dblp | Fetch Bibtex entries directly from DBLP | Addon library

Try this:

You can use etree.iterparse to avoid loading the whole file in memory:

I'm one of the developers of OCTIS.

Short answer: If I understood your problem, you can fix this issue by modifying the parameter "bert_path" of CTM and make it dataset-specific, e.g. CTM(bert_path="path/to/store/the/files/" + data)

TL;DR: I think the problem is related to the fact that CTM generates and stores the document representations in some files with a default name. If these files already exist, it uses them without generating new representations, even if the dataset has changed in the meantime. Then CTM will raise that issue because it is using the BOW representation of a dataset, but the contextualized representations of another dataset, resulting in two representations with different dimensions. Changing the name of the files with respect to the name of the dataset will allow the model to retrieve the correct representations.

If you have other issues, please open a GitHub issue in the repo. I've found out about this issue by chance.

sorry ,the output maybe like this:

Use globstar.

your match will include arbitrary depths of subdirectories, so your command logic and parameter parsing for output filenames won't even need to change.

I've had to make quite some extrapolations and assumptions, but it looks like

• you're using Django
• you want to populate an SQL database with venue, paper and author data
• you want to then do some analysis using Pandas

Populating your SQL database can be done pretty neatly with something like the following.

• I added the tqdm package so you get a progress indication.
• This assumes there's a PaperAuthor model that links papers and authors.
• Unlike the original code, this will not save duplicate Venues in the database.
• You can see I replaced get_or_create and create with stubs to make this runnable without the database models (or indeed, without Django), just having the dataset you're using available.

On my machine, this consumes practically no memory, as the records are (or would be) dumped into the SQL database, not into an ever-growing, fragmenting dataframe in memory.

The Pandas processing is left as an exercise for the reader ;-), but I'd imagine it'd involve pd.read_sql() to read this preprocessed data from the database.

I'm looking into the 406 error code, which happens when the server cannot respond with the accept-header specified in the request. If I can get urlopen to work correctly, I will post that answer too.

I don't get this error when using Python Requests

Try this:

• I replaced the set with a map.
• The statement that does the work is

Remark

Regexen are the wrong tool to extract information from xml (or similar structured formats). The general approach is not recommended. For the right way to handle it, cf. Michael Kay's answer.

Answer

You provide the wrong argument in constructing the matcher. Instead of the expression in your code you need to provide the current line: