https://github.com/antonmu/albert

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

https://github.com/antonmu/albert

Repository

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

https://github.com/AntonMu/albert/blob/master/

ALBERT
======

***************New March 28, 2020 ***************

Add a colab [tutorial](https://github.com/google-research/albert/blob/master/albert_glue_fine_tuning_tutorial.ipynb) to run fine-tuning for GLUE datasets.

***************New January 7, 2020 ***************

v2 TF-Hub models should be working now with TF 1.15, as we removed the
native Einsum op from the graph. See updated TF-Hub links below.

***************New December 30, 2019 ***************

Chinese models are released. We would like to thank [CLUE team ](https://github.com/CLUEbenchmark/CLUE) for providing the training data.

- [Base](https://storage.googleapis.com/albert_models/albert_base_zh.tar.gz)
- [Large](https://storage.googleapis.com/albert_models/albert_large_zh.tar.gz)
- [Xlarge](https://storage.googleapis.com/albert_models/albert_xlarge_zh.tar.gz)
- [Xxlarge](https://storage.googleapis.com/albert_models/albert_xxlarge_zh.tar.gz)

Version 2 of ALBERT models is released.

- Base: [[Tar file](https://storage.googleapis.com/albert_models/albert_base_v2.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_base/3)]
- Large: [[Tar file](https://storage.googleapis.com/albert_models/albert_large_v2.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_large/3)]
- Xlarge: [[Tar file](https://storage.googleapis.com/albert_models/albert_xlarge_v2.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_xlarge/3)]
- Xxlarge: [[Tar file](https://storage.googleapis.com/albert_models/albert_xxlarge_v2.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_xxlarge/3)]

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](https://arxiv.org/pdf/1909.11942.pdf) (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](https://arxiv.org/abs/1909.11942)

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:

- Base: [[Tar file](https://storage.googleapis.com/albert_models/albert_base_v1.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_base/1)]
- Large: [[Tar file](https://storage.googleapis.com/albert_models/albert_large_v1.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_large/1)]
- Xlarge: [[Tar file](https://storage.googleapis.com/albert_models/albert_xlarge_v1.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_xlarge/1)]
- Xxlarge: [[Tar file](https://storage.googleapis.com/albert_models/albert_xxlarge_v1.tar.gz)] [[TF-Hub](https://tfhub.dev/google/albert_xxlarge/1)]

Example usage of the TF-Hub module in code:

```
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"]
```

Most of the fine-tuning scripts in this repository support TF-hub modules
via the `--albert_hub_module_handle` flag.

Pre-training Instructions
=========================
To pretrain ALBERT, use `run_pretraining.py`:

```
pip install -r albert/requirements.txt
python -m albert.run_pretraining \
    --input_file=... \
    --output_dir=... \
    --init_checkpoint=... \
    --albert_config_file=... \
    --do_train \
    --do_eval \
    --train_batch_size=4096 \
    --eval_batch_size=64 \
    --max_seq_length=512 \
    --max_predictions_per_seq=20 \
    --optimizer='lamb' \
    --learning_rate=.00176 \
    --num_train_steps=125000 \
    --num_warmup_steps=3125 \
    --save_checkpoints_steps=5000
```

Fine-tuning on GLUE
===================
To fine-tune and evaluate a pretrained ALBERT on GLUE, please see the
convenience script `run_glue.sh`.

Lower-level use cases may want to use the `run_classifier.py` script directly.
The `run_classifier.py` script is used both for fine-tuning and evaluation of
ALBERT on individual GLUE benchmark tasks, such as MNLI:

```
pip install -r albert/requirements.txt
python -m albert.run_classifier \
  --data_dir=... \
  --output_dir=... \
  --init_checkpoint=... \
  --albert_config_file=... \
  --spm_model_file=... \
  --do_train \
  --do_eval \
  --do_predict \
  --do_lower_case \
  --max_seq_length=128 \
  --optimizer=adamw \
  --task_name=MNLI \
  --warmup_step=1000 \
  --learning_rate=3e-5 \
  --train_step=10000 \
  --save_checkpoints_steps=100 \
  --train_batch_size=128
```

Good default flag values for each GLUE task can be found in `run_glue.sh`.

