---
dataset_info:
  features:
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    dtype: string
  - name: submitter
    dtype: string
  - name: authors
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  - name: title
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  - name: comments
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  - name: journal-ref
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  - name: doi
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  - name: report-no
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  - name: categories
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  - name: license
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  - name: orig_abstract
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  - name: versions
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    - name: created
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    - name: version
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  - name: update_date
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  - name: authors_parsed
    sequence:
      sequence: string
  - name: abstract
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  splits:
  - name: train
    num_bytes: 147667993.3685569
    num_examples: 73768
  - name: test
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    num_examples: 15808
  - name: validation
    num_bytes: 31644285.315721553
    num_examples: 15808
  download_size: 115280347
  dataset_size: 210956563.99999997
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
---


# Dataset Card for arxiv_hep-th_primary Dataset

## Dataset Description

- **Homepage:** [Kaggle arXiv Dataset Homepage](https://www.kaggle.com/Cornell-University/arxiv)
- **Repository:** [FeynTune](https://github.com/Paul-Richmond/FeynTune)
- **Paper:** [tbd](tbd)
<!-- - **Point of Contact:** [Paul Richmond](mailto:p.richmond@qmul.ac.uk) -->

### Dataset Summary
This dataset contains metadata included in arXiv submissions.

## Dataset Structure

An example from the dataset looks as follows:

```
{'id': '0908.2896',
 'submitter': 'Paul Richmond',
 'authors': 'Neil Lambert, Paul Richmond',
 'title': 'M2-Branes and Background Fields',
 'comments': '19 pages',
 'journal-ref': 'JHEP 0910:084,2009',
 'doi': '10.1088/1126-6708/2009/10/084',
 'report-no': None,
 'categories': 'hep-th',
 'license': 'http://arxiv.org/licenses/nonexclusive-distrib/1.0/',
 'abstract': '  We discuss the coupling of multiple M2-branes to the background 3-form and\n6-form gauge fields of eleven-dimensional supergravity, including the coupling\nof the Fermions. In particular we show in detail how a natural generalization\nof the Myers flux-terms, along with the resulting curvature of the background\nmetric, leads to mass terms in the effective field theory.\n',
 'versions': [{'created': 'Thu, 20 Aug 2009 14:23:37 GMT', 'version': 'v1'}],
 'update_date': '2009-11-09',
 'authors_parsed': [['Lambert', 'Neil', ''], ['Richmond', 'Paul', '']]}
```

### Languages

The text in the `abstract` field of the dataset is in English, however there may be examples 
where the abstract also contains a translation into another language. 

## Dataset Creation

### Curation Rationale
The starting point was to load v193 of the Kaggle arXiv Dataset which includes arXiv submissions upto 23rd August 2024.
The arXiv dataset contains the following data fields:
- `id`: ArXiv ID (can be used to access the paper)
- `submitter`: Who submitted the paper
- `authors`: Authors of the paper
- `title`: Title of the paper
- `comments`: Additional info, such as number of pages and figures
- `journal-ref`: Information about the journal the paper was published in
- `doi`: [Digital Object Identifier](https://www.doi.org)
- `report-no`: Report Number
- `abstract`: The abstract of the paper
- `categories`: Categories / tags in the ArXiv system

To arrive at the arxiv_hep-th_primary dataset, the full arXiv data 
was filtered so that only `categories` which included 'hep-th' were retained.
This resulted in papers that were either primarily classified as 'hep-th' or appeared cross-listed.
For this dataset, the decision was made to focus only on papers primarily classified as 'hep-th'. 
This meant taking only those abstracts where the first characters in `categories` were 'hep-th' 
(see [here](https://info.arxiv.org/help/arxiv_identifier_for_services.html#indications-of-classification) for more details).

We also dropped entries whose `abstract` or `comments` contained the word 'Withdrawn' or 'withdrawn' and we removed the five records which appear in the repo `LLMsForHepth/arxiv_hepth_first_overfit`.

In addition, we have cleaned the data appearing in `abstract` by first replacing all occurences of '\n' with a whitespace and then removing any leading and trailing whitespace.

### Data splits

The dataset is split into a training, validation and test set with split percentages 70%, 15% and 15%. This was done by applying `train_test_split` twice (both with `seed=42`).
The final split sizes are as follows:

| Train | Test | Validation |
|:---:|:---:|:---:|
|73,768 | 15,808| 15,808   |