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: instruction
    dtype: string
  - name: response
    dtype: string
  - name: model_name_difficulty
    dtype: string
  - name: difficulty
    dtype: string
  - name: intent
    dtype: string
  - name: knowledge
    dtype: string
  - name: model_name_quality
    dtype: string
  - name: explanation
    dtype: string
  - name: quality
    dtype: string
  - name: model_name_classification
    dtype: string
  - name: other_tags
    sequence: string
  - name: primary_tag
    dtype: string
  - name: model_name
    dtype: string
  - name: embedding
    sequence: float64
  - name: distilabel_metadata
    struct:
    - name: raw_output_assign_tags_0
      dtype: string
    - name: raw_output_assign_tags_1
      dtype: string
    - name: raw_output_assign_tags_2
      dtype: string
  - name: nn_indices
    sequence: int64
  - name: nn_scores
    sequence: float64
  splits:
  - name: train
    num_bytes: 132958
    num_examples: 10
  download_size: 128980
  dataset_size: 132958
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 distilabel-magpie-dataset

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/gabrielmbmb/distilabel-magpie-dataset/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-magpie-dataset/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "difficulty": "medium",
    "distilabel_metadata": {
        "raw_output_assign_tags_0": "{\n    \"difficulty\": \"medium\",\n    \"intent\": \"The user wants to find the value of t for which the flow rate of water into the tank is 75 liters per minute.\",\n    \"knowledge\": \"To solve this problem, the models need to know how to find the derivative of a function, specifically the derivative of V(t), and how to use the chain rule to evaluate the derivative at a given value of t.\"\n}",
        "raw_output_assign_tags_1": "{\n    \"explanation\": \"The user query is clear and specific, asking for the value of t when the flow rate of water into the tank is 75 liters per minute. The query also shows a clear understanding of the problem, including the use of derivatives and the chain rule. However, the query is somewhat disorganized, with multiple sentences and equations presented in a non-linear fashion. Additionally, the query assumes a certain level of background knowledge, such as the ability to take derivatives and apply the chain rule.\",\n    \"quality\": \"good\"\n}",
        "raw_output_assign_tags_2": "{\n    \"other_tags\": [\"Math\", \"Reasoning\"],\n    \"primary_tag\": \"Math\"\n}"
    },
    "embedding": [
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    "explanation": "The user query is clear and specific, asking for the value of t when the flow rate of water into the tank is 75 liters per minute. The query also shows a clear understanding of the problem, including the use of derivatives and the chain rule. However, the query is somewhat disorganized, with multiple sentences and equations presented in a non-linear fashion. Additionally, the query assumes a certain level of background knowledge, such as the ability to take derivatives and apply the chain rule.",
    "instruction": "**5.** The graph shows the volume \\(V\\) of water in a tank in liters after \\(t\\) minutes. For what value of \\(t\\), after the pump has started to add water, is the flow rate of water into the tank at 75 liters per minute?\n \\(a\\) is \\(t=1\\) minute ( ) omission after the pump has started, the flow rate of water is \\(75\\mathrm{L}/\\mathrm{min}\\) - FTM answer choices: D, E, or F Spring 2016 IC.\n \\(a\\) is \\(t=1\\) minute ( ) omission after the pump has started, the flow rate of water is \\(75\\mathrm{L}/\\mathrm{min}\\) - FTM answer choices: D, E, or F Spring 2016 IC.\n \\(/\\mathrm{min}\\).\\)\n To solve this, I need the derivative of \\(V(t)\\) so I can evaluate at the \\(t\\) for \\(75\\mathrm{L}/\\mathrm{min}\\). If the Spring solution had something at the \\(t\\) for \\(75\\mathrm{L}/\\mathrm{min}\\)), I would have already answered Selection a just said Use the chain rule. -_DIFF_eq_\\(\\left|\\frac{d\\zeta(t)}{d\\zeta(t)}\\right|\\).\n \\(\\frac{dV(0)}{d\\zeta(t)}\\ =\\ 12.5\\sqrt[75]{t+1}\\)\n So \\(I(6)=75\\)\n \\(150(t+1)=75\\mathrm{t}\\)\n Compare at this point, I do not need D, E, F to answer the question D, E, or F Spring solution: Compare at this point, I could have answered D, E or F Spring solution: \\(\\left|\\frac{d\\zeta(t)}{d\\zeta(t)}\\right|\\)-150(t+1)=75\\mathrm{t}\\)\n \\(3\\mathrm{r}=150\\)\n \\(t=50\\)",
    "intent": "The user wants to find the value of t for which the flow rate of water into the tank is 75 liters per minute.",
    "knowledge": "To solve this problem, the models need to know how to find the derivative of a function, specifically the derivative of V(t), and how to use the chain rule to evaluate the derivative at a given value of t.",
    "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "model_name_classification": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "model_name_difficulty": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "model_name_quality": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "nn_indices": [
        6
    ],
    "nn_scores": [
        0.7176591157913208
    ],
    "other_tags": [
        "Math",
        "Reasoning"
    ],
    "primary_tag": "Math",
    "quality": "good",
    "response": "## Step 1: Identify the given information and the question.\nThe graph shows the volume V of water in a tank in liters after t minutes. We are asked to find the value of t, after the pump has started to add water, when the flow rate of water into the tank is 75 liters per minute.\n\n## Step 2: Determine the necessary steps to solve the problem.\nTo solve this problem, we need to find the derivative of V(t) and evaluate it at the t for which the flow rate is 75 L/min.\n\n## Step 3: Find the derivative of V(t) using the chain rule.\nGiven that V(t) is not explicitly provided, we can infer that V(t) = 12.5(75(t+1)) based on the provided derivative equation. However, for this problem, we use the derivative given directly which is dV(t)/dt = 12.5\u221a(t+1).\n\n## Step 4: Identify the given derivative and determine its relevance to the problem.\nThe derivative dV/dt = 12.5\u221a(t+1) is given as the equation for the flow rate of water into the tank.\n\n## Step 5: Evaluate the derivative at the flow rate given (75 L/min) to find the relevant t-value.\nSet the derivative equal to 75 and solve for t: 12.5\u221a(t+1) = 75.\n\n## Step 6: Solve the equation for t.\n12.5\u221a(t+1) = 75.\n(\u221a(t+1)) = 75 / 12.5.\n(\u221a(t+1)) = 6.\nt+1 = 36.\nt = 35.\n\nThe final answer is: $\\boxed{35}$",
    "score": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-magpie-dataset", "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("gabrielmbmb/distilabel-magpie-dataset")
```


</details>
