Difference between revisions of "Parsing/stanfordnlp"
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foo = stanfordnlp.Pipeline(lang = "en")("Kim wanted to be heard."); \ | foo = stanfordnlp.Pipeline(lang = "en")("Kim wanted to be heard."); \ | ||
foo.sentences[0].print_dependencies();' | foo.sentences[0].print_dependencies();' | ||
| + | </pre> | ||
| + | |||
| + | It is possible to combine this module with the NLPL installation of | ||
| + | CoreNLP and use its server interface to offload processing requests | ||
| + | to CoreNLP. | ||
| + | In a multi-user environment, it may in principle be necessary to | ||
| + | pick a different (non-privileged) port for the server, as each port | ||
| + | can only be used by one process at any point in time (see the | ||
| + | initialization in <code>test.py</code>): | ||
| + | <pre> | ||
| + | module purge; module load nlpl-corenlp nlpl-stanfordnlp | ||
| + | python3 /projects/nlpl/software/stanfordnlp/test.py | ||
</pre> | </pre> | ||
Latest revision as of 22:03, 11 May 2019
Background
The StanfordNLP pipeline makes available two multilingual parsing systems with a Python interface, one implemented natively on top of PyTorch, the other a server–client interface to the venerable Stanford CoreNLP software (which runs in Java).
module purge; module load nlpl-stanfordnlp
python3 -c 'import stanfordnlp; \
foo = stanfordnlp.Pipeline(lang = "en")("Kim wanted to be heard."); \
foo.sentences[0].print_dependencies();'
It is possible to combine this module with the NLPL installation of
CoreNLP and use its server interface to offload processing requests
to CoreNLP.
In a multi-user environment, it may in principle be necessary to
pick a different (non-privileged) port for the server, as each port
can only be used by one process at any point in time (see the
initialization in test.py):
module purge; module load nlpl-corenlp nlpl-stanfordnlp python3 /projects/nlpl/software/stanfordnlp/test.py
Installation on Abel
module purge; module load python3/3.70 /projects/nlpl/operation/python/initialize --version 0.1.1 stanfordnlpl
Next, it appears we need to manually patch the default location
for model files in
.../site-packages/stanfordnlp/utils/resources.py,
to point to a shared directory for all users (viz.
/projects/nlpl/software/stanfordnlp/0.1.1/resources/).
To download all available pre-trained models (for the complete
UD 2.x set of treebanks):
module purge; module load nlpl-stanfordnlp yes | python3 /projects/nlpl/software/stanfordnlp/0.1.1/download.py
