# Pre-trained Models

⚠️ Disclaimer: Checkpoints are based on training with publicly available datasets.
Some datasets contain limitations, including non-commercial use limitations. Please review the terms and conditions made available by third parties before using
the datasets provided. Checkpoints are licensed under
[Apache 2.0](https://github.com/tensorflow/models/blob/master/LICENSE).

⚠️ Disclaimer: Datasets hyperlinked from this page are not owned or distributed
by Google. Such datasets are made available by third parties. Please review the
terms and conditions made available by the third parties before using the data.

We provide a large collection of baselines and checkpoints for NLP pre-trained
models.

## How to Load Pretrained Models

### How to Initialize from Checkpoint

**Note:** TF-HUB/Savedmodel is the preferred way to distribute models as it is
self-contained. Please consider using TF-HUB for finetuning tasks first.

If you use the [NLP training library](train.md),
you can specify the checkpoint path link directly when launching your job. For
example, to initialize the model from the checkpoint, you can specify
`--params_override=task.init_checkpoint=PATH_TO_INIT_CKPT` as:

```
python3 train.py \
 --params_override=task.init_checkpoint=PATH_TO_INIT_CKPT
```

### How to load TF-HUB SavedModel

Finetuning tasks such as question answering (SQuAD) and sentence
prediction (GLUE) support loading a model from TF-HUB. These built-in tasks
support a specific `task.hub_module_url` parameter. To set this parameter,
replace `--params_override=task.init_checkpoint=...` with
`--params_override=task.hub_module_url=TF_HUB_URL`, like below:

```
python3 train.py \
 --params_override=task.hub_module_url=https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/3
```

## BERT

Public BERT pre-trained models released by the BERT authors.

We released both checkpoints and tf.hub modules as the pretrained models for
fine-tuning. They are TF 2.x compatible and are converted from the checkpoints
released in TF 1.x official BERT repository
[google-research/bert](https://github.com/google-research/bert)
in order to keep consistent with BERT paper.

### Checkpoints

Model                                    | Configuration                | Training Data | Checkpoint & Vocabulary | TF-HUB SavedModels
---------------------------------------- | :--------------------------: | ------------: | ----------------------: | ------:
BERT-base uncased English                | uncased_L-12_H-768_A-12      | Wiki + Books  | [uncased_L-12_H-768_A-12](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/uncased_L-12_H-768_A-12.tar.gz) | [`BERT-Base, Uncased`](https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/)
BERT-base cased English                  | cased_L-12_H-768_A-12        | Wiki + Books  | [cased_L-12_H-768_A-12](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/cased_L-12_H-768_A-12.tar.gz) | [`BERT-Base, Cased`](https://tfhub.dev/tensorflow/bert_en_cased_L-12_H-768_A-12/)
BERT-large uncased English               | uncased_L-24_H-1024_A-16     | Wiki + Books  | [uncased_L-24_H-1024_A-16](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/uncased_L-24_H-1024_A-16.tar.gz) | [`BERT-Large, Uncased`](https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/)
BERT-large cased English                  | cased_L-24_H-1024_A-16       | Wiki + Books  | [cased_L-24_H-1024_A-16](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/cased_L-24_H-1024_A-16.tar.gz) | [`BERT-Large, Cased`](https://tfhub.dev/tensorflow/bert_en_cased_L-24_H-1024_A-16/)
BERT-large, Uncased (Whole Word Masking) | wwm_uncased_L-24_H-1024_A-16 | Wiki + Books  | [wwm_uncased_L-24_H-1024_A-16](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/wwm_uncased_L-24_H-1024_A-16.tar.gz) | [`BERT-Large, Uncased (Whole Word Masking)`](https://tfhub.dev/tensorflow/bert_en_wwm_uncased_L-24_H-1024_A-16/)
BERT-large, Cased (Whole Word Masking)   | wwm_cased_L-24_H-1024_A-16   | Wiki + Books  | [wwm_cased_L-24_H-1024_A-16](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/wwm_cased_L-24_H-1024_A-16.tar.gz) | [`BERT-Large, Cased (Whole Word Masking)`](https://tfhub.dev/tensorflow/bert_en_wwm_cased_L-24_H-1024_A-16/)
BERT-base MultiLingual                   | multi_cased_L-12_H-768_A-12  | Wiki + Books  | [multi_cased_L-12_H-768_A-12](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/multi_cased_L-12_H-768_A-12.tar.gz) | [`BERT-Base, Multilingual Cased`](https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/)
BERT-base Chinese                        | chinese_L-12_H-768_A-12      | Wiki + Books  | [chinese_L-12_H-768_A-12](https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/chinese_L-12_H-768_A-12.tar.gz) | [`BERT-Base, Chinese`](https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/)

You may explore more in the TF-Hub BERT collection:
https://tfhub.dev/google/collections/bert/1

### BERT variants

We also have pretrained BERT models with variants in both network architecture
and training methodologies. These models achieve higher downstream accuracy
scores.

