ELECTRA

Introduction

ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. 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. At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the SQuAD 2.0 dataset.

For a detailed description and experimental results, please refer to our paper ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

This repository contains code to pre-train ELECTRA, including small ELECTRA models on a single GPU. It also supports fine-tuning ELECTRA on downstream tasks including classification tasks (e.g,. GLUE), QA tasks (e.g., SQuAD), and sequence tagging tasks (e.g., text chunking).

Released Models

We are initially releasing three pre-trained models:

Model	Layers	Hidden Size	Params	GLUE score	Download
ELECTRA-Small	12	256	14M	77.4	link
ELECTRA-Base	12	768	110M	82.7	link
ELECTRA-Large	24	1024	335M	85.2	link

The models were trained on uncased English text. They correspond to ELECTRA-Small++, ELECTRA-Base++, ELECTRA-1.45M in our paper. We hope to release other models, such as multilingual models, in the future.

On GLUE, ELECTRA-Large scores slightly better than ALBERT/XLNET, ELECTRA-Base scores better than BERT-Large, and ELECTRA-Small scores slightly worst than TinyBERT (but uses no distillation). See the expected results section below for detailed performance numbers.

Requirements

• Python 3
• TensorFlow 1.15 (although we hope to support TensorFlow 2.0 at a future date)
• NumPy
• scikit-learn and SciPy (for computing some evaluation metrics).

Pre-training

Use build_pretraining_dataset.py to create a pre-training dataset from a dump of raw text. It has the following arguments:

• --corpus-dir: A directory containing raw text files to turn into ELECTRA examples. A text file can contain multiple documents with empty lines separating them.
• --vocab-file: File defining the wordpiece vocabulary.
• --output-dir: Where to write out ELECTRA examples.
• --max-seq-length: The number of tokens per example (128 by default).
• --num-processes: If >1 parallelize across multiple processes (1 by default)

Use run_pretraining.py to pre-train an ELECTRA model. It has the following arguments:

• --data-dir: a directory where pre-training data, model weights, etc. are stored. By default, the training loads examples from <data-dir>/pretrain_tfrecords and a vocabulary from <data-dir>/vocab.txt.
• --model-name: a name for the model being trained. Model weights will be saved in <data-dir>/models/<model-name> by default.
• --hparams (optional): a JSON dict or path to a JSON file containing model hyperparameters, data paths, etc. See configure_pretraining.py for the supported hyperparameters.

If training is halted, re-running the run_pretraining.py with the same arguments will continue the training where it left off.

Quickstart: Pre-train a small ELECTRA model.

These instructions pre-train a small ELECTRA model (12 layers, 256 hidden size). Unfortunately, the data we used in the paper is not publicly available, so we will use the OpenWebTextCorpus released by Aaron Gokaslan and Vanya Cohen instead. The fully-trained model (~4 days on a v100 GPU) should perform roughly in between GPT and BERT-Base in terms of GLUE performance. By default the model is trained on length-128 sequences, so it is not suitable for running on question answering. See the "expected results" section below for more details on model performance.

Setup

1. Place a vocabulary file in $DATA_DIR/vocab.txt. Our ELECTRA models all used the exact same vocabulary as English uncased BERT, which you can download here.
2. Download the OpenWebText corpus (12G) and extract it (i.e., run tar xf openwebtext.tar.xz). Place it in $DATA_DIR/openwebtext.
3. Run python3 build_openwebtext_pretraining_dataset.py --data-dir $DATA_DIR --num-processes 5. It pre-processes/tokenizes the data and outputs examples as tfrecord files under $DATA_DIR/pretrain_tfrecords. The tfrecords require roughly 30G of disk space.

Pre-training the model.

Run python3 run_pretraining.py --data-dir $DATA_DIR --model-name electra_small_owt to train a small ELECTRA model for 1 million steps on the data. This takes slightly over 4 days on a Tesla V100 GPU. However, the model should achieve decent results after 200k steps (10 hours of training on the v100 GPU).

