text-summarization | Inference Model for BertSum | Natural Language Processing library
kandi X-RAY | text-summarization Summary
kandi X-RAY | text-summarization Summary
Inference Model for BertSum
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Top functions reviewed by kandi - BETA
- Get input from user input
- Remove special characters from a string
- Format source text into Bertree format
- Write text to a file
- Process text using Stanford CoreNLP
- Convert a directory to a list of lines
- Tokenize a text using Stanford CoreNLP
- Summarize a model
- Infer the model from the given example
- Tidy the text
- Loads a list of state dictionaries
- Load the state of the model
- Build an optimizer
- Set parameters
- Return whether the user should use
- Shrink a model
- Crop a file
- Download and extract the tokenizer
text-summarization Key Features
text-summarization Examples and Code Snippets
Lines of Code : 30
License : No License
Copy(base) C:\Users\42maru\PycharmProjects\demo_bertsum_en\src>python inference.py
[INFO] Starting BERTSUM ...
[INFO] Enter the text:
When I first brought my cat home from the Humane Society she was a mangy, pitiful animal. She was so thin that you c python inference.py
python inference.py -num_sentence 3 -block_trigram true -visible_gpus -1 -checkpoint ../models/demo_ckpt_classifier.pt -bert_path '../models/bert_config_uncased_base.json' -tokenized_dir '../stanford-corenlp' -tokenizer_date '20 python optimize.py -path PATH -new_name NAME
Community Discussions
Trending Discussions on text-summarization
QUESTION
I am using the R programming language. I am trying to learn how to summarize text articles by using the following website: https://www.hvitfeldt.me/blog/tidy-text-summarization-using-textrank/
As per the instructions, I copied the code from the website (I used some random PDF I found online):
...ANSWER
Answered 2021-Apr-07 at 05:11The link that you shared reads the data from a webpage. div[class="padded"] is specific to the webpage that they were reading. It will not work for any other webpage nor the pdf from which you are trying to read the data. You can use pdftools package to read data from pdf.
QUESTION
I am using huggingface transformer models for text-summarization. Currently I am testing different models such as T5 and Pegasus. Now these models were trained for summarizing Big Texts into very short like a maximum of two sentences. Now I have the task, that I want summarizations, that are about half the size of the text, ergo the generated summaries are too small for my purpose.
My question now is, if there is a way to tell the model that another sentence came before? Kind of similar to the logic inside stateful RNNs (although I know they work completly different). If yes, I could summarize small windows over the sentences always with the information which content came before.
Is that just a thing of my mind? I cant believe that I am the only one, who wants to create shorter summaries, but not only 1 or two sentence long ones.
Thank you
...ANSWER
Answered 2020-Oct-25 at 21:39Why not transfer learning? Train them on your specific texts and summaries.
I trained T5 on specific limited text over 5 epoch and got very good results. I adopted the code from here to my needs https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb
Let me know if you have a specific training questions.
QUESTION
I have a set of short text files that I was able to combine into one datatest so that each file is in a row.
I am trying to summarize the content using the LSAfun package using the generic function argument genericSummary(text,k,split=c(".","!","?"),min=5,breakdown=FALSE,...)
This works very well for single text entry, however it does not in my case. In the package explanation it says that the text input should be "A character vector of length(text) = 1 specifiying the text to be summarized".
Please see this example
...ANSWER
Answered 2020-Oct-03 at 16:02Check class(dd$text). It's a factor, which is not a character.
The following works:
QUESTION
I have the following code taken directly from here with some pretty little modifications:
...ANSWER
Answered 2020-Aug-02 at 01:50Try explicitly moving your model to the GPU.
QUESTION
I am new to machine learning, and I am trying to work my way through a tutorial for text summarization using Keras.
I have reached the point of vectorizing the data, however I am getting an error, and I have tried everything I can myself. I really would like to get this program working, and was hoping someone could shed some light into why it is giving me this error and how I can fix it. I did look at previous posts, but none have helped so far, thanks. Here is my code:
...ANSWER
Answered 2019-Aug-12 at 15:29It seems like at least one of your story dictionaries does not have a key-value pair for the key 'highlights'. If this is only true for certain stories, you can simply check if there is a NoneType before iterating. If this is true for all stories, there might be a discrepancy between your code and the data you are working with.
Also, I believe there is an indentation error (might just be wrong SO formatting), but I believe the code after target_text = highlight should be indented once more to the right.
QUESTION
I have cloned keras-text-summarization, then was running according to README.md
python seq2seq_train.py and I get:
ANSWER
Answered 2018-Jul-04 at 12:45The folder keras_text_summarization was outside the demo package. So as it appears the documentation is incorrect. I needed the either to:
- Install the
keras_text_summarizationby running setup.py on parent folder. - Move
keras_text_summarizationinside the demo folder.
I ran step 2 and it worked (moved keras_text_summarization inside the demo folder).
This means no external modules were missing and condo installation was perfect.
QUESTION
I have installed visual studio code and code runner extension. I then have this piece of code:
ANSWER
Answered 2018-Jul-06 at 06:22Note: the following answer assumes you are on the correct version (3.6+), if not: simply, variable annotation isn't supported on your current version of Python.
The problem may seem like the type annotation is causing the SyntaxError, but another very plausible possibility is that there is an unclosed parenthesis or a unclosed something in the lines preceding. Since in the docs, it said:
The parser repeats the offending line and displays a little ‘arrow’ pointing at the earliest point in the line where the error was detected. The error is caused by (or at least detected at) the token preceding the arrow
(emphasis mine)
The parser can only detect an unclosed bracket when a token that is invalid in that context is given. Since brackets and parenthesis can carry through multiple lines (which means EOL wouldn't be raised), and text is a valid variable identifier, which leaves only that colons aren't allowed in brackets or parentheses (excepted when it's used as an parameter, which also accepts type annotation).
Here's an reproducible example of your code hosted on tio.run (the SE code-golf compiler):
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install text-summarization
You can use text-summarization like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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