Computing Word Embeddings with BERT using Allennlp

This function uses the BERT model from the Allennlp library to compute word embeddings for the input text. The function first loads the pre-trained BERT model and tokenizer, then tokenizes the input text and converts it to the format required by the model, and finally uses the model to make a prediction and return the word embeddings.

Technology Stack : Allennlp, BERT, Transformer, PretrainedTransformerTokenizer, Vocabulary, DatasetReader, Instance

Code Type : Function

Code Difficulty : Intermediate

                
                    
def random_word_embeddings(text, model_path="https://storage.googleapis.com/allennlp-public-models/bert-base-uncased-sentiment"):
    from allennlp.models import Model
    from allennlp.data import DatasetReader, Instance
    from allennlp.data.tokenizers import PretrainedTransformerTokenizer
    from allennlp.data.vocabulary import Vocabulary
    from allennlp.nn.util import get_text_field_mask
    from allennlp.predictors.predictor import Predictor

    # Load the model and tokenizer
    dataset_reader = DatasetReader.from_pretrained("bert-base-uncased-sentiment")
    tokenizer = PretrainedTransformerTokenizer.from_pretrained("bert-base-uncased")
    vocabulary = Vocabulary.from_pretrained("bert-base-uncased-sentiment")

    # Load the model
    model = Model.from_pretrained(model_path)

    # Tokenize the text
    tokens = tokenizer.tokenize(text)
    token_ids = tokenizer.convert_tokens_to_ids(tokens)
    segment_ids = [0] * len(tokens)
    mask = [1] * len(tokens)

    # Create an instance
    instance = Instance(
        tokens=token_ids,
        segment_ids=segment_ids,
        mask=mask
    )

    # Predict embeddings
    embeddings = model.forward(instance).get("embedding")

    return embeddings