Python SDK¶
With the ELG Python SDK, you can use LT services and search the catalogue inside your Python projects.
Note
Find details of all the classes in Main Classes.
Installation¶
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.
Getting started¶
Functionalities¶
Use LTs¶
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 catalogue 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?")
Use corpus¶
from elg import Corpus
corpus = Corpus.from_id(913)
corpus.download()
Use the catalogue¶
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[0])
result = service("Did Nikola Tesla live in Berlin?")
Get info of an entity¶
from elg import Entity
entity = Entity.from_id(476)
print(entity)
Benchmark¶
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 Dupond 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 bench.services
.
CLI¶
elg run --help
elg download --help
elg info --help
elg search --help