# TF-NLP Model Garden

## Introduction

The TF-NLP library provides a collection of scripts for training and
evaluating transformer-based models, on various tasks such as sentence
classification, question answering, and translation. Additionally, we provide
checkpoints of pretrained models which can be finetuned on downstream tasks.

⚠️ Disclaimer: Checkpoints are based on training with publicly available
datasets. Some datasets contain limitations, including non-commercial use
limitations. Please review terms and conditions made available by third parties
before using models and datasets provided.

### How to Train Models

Model Garden can be easily installed with
`pip install tf-models-nightly`. After installation, check out
[this instruction](https://github.com/tensorflow/models/blob/master/official/nlp/docs/train.md)
on how to train models with this codebase.


By default, the experiment runs on GPUs. To run on TPUs, one should overwrite
`runtime.distribution_strategy` and set the tpu address. See [RuntimeConfig](https://github.com/tensorflow/models/blob/master/official/core/config_definitions.py) for details.

In general, the experiments can run with the folloing command by setting the
corresponding `${TASK}`, `${TASK_CONFIG}`, `${MODEL_CONFIG}`.
```
EXPERIMENT=???
TASK_CONFIG=???
MODEL_CONFIG=???
EXRTRA_PARAMS=???
MODEL_DIR=???  # a-folder-to-hold-checkpoints-and-logs
python3 train.py \
  --experiment=${EXPERIMENT} \
  --mode=train_and_eval \
  --model_dir=${MODEL_DIR} \ 
  --config_file=${TASK_CONFIG} \
  --config_file=${MODEL_CONFIG} \
  --params_override=${EXRTRA_PARAMS}
``` 

* `EXPERIMENT` can be found under `configs/`
* `TASK_CONFIG` can be found under `configs/experiments/`
* `MODEL_CONFIG` can be found under `configs/models/`

#### Order of params override:
1. `train.py` looks up the registered `ExperimentConfig` with `${EXPERIMENT}`
2. Overrides params in `TaskConfig` in `${TASK_CONFIG}`
3. Overrides params `model` in `TaskConfig` with `${MODEL_CONFIG}`
4. Overrides any params in `ExperimentConfig` with `${EXTRA_PARAMS}`

Note that 
1. `${TASK_CONFIG}`, `${MODEL_CONFIG}`, `${EXTRA_PARAMS}` can be optional when EXPERIMENT default is enough.
2. `${TASK_CONFIG}`, `${MODEL_CONFIG}`, `${EXTRA_PARAMS}` are only guaranteed to be compatible to it's `${EXPERIMENT}` that defines it.

## Experiments

| NAME          | EXPERIMENT                     | TASK_CONFIG  | MODEL_CONFIG | EXRTRA_PARAMS |
| ----------------- | ------------------------ | ------- | -------- | ----------- |
| BERT-base GLUE/MNLI-matched finetune | [bert/sentence_prediction](https://github.com/tensorflow/models/blob/master/official/nlp/configs/finetuning_experiments.py) | [glue_mnli_matched.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/experiments/glue_mnli_matched.yaml) | [bert_en_uncased_base.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/models/bert_en_uncased_base.yaml) | <details> <summary>data and bert-base hub init</summary>task.train_data.input_path=/path-to-your-training-data,task.validation_data.input_path=/path-to-your-val-data,task.hub_module_url=https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4 </details> |
| BERT-base GLUE/MNLI-matched finetune | [bert/sentence_prediction](https://github.com/tensorflow/models/blob/master/official/nlp/configs/finetuning_experiments.py) | [glue_mnli_matched.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/experiments/glue_mnli_matched.yaml) | [bert_en_uncased_base.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/models/bert_en_uncased_base.yaml) | <details> <summary>data and bert-base ckpt init</summary>task.train_data.input_path=/path-to-your-training-data,task.validation_data.input_path=/path-to-your-val-data,task.init_checkpoint=gs://tf_model_garden/nlp/bert/uncased_L-12_H-768_A-12/bert_model.ckpt </details> |
| BERT-base SQuAD v1.1 finetune        | [bert/squad](https://github.com/tensorflow/models/blob/master/official/nlp/configs/finetuning_experiments.py)               | [squad_v1.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/experiments/squad_v1.yaml) | [bert_en_uncased_base.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/models/bert_en_uncased_base.yaml) | <details> <summary>data and bert-base hub init</summary>task.train_data.input_path=/path-to-your-training-data,task.validation_data.input_path=/path-to-your-val-data,task.hub_module_url=https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4 </details> |
|ALBERT-base SQuAD v1.1 finetune | [bert/squad](https://github.com/tensorflow/models/blob/master/official/nlp/configs/finetuning_experiments.py)   | [squad_v1.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/experiments/squad_v1.yaml) | [albert_base.yaml](https://github.com/tensorflow/models/blob/master/official/nlp/configs/models/albert_base.yaml)| <details> <summary>data and albert-base hub init</summary>task.train_data.input_path=/path-to-your-training-data,task.validation_data.input_path=/path-to-your-val-data,task.hub_module_url=https://tfhub.dev/tensorflow/albert_en_base/3 </details>|
| Transformer-large WMT14/en-de scratch |[wmt_transformer/large](https://github.com/tensorflow/models/blob/master/official/nlp/configs/wmt_transformer_experiments.py)|  | | <details> <summary>ende-32k sentencepiece</summary>task.sentencepiece_model_path='gs://tf_model_garden/nlp/transformer_wmt/ende_bpe_32k.model'</details> |


## Useful links

[How to Train Models](https://github.com/tensorflow/models/blob/master/official/nlp/docs/train.md)

[List of Pretrained Models for finetuning](https://github.com/tensorflow/models/blob/master/official/nlp/docs/pretrained_models.md)

[How to Publish Models](https://github.com/tensorflow/models/blob/master/official/nlp/docs/tfhub.md)

[TensorFlow blog on Model Garden](https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html).