To customize the training, add --hparams '{"hparam1": value1, "hparam2": value2, ...}' to the run command. --hparams can also be a path to a .json file containing the hyperparameters. Some particularly useful options:

• "debug": true trains a tiny ELECTRA model for a few steps.
• "model_size": one of "small", "base", or "large": determines the size of the model
• "electra_objective": false trains a model with masked language modeling instead of replaced token detection (essentially BERT with dynamic masking and no next-sentence prediction).
• "num_train_steps": n controls how long the model is pre-trained for.
• "pretrain_tfrecords": <paths> determines where the pre-training data is located. Note you need to specify the specific files not just the directory (e.g., <data-dir>/pretrain_tf_records/pretrain_data.tfrecord*)
• "vocab_file": <path> and "vocab_size": n can be used to set a custom wordpiece vocabulary.
• "learning_rate": lr, "train_batch_size": n, etc. can be used to change training hyperparameters
• "model_hparam_overrides": {"hidden_size": n, "num_hidden_layers": m}, etc. can be used to changed the hyperparameters for the underlying transformer (the "model_size" flag sets the default values).

See configure_pretraining.py for the full set of supported hyperparameters.

Evaluating the pre-trained model.

To evaluate the model on a downstream task, see the below finetuning instructions. To evaluate the generator/discriminator on the openwebtext data run python3 run_pretraining.py --data-dir $DATA_DIR --model-name electra_small_owt --hparams '{"do_train": false, "do_eval": true}'. This will print out eval metrics such as the accuracy of the generator and discriminator, and also writing the metrics out to data-dir/model-name/results.

Fine-tuning

Use run_finetuning.py to fine-tune and evaluate an ELECTRA model on a downstream NLP task. It expects three arguments:

• --data-dir: a directory where data, model weights, etc. are stored. By default, the script loads finetuning data from <data-dir>/finetuning_data/<task-name> and a vocabulary from <data-dir>/vocab.txt.
• --model-name: a name of the pre-trained model: the pre-trained weights should exist in data-dir/models/model-name.
• --hparams: a JSON dict containing model hyperparameters, data paths, etc. (e.g., --hparams '{"task_names": ["rte"], "model_size": "base", "learning_rate": 1e-4, ...}'). See configure_pretraining.py for the supported hyperparameters. Instead of a dict, this can also be a path to a .json file containing the hyperparameters. You must specify the "task_names" and "model_size" (see examples below).

By default eval metrics will be saved in data-dir/model-name/results and model weights will be saved in data-dir/model-name/finetuning_models. To customize the training, add --hparams '{"hparam1": value1, "hparam2": value2, ...}' to the run command. Some particularly useful options:

• "debug": true fine-tunes a tiny ELECTRA model for a few steps.
• "task_names": ["task_name"]: specifies the tasks to train on. A list because the codebase nominally supports multi-task learning, (although be warned this has not been thoroughly tested).
• "model_size": one of "small", "base", or "large": determines the size of the model; you must set this to the same size as the pre-trained model.
• "do_train" and "do_eval": train and/or evaluate a model (both are set to true by default). For using "do_eval": true with "do_train": false, you need to specify the init_checkpoint, e.g., python3 run_finetuning.py --data-dir $DATA_DIR --model-name electra_base --hparams '{"model_size": "base", "task_names": ["mnli"], "do_train": false, "do_eval": true, "init_checkpoint": "<data-dir>/models/electra_base/finetuning_models/mnli_model_1"}'
• "num_trials": n: If >1, does multiple fine-tuning/evaluation runs with different random seeds.
• "learning_rate": lr, "train_batch_size": n, etc. can be used to change training hyperparameters.
• "model_hparam_overrides": {"hidden_size": n, "num_hidden_layers": m}, etc. can be used to changed the hyperparameters for the underlying transformer (the "model_size" flag sets the default values).

Setup

Get a pre-trained ELECTRA model either by training your own (see pre-training instructions above), or downloading the release ELECTRA weights and unziping them under $DATA_DIR/models (e.g., you should have a directory$DATA_DIR/models/electra_large if you are using the large model).

