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Mbedder: A pytorch powered framework for seemlessly adding contextual text embeddings from pretrained models

Project description

Mbedder is a language framework for adding contextual embeddings of pretrained language models to deep learning models.Mbedder is powered by PyTorch and HuggingFace and requires as less as 1 line of code to add embeddings and works similar to how the Embedding Layer works in PyTorch.

List of supported architectures

  • Bert
  • XLNet
  • Albert
  • TransfoXL
  • DistilBert
  • Roberta
  • XLM
  • XLMRoberta
  • GPT
  • GPT2
  • Flaubert

The pretrained models for the mentioned architecures can be found here.


  • Addition of embeddings with 1 line of code
  • Embeddings can output Sentence as well as Token level embeddings
  • Task specific combination strategies can be applied to hidden states and token embeddings
  • Custom pre-trained hugging face transformer models can be used with Mbedder.

Requirements and Installation

  • PyTorch version >= 1.6.0
  • Python version >= 3.6
  • Transformer >= 3.0.2

Mbedder can be using Pip as follows

pip install Mbedder

Getting Started

A basic example of using a Mbedder Bert embedding is shown below:

import torch
from Mbedder import BertEmbedding

class BertClassifier(torch.nn.Module):
    def __init__(self, num_classes):
        super(BertClassifier, self).__init__()
        self.embedding = BertEmbedding.from_pretrained('bert-base-uncased')
        self.fc = torch.nn.Linear(self.embedding.embedding_size, num_classes)
    def forward(self, input_ids, attention_mask):
        x = self.embedding(input_ids, attention_mask, output_token_embeddings=False)
        logits = self.fc(x[0])
        return logits

More advanced examples can be found in the examples folder.


Mbedder is MIT-licensed.

Project details

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