Model                            | Configuration            | Training Data            | TF-HUB SavedModels                                                                    | Comment
-------------------------------- | :----------------------: | -----------------------: | ------------------------------------------------------------------------------------: | ------:
BERT-base talking heads + ggelu  | uncased_L-12_H-768_A-12  | Wiki + Books   | [talkheads_ggelu_base](https://tfhub.dev/tensorflow/talkheads_ggelu_bert_en_base/1)   | BERT-base trained with [talking heads attention](https://arxiv.org/abs/2003.02436) and [gated GeLU](https://arxiv.org/abs/2002.05202).
BERT-large talking heads + ggelu | uncased_L-24_H-1024_A-16 | Wiki + Books  | [talkheads_ggelu_large](https://tfhub.dev/tensorflow/talkheads_ggelu_bert_en_large/1) | BERT-large trained with [talking heads attention](https://arxiv.org/abs/2003.02436) and [gated GeLU](https://arxiv.org/abs/2002.05202).
LAMBERT-large uncased English    | uncased_L-24_H-1024_A-16 | Wiki + Books  | [lambert](https://tfhub.dev/tensorflow/lambert_en_uncased_L-24_H-1024_A-16/1)         | BERT trained with LAMB and techniques from RoBERTa.

## ALBERT

The academic paper that describes ALBERT in detail and provides full results on
a number of tasks can be found here: https://arxiv.org/abs/1909.11942.

We released both checkpoints and tf.hub modules as the pretrained models for
fine-tuning. They are TF 2.x compatible and are converted from the ALBERT v2
checkpoints released in the TF 1.x official ALBERT repository
[google-research/albert](https://github.com/google-research/albert)
in order to be consistent with the ALBERT paper.

Our current released checkpoints are exactly the same as the TF 1.x official
ALBERT repository.

### Checkpoints

Model                                    | Training Data | Checkpoint & Vocabulary | TF-HUB SavedModels
---------------------------------------- | ------------: | ----------------------: | ------:
ALBERT-base English               |  Wiki + Books  | [`ALBERT Base`](https://storage.googleapis.com/tf_model_garden/nlp/albert/albert_base.tar.gz) | https://tfhub.dev/tensorflow/albert_en_base/3
ALBERT-large English               |  Wiki + Books  | [`ALBERT Large`](https://storage.googleapis.com/tf_model_garden/nlp/albert/albert_large.tar.gz) | https://tfhub.dev/tensorflow/albert_en_large/3
ALBERT-xlarge English               |  Wiki + Books  | [`ALBERT XLarge`](https://storage.googleapis.com/tf_model_garden/nlp/albert/albert_xlarge.tar.gz) | https://tfhub.dev/tensorflow/albert_en_xlarge/3
ALBERT-xxlarge English               |  Wiki + Books  | [`ALBERT XXLarge`](https://storage.googleapis.com/tf_model_garden/nlp/albert/albert_xxlarge.tar.gz) | https://tfhub.dev/tensorflow/albert_en_xxlarge/3


## ELECTRA

[ELECTRA](https://arxiv.org/abs/2003.10555), which stands for " Efficiently
Learning an Encoder that Classifies Token Replacements Accurately", is an
efficient language pretraining method. In a nutshell, ELECTRA contains two
transformer models, one called "generator" and the other called "discriminator".
Given a masked sequence, the generator replaces words in masked positions with
randomly generated words. The discriminator then takes the corrupted sentence as
input and predicts whether each word is replaced by the generator or not. During
the pretraining stage, ELECTRA jointly learns two models (i.e., trains the
generator using masked language modeling (MLM) task, and trains the
discriminator using replaced token detection (RTD) task). At the fine-tuning
stage, the generator is discard and the discriminator is used for downstream
tasks (e.g., GLUE and SQuAD tasks).

### Checkpoints

The checkpoints are re-trained with the Electra code in this repository.

Model                                    | Training Data | Checkpoint & Vocabulary
---------------------------------------- | ------------: | ----------------------:
ELECTRA-small English               |  Wiki + Books  | [`ELECTRA Small`](https://storage.googleapis.com/tf_model_garden/nlp/electra/small.tar.gz): the vocabulary is the same as BERT uncased English.
ELECTRA-base English               |  Wiki + Books  | [`ELECTRA Base`](https://storage.googleapis.com/tf_model_garden/nlp/electra/base.tar.gz): the vocabulary is the same as BERT uncased English.
