​Entity Recognition

POST https://vulavula-services.lelapa.ai/api/v1/entity_recognition/process.

The Named Entity Recognition (NER) endpoint analyses the provided text and identifies named entities such as persons, locations, and more. Each request can handle up to 2,000 tokens, with 180 tokens recommended for optimal performance.

BODY PARAMS

encoded_text string required.
This parameter represents the text input that will be analysed for named entities. It is required for the endpoint to function properly. The encoded_text should be provided as a string and contains the text to be processed by the Named Entity Recognition (NER) model. The text may include any language and should ideally be encoded in a format suitable for NER processing, ensuring accurate entity detection.

HEADERS

X-CLIENT-TOKEN string required.
Represents the authentication token required for accessing the API. This header ensures that only authorized clients can make requests to the endpoint.

EXAMPLES

pdm add vulavula

from vulavula import VulavulaClient
client = VulavulaClient("<INSERT_TOKEN>")

entity_result = client.get_entities({'encoded_text': 'President Ramaphosa gaan loop by Emfuleni Municipality.'})
print("Entity Recognition Output:", entity_result)

RESPONSES

[
  {
    "entity": "person",
    "word": "Ramaphosa",
    "start": 10,
    "end": 19
  },
  {
    "entity": "location",
    "word": "Emfuleni Municipality",
    "start": 33,
    "end": 54
  }
]