nlp | Module for Natural Language Processing | Natural Language Processing library
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kandi X-RAY | nlp Summary
Module for Natural Language Processing (NLP)
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QUESTION
I am using the code below to create a list of sentences from a file document. The function will return a list of sentences.
...ANSWER
Answered 2021-Jun-15 at 22:00sentences
is a list per your function.
You may want to change your return statement to return a string instead.
The full function would therefore look like:
QUESTION
The following link shows how to add multiple EntityRuler with spaCy. The code to do that is below:
...ANSWER
Answered 2021-Jun-15 at 09:55Imagine that your dataframe is
QUESTION
I am doing some NLP work
my original dataframe is df_all
ANSWER
Answered 2021-Jun-15 at 08:15You could use collections.Counter
to count the words:
QUESTION
I'm currently working on a seminar paper on nlp, summarization of sourcecode function documentation. I've therefore created my own dataset with ca. 64000 samples (37453 is the size of the training dataset) and I want to fine tune the BART model. I use for this the package simpletransformers which is based on the huggingface package. My dataset is a pandas dataframe. An example of my dataset:
My code:
...ANSWER
Answered 2021-Jun-08 at 08:27While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:
- I use fairseq
- I can run my code on google colab with 1 GPU
- Got
RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12)
immediately when I tried to run it on multiple GPUs.
From the other people's code, I found that he uses python -m torch.distributed.launch -- ...
to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.
So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.
QUESTION
I want to train a custom NER model using spaCy v3 I prepared my train data and I used this script
...ANSWER
Answered 2021-Jun-13 at 14:54Make sure you are really using spaCy 3, in case you haven't :)
You can check this from the console by running python -c "import spacy; print(spacy.__version__)"
By issuing via command line pip install spacy==3.0.6
in a python env, and then running in the python console
QUESTION
I am working with Amazon reviews data and I am still learning about python and dataframes.
The df looks like this:
...ANSWER
Answered 2021-Jun-12 at 19:07Here you go, a few simple steps:
- Get counts per product and rating
QUESTION
ANSWER
Answered 2021-Jun-12 at 06:42The SpaCy tokenizer seems to cache each token in a map internally. Consequently, each new token increases the size of that map. Over time, more and more new tokens inevitably occur (although with decreasing speed, following Zipf's law). At some point, after having processed large numbers of texts, the token map will thus outgrow the available memory. With a large amount of available memory, of course this can be delayed for a very long time.
The solution I have chosen is to store the SpaCy model in a TTLCache and to reload it every hour, emptying the token map. This adds some extra computational cost for reloading the SpaCy model from, but that is almost negligible.
QUESTION
I am using spacy to get the dependency relation, this works well. But I have a problem of getting a pair of token with a specific dependency relation (except for the conj
relation).
When using the .dep_
, I can get the dependency attribute of each seprate token.
However, I would like to a pair of token for a specific dependency relation.
For example, in the following code, I can get the shown result.
ANSWER
Answered 2021-Jun-11 at 12:28You can use the head index. E.g.,
QUESTION
The following link shows how to add custom entity rule where the entities span more than one token. The code to do that is below:
...ANSWER
Answered 2021-Jun-09 at 17:49You need to define your own method to instantiate the entity ruler:
QUESTION
I have a question to NLP in R. My data is very big and so I need to reduce my data for further analysis to apply a SVM on it.
I have a Document-Term-Matrix like this:
...ANSWER
Answered 2021-Jun-06 at 17:25Here is how I would do it:
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