You can fine-tune the model starting from TF-Hub modules instead of raw
checkpoints by setting e.g.
`--albert_hub_module_handle=https://tfhub.dev/google/albert_base/1` instead
of `--init_checkpoint`.

You can find the spm_model_file in the tar files or under the assets folder of
the tf-hub module. The name of the model file is "30k-clean.model".

After evaluation, the script should report some output like this:

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

Fine-tuning on SQuAD
====================
To fine-tune and evaluate a pretrained model on SQuAD v1, use the
`run_squad_v1.py` script:

```
pip install -r albert/requirements.txt
python -m albert.run_squad_v1 \
  --albert_config_file=... \
  --output_dir=... \
  --train_file=... \
  --predict_file=... \
  --train_feature_file=... \
  --predict_feature_file=... \
  --predict_feature_left_file=... \
  --init_checkpoint=... \
  --spm_model_file=... \
  --do_lower_case \
  --max_seq_length=384 \
  --doc_stride=128 \
  --max_query_length=64 \
  --do_train=true \
  --do_predict=true \
  --train_batch_size=48 \
  --predict_batch_size=8 \
  --learning_rate=5e-5 \
  --num_train_epochs=2.0 \
  --warmup_proportion=.1 \
  --save_checkpoints_steps=5000 \
  --n_best_size=20 \
  --max_answer_length=30
```

You can fine-tune the model starting from TF-Hub modules instead of raw
checkpoints by setting e.g.
`--albert_hub_module_handle=https://tfhub.dev/google/albert_base/1` instead
of `--init_checkpoint`.

For SQuAD v2, use the `run_squad_v2.py` script:

```
pip install -r albert/requirements.txt
python -m albert.run_squad_v2 \
  --albert_config_file=... \
  --output_dir=... \
  --train_file=... \
  --predict_file=... \
  --train_feature_file=... \
  --predict_feature_file=... \
  --predict_feature_left_file=... \
  --init_checkpoint=... \
  --spm_model_file=... \
  --do_lower_case \
  --max_seq_length=384 \
  --doc_stride=128 \
  --max_query_length=64 \
  --do_train \
  --do_predict \
  --train_batch_size=48 \
  --predict_batch_size=8 \
  --learning_rate=5e-5 \
  --num_train_epochs=2.0 \
  --warmup_proportion=.1 \
  --save_checkpoints_steps=5000 \
  --n_best_size=20 \
  --max_answer_length=30
```

You can fine-tune the model starting from TF-Hub modules instead of raw
checkpoints by setting e.g.
`--albert_hub_module_handle=https://tfhub.dev/google/albert_base/1` instead
of `--init_checkpoint`.

Fine-tuning on RACE
===================
For RACE, use the `run_race.py` script:

```
pip install -r albert/requirements.txt
python -m albert.run_race \
  --albert_config_file=... \
  --output_dir=... \
  --train_file=... \
  --eval_file=... \
  --data_dir=...\
  --init_checkpoint=... \
  --spm_model_file=... \
  --max_seq_length=512 \
  --max_qa_length=128 \
  --do_train \
  --do_eval \
  --train_batch_size=32 \
  --eval_batch_size=8 \
  --learning_rate=1e-5 \
  --train_step=12000 \
  --warmup_step=1000 \
  --save_checkpoints_steps=100
```

You can fine-tune the model starting from TF-Hub modules instead of raw
checkpoints by setting e.g.
`--albert_hub_module_handle=https://tfhub.dev/google/albert_base/1` instead
of `--init_checkpoint`.

SentencePiece
=============
Command for generating the sentence piece vocabulary:

```
spm_train \
--input all.txt --model_prefix=30k-clean --vocab_size=30000 --logtostderr
--pad_id=0 --unk_id=1 --eos_id=-1 --bos_id=-1
--control_symbols=[CLS],[SEP],[MASK]
--user_defined_symbols="(,),\",-,.,,,"
--shuffle_input_sentence=true --input_sentence_size=10000000
--character_coverage=0.99995 --model_type=unigram
```

Owner

GitHub Events