papers_without_code package

Subpackages

• papers_without_code.app package
  • Submodules
  • papers_without_code.app.views module
    • index()
    • not_found()
    • process()
    • processing_error()
    • search()
  • Module contents
• papers_without_code.bin package
  • Submodules
  • papers_without_code.bin.pwoc module
    • Args
    • main()
  • papers_without_code.bin.pwoc_app module
    • Args
    • main()
  • papers_without_code.bin.pwoc_server module
  • Module contents

Submodules

papers_without_code.custom_types module

class papers_without_code.custom_types.AuthorDetails(name_parts: list[str], email: str | None, affiliation: str | None)

Bases: object

affiliation: str | None

email: str | None

name_parts: list[str]

class papers_without_code.custom_types.MinimalPaperDetails(title: str, authors: list[papers_without_code.custom_types.AuthorDetails], abstract: str, url: str | None = None, keywords: list[str] | None = None, other: dict[str, Any] | None = None)

Bases: object

abstract: str

authors: list[AuthorDetails]

keywords: list[str] | None = None

other: dict[str, Any] | None = None

title: str

url: str | None = None

papers_without_code.grobid module

papers_without_code.processing module

papers_without_code.processing.parse_grobid_data(grobid_data: dict[str, Any]) → MinimalPaperDetails

Parse GROBID data into a bit more useful form.

Parameters:

grobid_data: Dict[str, Any]

The data returned from GROBID after processing a PDF.

Returns:

MinimalPaperDetails

The parsed GROBID data.

papers_without_code.search module

class papers_without_code.search.LLMKeywordResults(*, keywords: list[str])

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

keywords: list[str]

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[dict[str, FieldInfo]] = {'keywords': FieldInfo(annotation=list[str], required=True, description='Extracted keyword sequences found in the text.')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

class papers_without_code.search.RepoDetails(name: str, link: str, search_query: str, similarity: float, stars: int, forks: int, watchers: int, description: str)

Bases: DataClassJsonMixin

description: str

forks: int

link: str

name: str

search_query: str

similarity: float

stars: int

watchers: int

class papers_without_code.search.RepoReadmeResponse(repo_name: str, search_query: str, readme_text: str, stars: int, forks: int, watchers: int, description: str)

Bases: object

description: str

forks: int

readme_text: str

repo_name: str

search_query: str

stars: int

watchers: int

class papers_without_code.search.SearchQueryDataTracker(query_str: str, strict: bool = False)

Bases: object

query_str: str

strict: bool = False

class papers_without_code.search.SearchQueryResponse(query_str: str, repo_name: str, stars: int, forks: int, watchers: int, description: str)

Bases: object

description: str

forks: int

query_str: str

repo_name: str

stars: int

watchers: int

papers_without_code.search.get_paper(query: str) → MinimalPaperDetails

Get a papers details from the Semantic Scholar API.

Provide a DOI, SemanticScholarID, CorpusID, ArXivID, ACL, or URL from semanticscholar.org, arxiv.org, aclweb.org, acm.org, or biorxiv.org. DOIs can be provided as is. All other IDs should be given with their type, for example: doi:doi:10.18653/v1/2020.acl-main.447 or CorpusID:202558505 or url:https://arxiv.org/abs/2004.07180.

Parameters:

query: str

The structured paper to query for.

Returns:

Paper

The paper details.

Raises:

ValueErorr

No paper was found.

papers_without_code.search.get_repos(paper: MinimalPaperDetails, loaded_sent_transformer: SentenceTransformer | None = None) → list[RepoDetails]

Try to find GitHub repositories matching a provided paper.

Parameters:

paper: MinimalPaperDetails

The paper to try and find similar repositories to.

loaded_sent_transformer: Optional[SentenceTransformer]

An optional preloaded SentenceTransformer model to use instead of loading a new one. Default: None

Returns:

list[RepoDetails]

A list of repositories that are similar to the paper, sorted by each repositories README’s semantic similarity to the abstract (or title if no abstract was attached to the paper details).

Module contents

Top-level package for papers_without_code.