Allennlp Random Text Instance Generation with Embedding

This function uses the Allennlp library to generate a random text instance and embed it into an embedder. First, it generates a random string, then tokenizes it and creates Token objects. Next, it creates a TextField from these token objects and converts it to an instance. Finally, it embeds the text field into a vector representation using a text field embedder.

Technology Stack : Allennlp (text processing library)

Code Type : Function

Code Difficulty : Intermediate

                
                    
import random
from allennlp.data import Instance
from allennlp.models import Model
from allennlp.modules import TextField, TextFieldEmbedder
from allennlp.tokenizers import Tokenizer, Token

def generate_random_instance(tokenizer: Tokenizer, text_field_embedder: TextFieldEmbedder):
    # Generate a random string
    random_text = ''.join(random.choices('abcdefghijklmnopqrstuvwxyz', k=50))
    # Tokenize the random string
    tokens = tokenizer.tokenize(random_text)
    # Create a Token object for each token
    token_objects = [Token(token, token=token) for token in tokens]
    # Create a TextField from the token objects
    text_field = TextField(token_objects, namespace="source")
    # Create an instance with the text field
    instance = Instance({"source": text_field})
    # Embed the text field
    embedded_text = text_field_embedder(embed(instance["source"]))
    return embedded_text