Allennlp-Based Random Sentence Generator

The function uses the Allennlp library to generate a specified number of random sentences. It first defines a basic text field and text field embedder, then creates a model, dataset reader, iterator, and trainer. Finally, it generates random sentences by simulating the training process.

Technology Stack : Allennlp, TextField, BasicTextFieldEmbedder, Model, SentenceReader, BucketIterator, Trainer

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
def random_sentence_generator(seed, num_sentences):
    from allennlp.data import Instance
    from allennlp.data.fields import TextField
    from allennlp.models import Model
    from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
    from allennlp.data.dataset_readers import SentenceReader
    from allennlp.data.iterators import BucketIterator
    from allennlp.training import Trainer

    # Define the text field
    text_field = TextField()

    # Create the text field embedder
    text_field_embedder = BasicTextFieldEmbedder({"tokens": text_field})

    # Create the model
    model = Model(
        text_field_embedder=text_field_embedder
    )

    # Create the dataset reader
    reader = SentenceReader()

    # Generate random sentences
    random_sentences = [f"Random sentence {i}" for i in range(num_sentences)]

    # Create instances
    instances = [reader.read_instance(random_sentences[i]) for i in range(num_sentences)]

    # Create the iterator
    iterator = BucketIterator(batch_size=2, sort_key=lambda x: len(x["tokens"]), shuffle=True)

    # Create the trainer
    trainer = Trainer(model=model, iterator=iterator, dataset_reader=reader)

    # Train the model (simulated training with random data)
    for _ in range(1):  # Simulate one training iteration
        for instance in instances:
            trainer.train_on_instance(instance)

    return random_sentences