Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/google-research/ALBERT

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/google-research/ALBERT
Last synced: 3 months ago
JSON representation
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Host: GitHub
URL: https://github.com/google-research/ALBERT
Owner: google-research
License: apache-2.0
Archived: true
Created: 2019-11-26T22:23:13.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2023-04-14T18:02:55.000Z (over 1 year ago)
Last Synced: 2024-08-01T15:34:52.624Z (3 months ago)
Language: Python
Homepage:
Size: 289 KB
Stars: 3,224
Watchers: 71
Forks: 571
Open Issues: 102
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project

StarryDivineSky - google-research/ALBERT
README

        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

```