# AutoTrain Dataset for project: lwf-summarization

## Dataset Description

This dataset has been automatically processed by AutoTrain for project lwf-summarization.

### Languages

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

## Dataset Structure

### Data Instances

A sample from this dataset looks as follows:

```json
[
  {
    "feat_id": "13716782",
    "target": "The scariest place for Jessica was the Capuchin Catacombs in Palermo.",
    "text": "Kelly: Oh! Oh! Can I pick the first question?\r\nJessica: Sure. Go for it!\r\nKelly: What's the scariest place you've been to!\r\nJessica: I'll start: Palermo in Italy.\r\nMickey: And what's so scary about that? Did you break your nail? :P\r\nJessica: Shut it, Mickey! No, there are the Capuchin Catacombs with 8000 corpses! \r\nKelly: Ewwww! Corpses? Rly?\r\nJessica: Yeah! And you can look at them like museum exhibits. I think they're divided somehow, but have no clue how!\r\nOllie: That's so cool! Do you get to see the bones or are they covered up?\r\nJessica: Well, partly. Most of them were exhibited in their clothes. Basically only skulls and hands. \r\nMickey: I'm writing this one down! That's so precious!\r\nOllie: Me too!"
  },
  {
    "feat_id": "13716592",
    "target": "Carrie and Gina saw \"Fantastic Beast\" and liked it. Ginna loved Eddie Redmayne as Newt.  ",
    "text": "Carrie: Just back from Fantastic Beast :)\r\nGina: and what do you think?\r\nCarrie: generally good - as usual nice special effect and visuals, an ok plot, a glimpse of the wizarding community in the US.\r\nAlex: Sounds cool. I was thinking of going this weekend with Lane, but I've seen some bad reviews.\r\nCarrie: Depends on what you expect really - I have a lot of sentiment towards Harry Potter so, I'm gonna like everything the do. But seriously the movie was decent. However, if you're expecting to have your mind blown, then no, it's not THAT good.\r\nGina: I agree. I saw it last week and basically I'm satisfied.\r\nAlex: No spoilers, girls.\r\nCarrie: no worries ;)\r\nCarrie: And Gina, what do you think about Eddie Redmayne as Newt?\r\nGina: I loved him <3 I loved how introverted and awkward he was and how caring he was towards the animals. And with all that he showed a lot of confidence in his beliefs and was a genuinely compassionate character\r\nCarrie: not your standard protagonist, that's for sure\r\nGina: and that's what I liked about him\r\nAlex: Maybe I'll go and see it sooner so we can all talk about it.\r\nCarrie: go see it. If' you're not expecting god-knows-what you're going to enjoy it ;)"
  }
]
```

### Dataset Fields

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

```json
{
  "feat_id": "Value(dtype='string', id=None)",
  "target": "Value(dtype='string', id=None)",
  "text": "Value(dtype='string', 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        | 655 |
| valid        | 164 |

task_categories:summarization language:zh region:us
{"language": ["zh"], "task_categories": ["summarization"]}
TAGS
#task_categories-summarization #language-Chinese #region-us 

AutoTrain Dataset for project: lwf-summarization
================================================


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


This dataset has been automatically processed by AutoTrain for project lwf-summarization.


### Languages


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


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 zh.


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-summarization #language-Chinese #region-us 
 ### Languages


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


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-summarization #language-Chinese #region-us 
### Languages


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


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: