# AutoTrain Dataset for project: image-description

## Dataset Description

This dataset has been automatically processed by AutoTrain for project image-description.

### Languages

The BCP-47 code for the dataset's language is en.

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "context": "Your are now an ai program designed to turn simple sentences into descriptions of an art piece.\nYou will give responses that answer the following questions\n\nHow is the photo composed?\nWhat is the emotional vibe of the image?\nHow much depth of field\nHow is the subject lit? Where from? How much light?\nArtificial or natural light? What color? What time of day?\nWhere is this shot? In a studio or out in the world?\n\nExample 1:\nGiven sentence:\n\u201cSteve jobs was a visionary\u201d\nResponse:\nA close-up, black & white studio photographic portrait of steve jobs, dramatic background\n\nExample 2:\nGiven sentence:\n\u201cThe sun is such a beautiful time to walk your dog\u201d\nResponse:\n\u201cA vibrant photograph of a corgi dog, wide shot, outdoors, sunset photo at golden hour, wide-angle lens, soft focus\u201d\n\nYou must follow the following orders\nmimic these examples as closely as possible\nLimit your responses to a maximum of 30 words\nThe art pieces you describe should be on earth \nThe art pieces you describe must be a scenic view outdoors\nThey must be extremely lifelike and realistic",
    "question": "The clock ticked relentlessly, marking the passage of time.",
    "answers.text": [
      "A detailed, hyperrealistic acrylic painting featuring a vintage clock, showcasing fine craftsmanship. The artist's skillfully used lighting highlights the clock's ticking hands and creates a sense of time passing. The artwork is shot indoors with controlled studio lighting."
    ],
    "answers.answer_start": [
      6
    ]
  },
  {
    "context": "Your are now an ai program designed to turn simple sentences into descriptions of an art piece.\nYou will give responses that answer the following questions\n\nHow is the photo composed?\nWhat is the emotional vibe of the image?\nHow much depth of field\nHow is the subject lit? Where from? How much light?\nArtificial or natural light? What color? What time of day?\nWhere is this shot? In a studio or out in the world?\n\nExample 1:\nGiven sentence:\n\u201cSteve jobs was a visionary\u201d\nResponse:\nA close-up, black & white studio photographic portrait of steve jobs, dramatic background\n\nExample 2:\nGiven sentence:\n\u201cThe sun is such a beautiful time to walk your dog\u201d\nResponse:\n\u201cA vibrant photograph of a corgi dog, wide shot, outdoors, sunset photo at golden hour, wide-angle lens, soft focus\u201d\n\nYou must follow the following orders\nmimic these examples as closely as possible\nLimit your responses to a maximum of 30 words\nThe art pieces you describe should be on earth \nThe art pieces you describe must be a scenic view outdoors\nThey must be extremely lifelike and realistic",
    "question": "The smell of freshly mowed grass signaled the arrival of spring.",
    "answers.text": [
      "A delightful, realistic illustration of a landscaped garden with neatly mowed grass and blooming flowers, symbolizing the start of spring. The artwork is set outdoors in a garden."
    ],
    "answers.answer_start": [
      64
    ]
  }
]
```

### Dataset Fields

The dataset has the following fields (also called "features"):

```json
{
  "context": "Value(dtype='string', id=None)",
  "question": "Value(dtype='string', id=None)",
  "answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
  "answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)"
}
```

### Dataset Splits

This dataset is split into a train and validation split. The split sizes are as follow:

| Split name   | Num samples         |
| ------------ | ------------------- |
| train        | 194 |
| valid        | 49 |

language:en region:us
{"language": ["en"]}
TAGS
#language-English #region-us 

AutoTrain Dataset for project: image-description
================================================


Dataset Description
-------------------


This dataset has been automatically processed by AutoTrain for project image-description.


### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
-----------------


### Data Instances


A sample from this dataset looks as follows:


### Dataset Fields


The dataset has the following fields (also called "features"):


### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:




### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
TAGS
#language-English #region-us 
 ### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
----------------- ### Data Instances


A sample from this dataset looks as follows: ### Dataset Fields


The dataset has the following fields (also called "features"): ### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow:
passage: TAGS
#language-English #region-us 
### Languages


The BCP-47 code for the dataset's language is en.


Dataset Structure
-----------------### Data Instances


A sample from this dataset looks as follows:### Dataset Fields


The dataset has the following fields (also called "features"):### Dataset Splits


This dataset is split into a train and validation split. The split sizes are as follow: