size_categories:n<1K 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: video_id
    dtype: string
  - name: scene_id
    dtype: int64
  - name: activities
    sequence: string
  - name: activities_qa
    dtype: string
  - name: mood
    sequence: string
  - name: mood_qa
    dtype: string
  - name: narrativeProgression
    sequence: string
  - name: narrative_progression_qa
    dtype: string
  - name: props
    sequence: string
  - name: props_qa
    dtype: string
  - name: thematicElements
    sequence: string
  - name: thematic_elements_qa
    dtype: string
  - name: videoEditingDetails
    sequence: string
  - name: video_editing_details_qa
    dtype: string
  - name: characters
    sequence: string
  - name: characters_qa
    dtype: string
  - name: model_names
    sequence: string
  splits:
  - name: train
    num_bytes: 388683
    num_examples: 100
  download_size: 161976
  dataset_size: 388683
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 finevideo-qa-debug

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


The pipeline script was uploaded to easily reproduce the dataset:
[qa.py](https://huggingface.co/datasets/dvilasuero/finevideo-qa-debug/raw/main/qa.py).

It can be run directly using the CLI:

```console
distilabel pipeline run --script "https://huggingface.co/datasets/dvilasuero/finevideo-qa-debug/raw/main/qa.py"
```


## 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/dvilasuero/finevideo-qa-debug/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/finevideo-qa-debug/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "activities": [],
    "activities_qa": "{ \n  \"qa\": [\n \n  ]\n}",
    "characters": [
        "No visual appearance, only voiceover."
    ],
    "characters_qa": "{\"qa\": [\n    {\"answer\": \"No\", \"question\": \"Is this character visible in the scene?\"}\n    ]}",
    "model_names": [
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct",
        "meta-llama/Llama-3.1-70B-Instruct"
    ],
    "mood": [
        "Curious - The music and the opening statement about thinning being an \"important tool\" evoke curiosity about the topic."
    ],
    "mood_qa": "{\"qa\": [\n  {\"answer\": \"thinning\", \"question\": \"What is being discussed in the scene?\"}, \n  {\"answer\": \"The music and the opening statement.\", \"question\": \"What creates the curious mood in the scene?\"}, \n  {\"answer\": \"Curious\", \"question\": \"What mood is evoked about the topic?\"}, \n  {\"answer\": \"the topic\", \"question\": \"What is the curious mood about in the scene?\"}\n]}",
    "narrativeProgression": [
        "Introduction to the concept of thinning as a forest management tool."
    ],
    "narrative_progression_qa": "{\n  \"qa\": [\n    {\n      \"answer\": \"Introduction to the concept of thinning as a forest management tool\",\n      \"question\": \"What is the video about?\"\n    },\n    {\n      \"answer\": \"None\",\n      \"question\": \"Can the speaker be seen in the video?\"\n    },\n    {\n      \"answer\": \"Yes\",\n      \"question\": \"Is the speaker heard in the video?\"\n    },\n    {\n      \"answer\": \"No information provided\",\n      \"question\": \"Where does the scene take place?\"\n    }\n  ]\n}",
    "props": [],
    "props_qa": "{\n\n\"qa\": []\n\n}",
    "scene_id": 1,
    "thematicElements": [
        "Forest management, thinning, benefits."
    ],
    "thematic_elements_qa": "{\"qa\": [\n  {\"answer\": \"Forest management, thinning, and its benefits are mentioned.\", \"question\": \"What topics are discussed in the video?\"},\n  {\"answer\": \"Thinning as a forest management practice is discussed.\", \"question\": \"Is thinning mentioned as part of forest management?\"},\n  {\"answer\": \"Forest management is discussed in the video, including thinning and its associated benefits.\", \"question\": \"What does the video say about forest management?\"}\n]}",
    "videoEditingDetails": [
        "Fade in from black at 0:00, title card appears at 0:04 with a split-screen showing a thinned forest on the left and title information on the right.",
        "Light, upbeat background music."
    ],
    "video_editing_details_qa": "{\"qa\": [\n  {\"answer\": \"The video starts with a fade in from black.\", \"question\": \"How does the video begin?\"},\n  {\"answer\": \"A title card appears at 0:04.\", \"question\": \"When does the title card appear?\"},\n  {\"answer\": \"The title card is shown in a split-screen format.\", \"question\": \"How is the title card displayed?\"},\n  {\"answer\": \"The split-screen shows a thinned forest on the left and title information on the right.\", \"question\": \"What is shown in the split-screen?\"},\n  {\"answer\": \"The background music is light and upbeat.\", \"question\": \"What is the tone of the background music?\"}\n]}",
    "video_id": "kT5C8WiFm6k"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/finevideo-qa-debug", "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("dvilasuero/finevideo-qa-debug")
```


</details>




