 write 1. If \u0027non-causal\u0027 or \u00272\u0027, write \u00272\u0027.\nYour input: Alright, so I need to classify the question \"ben may main library - mobile public library mobile al\" into either Category 1 (Causal) or Category 2 (Non-causal). Let me think about what each category entails.\n\nFirst, I remember that Category 1 is about cause-and-effect relationships. It includes questions that predict effects based on causes, find causes behind effects, or determine the causal link between variables. On the other hand, Category 2 doesn\u0027t involve any cause-effect reasoning.\n\nLooking at the question: \"ben may main library - mobile public library mobile al\". It seems like it\u0027s mentioning two libraries, \"main library\" and \"mobile public library.\" The mention of \"mobile\" might imply something related to portability or perhaps digital access. But is this suggesting a cause or an effect?\n\nI don\u0027t see any indication that one library causes the other or any action that leads to a result. It\u0027s just listing two libraries, one of which is mobile. There\u0027s no question about why one is mobile or how it affects the other. It doesn\u0027t ask about effects or causes; it\u0027s more about the existence or mention of these libraries.\n\nSo, since there\u0027s no cause-effect relationship implied here, I think it falls under Category 2: Non-causal.\n\u003c/think\u003e\n\nThe question \"ben may main library - mobile public library mobile al\" is asking about the mention of two libraries without implying any cause-or-effect relationship. It is therefore classified as:\n\n**boxed{Category 2: Non-causal}**",
                "role": "user"
            }
        ],
        "raw_output_text_generation_0": "2",
        "statistics_text_generation_0": {
            "input_tokens": 404,
            "output_tokens": 2
        }
    },
    "domain_class": null,
    "generation": "2",
    "is_causal": false,
    "is_causal_raw": "Reasoning: The question appears to be seeking information about a specific location, possibly looking for directions, contact information, or details about the \"Ben May Main Library - Mobile Public Library\" in Mobile, AL. This question does not suggest any asking for a cause, an effect, an intervention to achieve a goal, or exploring a cause-and-effect relationship. It simply seeks factual information about a location.\n\nCategory: Non-causal",
    "is_subjective": null,
    "model_name": "Qwen/Qwen2.5-1.5B-Instruct",
    "query": "ben may main library - mobile public library mobile al",
    "query_id": 7717,
    "shortened_query": "ben may main library - mobile public library mobile al",
    "source": "naturalQuestions",
    "uniqueness": "Unique Answer",
    "user_needs": "Knowledge and Information"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dmitriihook/deepseek-r1-qwen-7b-cq-answers-full", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("dmitriihook/deepseek-r1-qwen-7b-cq-answers-full")
```


</details>




