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shoarora/electra-small-owt
last 30 days

pytorch

tf

#### Contributed by

How to use this model directly from the 🤗/transformers library:



tokenizer = AutoTokenizer.from_pretrained("shoarora/electra-small-owt")


Update on GitHub

# ELECTRA-small-OWT

This is an unnoficial implementation of an ELECTRA small model, trained on the OpenWebText corpus.

Differences from official ELECTRA models:

• we use a BertForMaskedLM as the generator and BertForTokenClassification as the discriminator
• they use an embedding projection layer, but Bert doesn't have one

(figure from Clark et al. 2020)

ELECTRA uses discriminative LM / replaced-token-detection for pretraining. This involves a generator (a Masked LM model) creating examples for a discriminator to classify as original or replaced for each token.

## Usage

from transformers import BertForSequenceClassification, BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
electra = BertForSequenceClassification.from_pretrained('shoarora/electra-small-owt')

## Code

The pytorch module that implements this task is available here.

Further implementation information here, and here is the script that created this model.

This specific model was trained with the following params:

• batch_size: 512
• training_steps: 5e5
• warmup_steps: 4e4
• learning_rate: 2e-3

#### GLUE Dev results

Model # Params CoLA SST MRPC STS QQP MNLI QNLI RTE
ELECTRA-Small++ 14M 57.0 91. 88.0 87.5 89.0 81.3 88.4 66.7
ELECTRA-Small-OWT 14M 56.8 88.3 87.4 86.8 88.3 78.9 87.9 68.5
ELECTRA-Small-OWT (ours) 17M 56.3 88.4 75.0 86.1 89.1 77.9 83.0 67.1
ALECTRA-Small-OWT (ours) 4M 50.6 89.1 86.3 87.2 89.1 78.2 85.9 69.6

#### GLUE Test results

Model # Params CoLA SST MRPC STS QQP MNLI QNLI RTE
BERT-Base 110M 52.1 93.5 84.8 85.9 89.2 84.6 90.5 66.4
GPT 117M 45.4 91.3 75.7 80.0 88.5 82.1 88.1 56.0
ELECTRA-Small++ 14M 57.0 91.2 88.0 87.5 89.0 81.3 88.4 66.7
ELECTRA-Small-OWT (ours) 17M 57.4 89.3 76.2 81.9 87.5 78.1 82.4 68.1
ALECTRA-Small-OWT (ours) 4M 43.9 87.9 82.1 82.0 87.6 77.9 85.8 67.5