Use the European Language Grid in your Python projects
European Language Grid Python SDK
The European Language Grid is the primary platform for Language Technology in Europe. With the ELG Python SDK, you can use LT services and search the catalog inside your Python projects.
Via pip / PyPI
pip install elg
Register on the ELG
Please visit the ELG website to create a user account if you haven't got one already.
Initialize LT service
LT Services can be initialized as
Service objects using the
service_id parameter and an integer corresponding to the LT service you want to use.
In the following code example,
474 corresponds to the Cogito Discover Named Entity Recognizer (notice the ID at the end of the URL). For more LT services, search the catalog via the
Catalog functionality (see section below) or visit the list of LT services on the ELG.
from elg import Service # Init LT service using its ID lt = Service.from_id(474)
This requires you to login to your ELG account via the URL that is printed on your terminal.
After successful login, your tokens are saved as
~/.cache/elg/tokens.json, so you do not need to log in again for subsequent calls.
Run LT service
You can either pass an input file or a string / raw text.
# Pass an input file that should be processed by the LT service result = lt("path/to/file") # You can also directly pass raw text to the LT service in most cases result = lt("Did Nikola Tesla live in Berlin?")
from elg import Corpus corpus = Corpus.from_id(913) corpus.download()
Use the catalog
from elg import Catalog catalog = Catalog() # Search and get the result as a list of Entity results = catalog.search( resource = "Tool/Service", # "Corpus", "Lexical/Conceptual resource" or "Language description" function = "Machine Translation", # function should be pass only if resource is set to "Tool/Service" languages = ["en", "fr"], # string or list if multiple languages limit = 100, ) # search interactively catalog.interactive_search( search = "keyword1 keyword2 ...", resource = "Tool/Service", # "Corpus", "Lexical/Conceptual resource" or "Language description" function = "Machine Translation", # function should be pass only if resource is set to "Tool/Service" languages = ["en", "fr"], # string or list if multiple languages )
Create a LT service object from the results
service = Service.from_entity(results) result = service("Did Nikola Tesla live in Berlin?")
Get info of an entity
from elg import Entity entity = Entity.from_id(476) print(entity)
You can also run a benchmark that evaluates multiple services receiving the same input:
from elg import Benchmark ben = Benchmark.from_ids([610, 624]) result = ben(["Bush is the president of the USA and lives in Washington", "My name is Rémi and I live in France"], number_of_runs=4) df = result.compare() print("General comparison:\n", df) df = result.compare_results() print("Comparison of the results:\n", df) df = result.compare_response_times() print("Comparison of the response time:\n", df)
You can investigate the results by saving the output from the
bench call to a variable or by accessing
elg-cli search *term1* *term2* ... *termN* --lang "" --resource "" --function "" elg-cli info *id* elg-cli run *id* --authentication_file path/to/tokens.json --data_file path/to/file
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