NER_RNN_mine | modification of NER_RNN https
kandi X-RAY | NER_RNN_mine Summary
kandi X-RAY | NER_RNN_mine Summary
NER_RNN_mine is a Python library. NER_RNN_mine has no bugs, it has no vulnerabilities and it has low support. However NER_RNN_mine build file is not available. You can download it from GitHub.
Inintian NER_RNN provide the functionality to name-entityi recognition problem. The proposed approcahes are: birnn cfr- word-level embeddings cnn- char-level embeddings and convolution under it cnn-cfr - concatenation of word-level and char-level embeddings(with convolution). You can see the details in src/model/model_name.py in forward function. The model provides seq_indexer class to map word or char to indexes for subsequent submission to the model input. I replace 2 layer of cnn-cfr by elmo layer from allenlp module. As allennlp provide its own indexing, seq_indexer is disabled. Also elmo requires input not according to words, but according to sentences (since embedding is determined from the context). Elmo object is created when create object of corresponding model (in main.py "isElmo" flag goes to model factory). run as python3 main.py --train path_to_train --dev path_to_dev --test path_to_test --data-io connl-ner-2003 --evaluator f1-alpha-match-10 --model BiRNNCRF --opt adam --lr 0.001 --save-best yes --patience 20 --rnn-hidden-dim 200 --gpu 1 --save model_name. dev-train-test for aspects are in
Inintian NER_RNN provide the functionality to name-entityi recognition problem. The proposed approcahes are: birnn cfr- word-level embeddings cnn- char-level embeddings and convolution under it cnn-cfr - concatenation of word-level and char-level embeddings(with convolution). You can see the details in src/model/model_name.py in forward function. The model provides seq_indexer class to map word or char to indexes for subsequent submission to the model input. I replace 2 layer of cnn-cfr by elmo layer from allenlp module. As allennlp provide its own indexing, seq_indexer is disabled. Also elmo requires input not according to words, but according to sentences (since embedding is determined from the context). Elmo object is created when create object of corresponding model (in main.py "isElmo" flag goes to model factory). run as python3 main.py --train path_to_train --dev path_to_dev --test path_to_test --data-io connl-ner-2003 --evaluator f1-alpha-match-10 --model BiRNNCRF --opt adam --lr 0.001 --save-best yes --patience 20 --rnn-hidden-dim 200 --gpu 1 --save model_name. dev-train-test for aspects are in
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NER_RNN_mine has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
NER_RNN_mine has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of NER_RNN_mine is current.
Quality
NER_RNN_mine has no bugs reported.
Security
NER_RNN_mine has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
NER_RNN_mine does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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NER_RNN_mine releases are not available. You will need to build from source code and install.
NER_RNN_mine has no build file. You will be need to create the build yourself to build the component from source.
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NER_RNN_mine Key Features
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NER_RNN_mine Examples and Code Snippets
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Install NER_RNN_mine
You can download it from GitHub.
You can use NER_RNN_mine 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.
You can use NER_RNN_mine 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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For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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