electra small discriminator

Introduction

ELECTRA is a method for self-supervised language representation learning, involving the pre-training of transformer networks. Unlike traditional generators, ELECTRA trains models to discern between "real" and "fake" input tokens, akin to the discriminator function in Generative Adversarial Networks (GANs). This approach is efficient in terms of computational resources and has demonstrated strong performance on tasks like SQuAD 2.0.

Architecture

ELECTRA's architecture involves a discriminator that evaluates whether each input token is "real" or "fake." The training process mirrors that of GANs, where the discriminator learns by identifying discrepancies between real and synthesized tokens. This method allows for effective training even on smaller scales, achieving state-of-the-art results without extensive computational demands.

Training

Training involves pre-training the ELECTRA models using a process that does not rely heavily on computational resources, making it feasible on a single GPU. Post pre-training, the models can be fine-tuned for various tasks, including classification (e.g., GLUE), question answering (e.g., SQuAD), and sequence tagging (e.g., text chunking).

Guide: Running Locally

1. Installation: Ensure you have Python and PyTorch installed.
2. Model Loading: Use the transformers library to load the discriminator and tokenizer:

   from transformers import ElectraForPreTraining, ElectraTokenizerFast
   discriminator = ElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
   tokenizer = ElectraTokenizerFast.from_pretrained("google/electra-small-discriminator")
   

3. Tokenization and Prediction: Tokenize input sentences and use the discriminator to predict the authenticity of tokens.
4. GPU Usage: For optimal performance, a cloud-based GPU such as those from AWS, Google Cloud, or Azure is recommended.

License

The model and code are licensed under the Apache License 2.0, allowing free use, modification, and distribution under the same license terms.