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: activities
    sequence: string
  - name: characters_with_names
    sequence: string
  - name: activities_qa
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_activities
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_activities
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 3366
    num_examples: 1
  download_size: 25128
  dataset_size: 3366
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-activities

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


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

It can be run directly using the CLI:

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

or explore the configuration:

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

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "activities": [
        "R\u00f3mulo Neves speaks to the camera throughout the scene.",
        "He gestures with his hands to emphasize his points.",
        "He brings his hands together.",
        "He points his finger up.",
        "He moves his right hand to the right.",
        "He brings his hands together again."
    ],
    "activities_qa": "{\n  \"qa\": [\n    {\n      \"answer\": \"A person is seated and talking to the camera.\",\n      \"question\": \"What is the initial position of the person?\"\n    },\n    {\n      \"answer\": \"The person emphasizes his points with gestures.\",\n      \"question\": \"What is the purpose of the person\u0027s hand movements?\"\n    },\n    {\n      \"answer\": \"They are together, with palms facing each other.\",\n      \"question\": \"What position are the person\u0027s hands in?\"\n    },\n    {\n      \"answer\": \"His finger is pointed upwards.\",\n      \"question\": \"In which direction is the person pointing?\"\n    },\n    {\n      \"answer\": \"To the right.\",\n      \"question\": \"Which direction does the person\u0027s right hand move to?\"\n    },\n    {\n      \"answer\": \"They are together, with palms facing each other.\",\n      \"question\": \"What position are the person\u0027s hands in, again?\"\n    }\n  ]\n}",
    "characters_with_names": [
        "A man with a black mustache and short beard. He wears a beige suit and a white shirt with a silver and black tie. He is seated and gestures with his hands while speaking."
    ],
    "distilabel_metadata": {
        "raw_input_activities": [
            {
                "content": "Your task is to understand the activities described in the video scene and provide a list of questions that users could ask to understand what happens in the video, for each question you should add the answer using ONLY the scene description provided.\n    \nMake sure the question can be answered with the information in the scene description and the answer is precise and accurate.\n\nYou must not use the name of the characters as those are unknown to potential users.\n\nTry to make the questions and answers diverse both in style and angle.\n\nRespond only with the questions and answer pairs as a list of json objects.\n\n\n1. Description: A man with a black mustache and short beard. He wears a beige suit and a white shirt with a silver and black tie. He is seated and gestures with his hands while speaking.\n\n\n\n## Activities\n\n1. Description: R\u00f3mulo Neves speaks to the camera throughout the scene.\n\n\n2. Description: He gestures with his hands to emphasize his points.\n\n\n3. Description: He brings his hands together.\n\n\n4. Description: He points his finger up.\n\n\n5. Description: He moves his right hand to the right.\n\n\n6. Description: He brings his hands together again.\n\n",
                "role": "user"
            }
        ],
        "raw_output_activities": "{\n  \"qa\": [\n    {\n      \"answer\": \"A person is seated and talking to the camera.\",\n      \"question\": \"What is the initial position of the person?\"\n    },\n    {\n      \"answer\": \"The person emphasizes his points with gestures.\",\n      \"question\": \"What is the purpose of the person\u0027s hand movements?\"\n    },\n    {\n      \"answer\": \"They are together, with palms facing each other.\",\n      \"question\": \"What position are the person\u0027s hands in?\"\n    },\n    {\n      \"answer\": \"His finger is pointed upwards.\",\n      \"question\": \"In which direction is the person pointing?\"\n    },\n    {\n      \"answer\": \"To the right.\",\n      \"question\": \"Which direction does the person\u0027s right hand move to?\"\n    },\n    {\n      \"answer\": \"They are together, with palms facing each other.\",\n      \"question\": \"What position are the person\u0027s hands in, again?\"\n    }\n  ]\n}"
    },
    "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

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


</details>