Finetune ELECTRA on a GLUE task

Download the GLUE data by running this script. Set up the data by running mv CoLA cola && mv MNLI mnli && mv MRPC mrpc && mv QNLI qnli && mv QQP qqp && mv RTE rte && mv SST-2 sst && mv STS-B sts && mv diagnostic/diagnostic.tsv mnli && mkdir -p $DATA_DIR/finetuning_data && mv * $DATA_DIR/finetuning_data.

Then run run_finetuning.py. For example, to fine-tune ELECTRA-Base on MNLI

python3 run_finetuning.py --data-dir $DATA_DIR --model-name electra_base --hparams '{"model_size": "base", "task_names": ["mnli"]}'

Or fine-tune a small model pre-trained using the above instructions on CoLA.

python3 run_finetuning.py --data-dir $DATA_DIR --model-name electra_small_owt --hparams '{"model_size": "small", "task_names": ["cola"]}'

Finetune ELECTRA on question answering

The code supports SQuAD 1.1 and 2.0, as well as datasets in the 2019 MRQA shared task

• Squad 1.1: Download the train and dev datasets and move them under $DATA_DIR/finetuning_data/squadv1/(train|dev).json
• Squad 2.0: Download the datasets from the SQuAD Website and move them under $DATA_DIR/finetuning_data/squad/(train|dev).json
• MRQA tasks: Download the data from here. Move the data to $DATA_DIR/(newsqa|naturalqs|triviaqa|searchqa)/(train|dev).jsonl.

Then run (for example)

python3 run_finetuning.py --data-dir $DATA_DIR --model-name electra_base --hparams '{"model_size": "base", "task_names": ["squad"]}'

This repository uses the official evaluation code released by the SQuAD authors and the MRQA shared task to compute metrics

Finetune ELECTRA on sequence tagging

Download the CoNLL-2000 text chunking dataset from here and put it under $DATA_DIR/chunk/(train|dev).txt. Then run

python3 run_finetuning.py --data-dir $DATA_DIR --model-name electra_base --hparams '{"model_size": "base", "task_names": ["chunk"]}'

Adding a new task

The easiest way to run on a new task is to implement a new finetune.task.Task, add it to finetune.task_builder.py, and then use run_finetuning.py as normal. For classification/qa/sequence tagging, you can inherit from a finetune.classification.classification_tasks.ClassificationTask, finetune.qa.qa_tasks.QATask, or finetune.tagging.tagging_tasks.TaggingTask. For preprocessing data, we use the same tokenizer as BERT.

Expected Results

Here are expected results for ELECTRA on various tasks. Note that variance in fine-tuning can be quite large, so for some tasks you may see big fluctuations in scores when fine-tuning from the same checkpoint multiple times. The below scores show median performance over a large number of random seeds. ELECTRA-Small/Base/Large are our released models. ELECTRA-Small-OWT is the OpenWebText-trained model from above (it performs a bit worse than ELECTRA-Small due to being trained for less time and on a smaller dataset).

	CoLA	SST	MRPC	STS	QQP	MNLI	QNLI	RTE	SQuAD 1.1	SQuAD 2.0	Chunking
Metrics	MCC	Acc	Acc	Spearman	Acc	Acc	Acc	Acc	EM	EM	F1
ELECTRA-Large	69.1	96.9	90.8	92.6	92.4	90.9	95.0	88.0	89.7	88.1	97.2
ELECTRA-Base	67.7	95.1	89.5	91.2	91.5	88.8	93.2	82.7	86.8	83.7	97.1
ELECTRA-Small	57.0	91.2	88.0	87.5	89.0	81.3	88.4	66.7	75.8	70.1	96.5
ELECTRA-Small-OWT	56.8	88.3	87.4	86.8	88.3	78.9	87.9	68.5	--	--	--

Citation

If you use this code for your publication, please cite the original paper:

@inproceedings{clark2019electra,
  title = {{ELECTRA}: Pre-training Text Encoders as Discriminators Rather Than Generators},
  author = {Kevin Clark and Minh-Thang Luong and and Quoc V. Le and Christopher D. Manning},
  booktitle = {ICLR},
  year = {2020}
}

Contact Info

For help or issues using ELECTRA, please submit a GitHub issue.

For personal communication related to ELECTRA, please contact Kevin Clark (kevclark@cs.stanford.edu).