For a detailed description and experimental results, please refer to our paper This repository contains code to pre-train ELECTRA, including small ELECTRA models on a single GPU.

The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. In “ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators”, we take a different approach to language pre-training that provides the benefits of BERT but learns far more efficiently. At large scale, ELECTRA achieves state-of-the-art results on the SQuAD 2.0dataset. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators It also supports fine-tuning ELECTRA on downstream tasks including classification tasks (e.g,. They correspond to ELECTRA-Small++, ELECTRA-Base++, ELECTRA-1.75M in our paper. After pre-training the generator and ELECTRA in conjunction with the above-described process, the generator can be thanked for its service and be discarded. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens.

For a detailed description an… From this viewpoint, we train ELECTRA models of various sizes and evaluate their downstream performance vs. their

Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network.

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Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Note that variance in fine-tuning can be If you use this code for your publication, please cite the original paper:For help or issues using ELECTRA, please submit a GitHub issue.For personal communication related to ELECTRA, please contact ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN. 2. ELECTRA is a new pre-training approach which trains two transformer models: the generator and the discriminator. We hope to release other models, such as multilingual models, in the future.You can continue pre-training from the released ELECTRA checkpoints byThese instructions pre-train a small ELECTRA model (12 layers, 256 hidden size). ELECTRA is a new method for self-supervised language representation learning.

While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. ICLR 2020 • Kevin Clark • Minh-Thang Luong • Quoc V. Le • Christopher D. Manning. We are initially releasing three pre-trained models:The models were trained on uncased English text. Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens.

While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. Use Git or checkout with SVN using the web URL. The generator’s role is to replace tokens in a sequence, and is therefore trained as a masked language model.

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