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Error code: UnexpectedError
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text string |
|---|
First Citizen: |
Before we proceed any further, hear me speak. |
All: |
Speak, speak. |
First Citizen: |
You are all resolved rather to die than to famish? |
All: |
Resolved. resolved. |
First Citizen: |
First, you know Caius Marcius is chief enemy to the people. |
All: |
We know't, we know't. |
First Citizen: |
Let us kill him, and we'll have corn at our own price. |
Is't a verdict? |
All: |
No more talking on't; let it be done: away, away! |
Second Citizen: |
One word, good citizens. |
First Citizen: |
We are accounted poor citizens, the patricians good. |
What authority surfeits on would relieve us: if they |
would yield us but the superfluity, while it were |
wholesome, we might guess they relieved us humanely; |
but they think we are too dear: the leanness that |
afflicts us, the object of our misery, is as an |
inventory to particularise their abundance; our |
sufferance is a gain to them Let us revenge this with |
our pikes, ere we become rakes: for the gods know I |
speak this in hunger for bread, not in thirst for revenge. |
Second Citizen: |
Would you proceed especially against Caius Marcius? |
All: |
Against him first: he's a very dog to the commonalty. |
Second Citizen: |
Consider you what services he has done for his country? |
First Citizen: |
Very well; and could be content to give him good |
report fort, but that he pays himself with being proud. |
Second Citizen: |
Nay, but speak not maliciously. |
First Citizen: |
I say unto you, what he hath done famously, he did |
it to that end: though soft-conscienced men can be |
content to say it was for his country he did it to |
please his mother and to be partly proud; which he |
is, even till the altitude of his virtue. |
Second Citizen: |
What he cannot help in his nature, you account a |
vice in him. You must in no way say he is covetous. |
First Citizen: |
If I must not, I need not be barren of accusations; |
he hath faults, with surplus, to tire in repetition. |
What shouts are these? The other side o' the city |
is risen: why stay we prating here? to the Capitol! |
All: |
Come, come. |
First Citizen: |
Soft! who comes here? |
Second Citizen: |
Worthy Menenius Agrippa; one that hath always loved |
the people. |
First Citizen: |
He's one honest enough: would all the rest were so! |
MENENIUS: |
What work's, my countrymen, in hand? where go you |
With bats and clubs? The matter? speak, I pray you. |
First Citizen: |
Our business is not unknown to the senate; they have |
had inkling this fortnight what we intend to do, |
which now we'll show 'em in deeds. They say poor |
suitors have strong breaths: they shall know we |
have strong arms too. |
MENENIUS: |
Why, masters, my good friends, mine honest neighbours, |
Will you undo yourselves? |
End of preview.
Dataset Card for "tiny_shakespeare"
Dataset Summary
40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
To use for e.g. character modelling:
d = datasets.load_dataset(name='tiny_shakespeare', sample_by="document")['train']
d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))
# train split includes vocabulary for other splits
vocabulary = sorted(set(next(iter(d)).numpy()))
d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
d = d.unbatch()
seq_len = 100
batch_size = 2
d = d.batch(seq_len)
d = d.batch(batch_size)
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 1.11 MB
- Size of the generated dataset: 1.11 MB
- Total amount of disk used: 2.23 MB
An example of 'train' looks as follows.
{
"text": "First Citizen:\nBefore we proceed any further, hear me "
}
Data Fields
The data fields are the same among all splits.
default
text: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 1 | 1 | 1 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@misc{
author={Karpathy, Andrej},
title={char-rnn},
year={2015},
howpublished={\url{https://github.com/karpathy/char-rnn}}
}
Contributions
Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.
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