ALBERT

***************New October 31, 2019 ***************

Version 2 of ALBERT models is released. TF-Hub modules are available:

• https://tfhub.dev/google/albert_base/2
• https://tfhub.dev/google/albert_large/2
• https://tfhub.dev/google/albert_xlarge/2
• https://tfhub.dev/google/albert_xxlarge/2

In this version, we apply 'no dropout', 'additional training data' and 'long training time' strategies to all models. We train ALBERT-base for 10M steps and other models for 3M steps.

The result comparison to the v1 models is as followings:

	Average	SQuAD1.1	SQuAD2.0	MNLI	SST-2	RACE
V2
ALBERT-base	82.3	90.2/83.2	82.1/79.3	84.6	92.9	66.8
ALBERT-large	85.7	91.8/85.2	84.9/81.8	86.5	94.9	75.2
ALBERT-xlarge	87.9	92.9/86.4	87.9/84.1	87.9	95.4	80.7
ALBERT-xxlarge	90.9	94.6/89.1	89.8/86.9	90.6	96.8	86.8
V1
ALBERT-base	80.1	89.3/82.3	80.0/77.1	81.6	90.3	64.0
ALBERT-large	82.4	90.6/83.9	82.3/79.4	83.5	91.7	68.5
ALBERT-xlarge	85.5	92.5/86.1	86.1/83.1	86.4	92.4	74.8
ALBERT-xxlarge	91.0	94.8/89.3	90.2/87.4	90.8	96.9	86.5

The comparison shows that for ALBERT-base, ALBERT-large, and ALBERT-xlarge, v2 is much better than v1, indicating the importance of applying the above three strategies. On average, ALBERT-xxlarge is slightly worse than the v1, because of the following two reasons: 1) Training additional 1.5 M steps (the only difference between these two models is training for 1.5M steps and 3M steps) did not lead to significant performance improvement. 2) For v1, we did a little bit hyperparameter search among the parameters sets given by BERT, Roberta, and XLnet. For v2, we simply adopt the parameters from v1 except for RACE, where we use a learning rate of 1e-5 and 0 ALBERT DR (dropout rate for ALBERT in finetuning). The original (v1) RACE hyperparameter will cause model divergence for v2 models. Given that the downstream tasks are sensitive to the fine-tuning hyperparameters, we should be careful about so called slight improvements.

ALBERT is "A Lite" version of BERT, a popular unsupervised language representation learning algorithm. ALBERT uses parameter-reduction techniques that allow for large-scale configurations, overcome previous memory limitations, and achieve better behavior with respect to model degradation.

For a technical description of the algorithm, see our paper:

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut

Release Notes

• Initial release: 10/9/2019

Results

Performance of ALBERT on GLUE benchmark results using a single-model setup on dev:

Models	MNLI	QNLI	QQP	RTE	SST	MRPC	CoLA	STS
BERT-large	86.6	92.3	91.3	70.4	93.2	88.0	60.6	90.0
XLNet-large	89.8	93.9	91.8	83.8	95.6	89.2	63.6	91.8
RoBERTa-large	90.2	94.7	92.2	86.6	96.4	90.9	68.0	92.4
ALBERT (1M)	90.4	95.2	92.0	88.1	96.8	90.2	68.7	92.7
ALBERT (1.5M)	90.8	95.3	92.2	89.2	96.9	90.9	71.4	93.0

Performance of ALBERT-xxl on SQuaD and RACE benchmarks using a single-model setup:

Models	SQuAD1.1 dev	SQuAD2.0 dev	SQuAD2.0 test	RACE test (Middle/High)
BERT-large	90.9/84.1	81.8/79.0	89.1/86.3	72.0 (76.6/70.1)
XLNet	94.5/89.0	88.8/86.1	89.1/86.3	81.8 (85.5/80.2)
RoBERTa	94.6/88.9	89.4/86.5	89.8/86.8	83.2 (86.5/81.3)
UPM	-	-	89.9/87.2	-
XLNet + SG-Net Verifier++	-	-	90.1/87.2	-
ALBERT (1M)	94.8/89.2	89.9/87.2	-	86.0 (88.2/85.1)
ALBERT (1.5M)	94.8/89.3	90.2/87.4	90.9/88.1	86.5 (89.0/85.5)

Pre-trained Models

TF-Hub modules are available:

• https://tfhub.dev/google/albert_base/1
• https://tfhub.dev/google/albert_large/1
• https://tfhub.dev/google/albert_xlarge/1
• https://tfhub.dev/google/albert_xxlarge/1

Example usage of the TF-Hub module:

tags = set()
if is_training:
  tags.add("train")
albert_module = hub.Module("https://tfhub.dev/google/albert_base/1", tags=tags,
                           trainable=True)
albert_inputs = dict(
    input_ids=input_ids,
    input_mask=input_mask,
    segment_ids=segment_ids)
albert_outputs = albert_module(
    inputs=albert_inputs,
    signature="tokens",
    as_dict=True)

# If you want to use the token-level output, use
# albert_outputs["sequence_output"] instead.
output_layer = albert_outputs["pooled_output"]

For a full example, see run_classifier_with_tfhub.py.

Pre-training Instructions

Use run_pretraining.py to pretrain ALBERT:

pip install -r albert/requirements.txt
python -m albert.run_pretraining \
    --output_dir="${OUTPUT_DIR}" \
    --do_train \
    --do_eval \
    <additional flags>

Fine-tuning Instructions

For XNLI, COLA, MNLI, and MRPC, use run_classifier_sp.py:

pip install -r albert/requirements.txt
python -m albert.run_classifier \
  --albert_config_file=albert_config.json \
  --init_checkpoint=/path/to/ckpt \
  --task_name=MNLI \
  <additional flags>

You should see some output like this:

***** Eval results *****
  global_step = ...
  loss = ...
  masked_lm_accuracy = ...
  masked_lm_loss = ...
  sentence_order_accuracy = ...
  sentence_order_loss = ...

You can also fine-tune the model starting from TF-Hub modules:

pip install -r albert/requirements.txt
python -m albert.run_classifier \
  --albert_hub_module_handle=https://tfhub.dev/google/albert_base/1 \
  --task_name=MNLI \
  <additional flags>