Datasets:
christopherastone
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README.md
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@@ -42,17 +42,25 @@ We hope it can serve as an aid in the development of language-based proof assist
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## Dataset Structure
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There are
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* The data in `proofs` consists of a `
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* The data in `sentences` consists of a `
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## Dataset Statistics
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## Dataset Usage
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Data can be downloaded as TSV files.
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```python
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from datasets import load_dataset
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dataset = load_dataset('proofcheck/prooflang', 'proofs', split='train', streaming
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for d in dataset.take(10):
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print(d['
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```
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To
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### Data Splits
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## Dataset Structure
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There are multiple TSV versions of the data. Primarily, `proofs` divides up the data proof-by-proof, and `sentences` further divides up the same data sentence-by-sentence.
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The `raw` dataset is a less-cleaned-up version of `proofs`. More usefully, the `tags` dataset gives arXiv subject tags for each paper ID found in the other data files.
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* The data in `proofs` (and `raw`) consists of a `paper` ID (identifying where the proof was extracted from), and the `proof` as a string.
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* The data in `sentences` consists of a `paper` ID, and the `sentence` as a string.
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* The data in `tags` consists of a `paper` ID, and the arXiv subject tags for that paper as a single comma-separated string.
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Further metadata about papers can be queried from arXiv.org using the paper ID.
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In particular, each paper `<id>` in the dataset can be accessed online at the url `https://arxiv.org/abs/<id>`
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## Dataset Size
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* `proofs` is 3,094,779,182 bytes (unzipped) and has 3,681,893 examples.
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* `sentences` is 3,545,309,822 bytes (unzipped) and has 38,899,132 examples.
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* `tags` is 7,967,839 bytes (unzipped) and has 328,642 rows.
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* `raw` is 3,178,997,379 bytes (unzipped) and has 3,681,903 examples.
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## Dataset Statistics
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## Dataset Usage
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Data can be downloaded as (zipped) TSV files.
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Accessing the data programmatically from Python is also possible using the `Datasets` library.
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For example, to print the first 10 proofs:
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```python
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from datasets import load_dataset
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dataset = load_dataset('proofcheck/prooflang', 'proofs', split='train', streaming='True')
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for d in dataset.take(10):
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print(d['paper'], d['proof'])
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```
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To look at individual sentences from the proofs,
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```python
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dataset = load_dataset('proofcheck/prooflang', 'proofs', split='train', streaming='True')
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for d in dataset.take(10):
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print(d['paper'], d['sentence'])
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```
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To get a comma-separated list of arXiv subject tags for each paper,
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```python
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from datasets import load_dataset
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dataset = load_dataset('proofcheck/prooflang', 'tags', split='train', streaming='True')
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for d in dataset.take(10):
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print(d['paper'], d['tags'])
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```
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Finally, to look at a version of the proofs with less aggressive cleanup (straight from the LaTeX extraction),
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```python
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dataset = load_dataset('proofcheck/prooflang', 'raw', split='train', streaming='True')
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for d in dataset.take(10):
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print(d['paper'], d['proof'])
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```
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### Data Splits
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