Random Translation with Fairseq Models and Datasets

This function uses Fairseq library's models and datasets for random translation. It first randomly selects a model and corpus, then loads the model and dataset, and finally translates the input sentence.

Technology Stack : Fairseq, PyTorch, FairseqDataset, FairseqModel

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
import random
import torch
from fairseq.models import FairseqModel
from fairseq.data import FairseqDataset

def generate_random_translation(input_sentence, model_path):
    """
    This function takes an input sentence and a model path, then uses a randomly selected Fairseq model
    to translate the input sentence.
    """
    # Randomly select a Fairseq model
    models = ['transformer', 'lstm', 'convolutional', 'transformer_xl']
    selected_model = random.choice(models)
    
    # Load the model
    model = FairseqModel.from_pretrained(model_path, arch=selected_model)
    
    # Randomly select a Fairseq dataset
    datasets = ['wmt14_en_de', 'opus14', 'iwslt15_de_en']
    selected_dataset = random.choice(datasets)
    
    # Load the dataset
    dataset = FairseqDataset.from_pretrained(model_path, corpus_name=selected_dataset)
    
    # Translate the input sentence
    translated_sentence = model.translate_one(dataset, input_sentence)
    
    return translated_sentence