# AutoTrain Dataset for project: twitter-goemotions-binary-fear-classification

## Dataset Description

This dataset has been automatically processed by AutoTrain for project twitter-goemotions-binary-fear-classification.

### Languages

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

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "text": "Downvoting comments you don't like is your right.",
    "feat_id": "ed62dkv",
    "feat_author": "128bitworm",
    "feat_subreddit": "im14andthisisdeep",
    "feat_link_id": "t3_ac6bna",
    "feat_parent_id": "t1_ed5trip",
    "feat_created_utc": 1546542336.0,
    "feat_rater_id": 35,
    "feat_example_very_unclear": false,
    "feat_admiration": 0,
    "feat_amusement": 0,
    "feat_anger": 0,
    "feat_annoyance": 0,
    "feat_approval": 0,
    "feat_caring": 0,
    "feat_confusion": 0,
    "feat_curiosity": 0,
    "feat_desire": 0,
    "feat_disappointment": 0,
    "feat_disapproval": 1,
    "feat_disgust": 0,
    "feat_embarrassment": 0,
    "feat_excitement": 0,
    "target": 0,
    "feat_gratitude": 0,
    "feat_grief": 0,
    "feat_joy": 0,
    "feat_love": 0,
    "feat_nervousness": 0,
    "feat_optimism": 0,
    "feat_pride": 0,
    "feat_realization": 0,
    "feat_relief": 0,
    "feat_remorse": 0,
    "feat_sadness": 0,
    "feat_surprise": 0,
    "feat_neutral": 0
  },
  {
    "text": "I fucking love this",
    "feat_id": "edxv95q",
    "feat_author": "fueryerhealth",
    "feat_subreddit": "FellowKids",
    "feat_link_id": "t3_af72i1",
    "feat_parent_id": "t3_af72i1",
    "feat_created_utc": 1547342464.0,
    "feat_rater_id": 19,
    "feat_example_very_unclear": false,
    "feat_admiration": 1,
    "feat_amusement": 0,
    "feat_anger": 0,
    "feat_annoyance": 0,
    "feat_approval": 0,
    "feat_caring": 0,
    "feat_confusion": 0,
    "feat_curiosity": 0,
    "feat_desire": 0,
    "feat_disappointment": 0,
    "feat_disapproval": 0,
    "feat_disgust": 0,
    "feat_embarrassment": 0,
    "feat_excitement": 0,
    "target": 0,
    "feat_gratitude": 0,
    "feat_grief": 0,
    "feat_joy": 0,
    "feat_love": 1,
    "feat_nervousness": 0,
    "feat_optimism": 0,
    "feat_pride": 0,
    "feat_realization": 0,
    "feat_relief": 0,
    "feat_remorse": 0,
    "feat_sadness": 0,
    "feat_surprise": 0,
    "feat_neutral": 0
  }
]
```

### Dataset Fields

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

```json
{
  "text": "Value(dtype='string', id=None)",
  "feat_id": "Value(dtype='string', id=None)",
  "feat_author": "Value(dtype='string', id=None)",
  "feat_subreddit": "Value(dtype='string', id=None)",
  "feat_link_id": "Value(dtype='string', id=None)",
  "feat_parent_id": "Value(dtype='string', id=None)",
  "feat_created_utc": "Value(dtype='float32', id=None)",
  "feat_rater_id": "Value(dtype='int32', id=None)",
  "feat_example_very_unclear": "Value(dtype='bool', id=None)",
  "feat_admiration": "Value(dtype='int32', id=None)",
  "feat_amusement": "Value(dtype='int32', id=None)",
  "feat_anger": "Value(dtype='int32', id=None)",
  "feat_annoyance": "Value(dtype='int32', id=None)",
  "feat_approval": "Value(dtype='int32', id=None)",
  "feat_caring": "Value(dtype='int32', id=None)",
  "feat_confusion": "Value(dtype='int32', id=None)",
  "feat_curiosity": "Value(dtype='int32', id=None)",
  "feat_desire": "Value(dtype='int32', id=None)",
  "feat_disappointment": "Value(dtype='int32', id=None)",
  "feat_disapproval": "Value(dtype='int32', id=None)",
  "feat_disgust": "Value(dtype='int32', id=None)",
  "feat_embarrassment": "Value(dtype='int32', id=None)",
  "feat_excitement": "Value(dtype='int32', id=None)",
  "target": "ClassLabel(names=['0', '1'], id=None)",
  "feat_gratitude": "Value(dtype='int32', id=None)",
  "feat_grief": "Value(dtype='int32', id=None)",
  "feat_joy": "Value(dtype='int32', id=None)",
  "feat_love": "Value(dtype='int32', id=None)",
  "feat_nervousness": "Value(dtype='int32', id=None)",
  "feat_optimism": "Value(dtype='int32', id=None)",
  "feat_pride": "Value(dtype='int32', id=None)",
  "feat_realization": "Value(dtype='int32', id=None)",
  "feat_relief": "Value(dtype='int32', id=None)",
  "feat_remorse": "Value(dtype='int32', id=None)",
  "feat_sadness": "Value(dtype='int32', id=None)",
  "feat_surprise": "Value(dtype='int32', id=None)",
  "feat_neutral": "Value(dtype='int32', 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        | 168979 |
| valid        | 42246 |

task_categories:text-classification region:us
{"task_categories": ["text-classification"]}
TAGS
#task_categories-text-classification #region-us 

AutoTrain Dataset for project: twitter-goemotions-binary-fear-classification
============================================================================


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


This dataset has been automatically processed by AutoTrain for project twitter-goemotions-binary-fear-classification.


### Languages


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


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 unk.


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
#task_categories-text-classification #region-us 
 ### Languages


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


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
#task_categories-text-classification #region-us 
### Languages


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


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: