diff --git a/README.md b/README.md
index c382b4853e28bfddd6c2acbf51f18dcdd4724c18..a9d524ac7de6b38a5ff8f2b1f25faeca7c8824aa 100644
--- a/README.md
+++ b/README.md
@@ -13,12 +13,14 @@ pip install -e .
For more details about this package, make sure to see the documentation available at https://teklia.gitlab.io/atr/dan/.
## Development
+
For development and tests purpose it may be useful to install the project as a editable package with pip.
* Use a virtualenv (e.g. with virtualenvwrapper `mkvirtualenv -a . dan`)
* Install `dan` as a package (e.g. `pip install -e .`)
### Linter
+
Code syntax is analyzed before submitting the code.\
To run the linter tools suite you may use pre-commit.
```shell
@@ -27,6 +29,7 @@ pre-commit run -a
```
### Run tests
+
Tests are executed with `tox` using [pytest](https://pytest.org).
To install `tox`,
```shell
@@ -41,6 +44,16 @@ Run a single test: `tox -- <test_path>::<test_function>`
The tests use a large file stored via [Git-LFS](https://docs.gitlab.com/ee/topics/git/lfs/). Make sure to run `git-lfs pull` before running them.
+### Update documentation
+
+Please keep the documentation updated when modifying or adding features.
+It's pretty easy to do:
+```shell
+pip install -r doc-requirements.txt
+mkdocs serve
+```
+
+You can then write in Markdown in the relevant `docs/*.md` files, and see live output on http://localhost:8000.
## Inference
@@ -71,6 +84,10 @@ text, confidence_scores = model.predict(image, confidences=True)
This package provides three subcommands. To get more information about any subcommand, use the `--help` option.
+### Get started
+
+See the [dedicated section](https://teklia.gitlab.io/atr/dan/get_started/training/) on the official DAN documentation.
+
### Data extraction from Arkindex
See the [dedicated section](https://teklia.gitlab.io/atr/dan/usage/datasets/extract/) on the official DAN documentation.
diff --git a/dan/ocr/document/train.py b/dan/ocr/document/train.py
index 7ba39de33f2043016653f88fe4f92de2bcd43562..63b5080a38857a483c3d205296a828b1cfc8d082 100644
--- a/dan/ocr/document/train.py
+++ b/dan/ocr/document/train.py
@@ -163,7 +163,7 @@ def get_config():
},
"training_params": {
"output_folder": "outputs/dan_esposalles_record", # folder name for checkpoint and results
- "max_nb_epochs": 710, # maximum number of epochs before to stop
+ "max_nb_epochs": 800, # maximum number of epochs before to stop
"max_training_time": 3600
* 24
* 1.9, # maximum time before to stop (in seconds)
diff --git a/docs/get_started/development.md b/docs/get_started/development.md
new file mode 100644
index 0000000000000000000000000000000000000000..0e26ccd42725b9a27737b537c95da8ec8347334a
--- /dev/null
+++ b/docs/get_started/development.md
@@ -0,0 +1,36 @@
+# Development
+
+DAN uses different tools during its development.
+
+## Linter
+
+Code syntax is analyzed before submitting the code.
+
+To run the linter tools suite you may use [pre-commit](https://pre-commit.com).
+
+```shell
+pip install pre-commit
+pre-commit run -a
+```
+
+## Run tests
+
+Tests are executed with [tox](https://tox.wiki) using [pytest](https://pytest.org).
+
+```shell
+pip install tox
+tox
+```
+
+To recreate tox virtual environment (e.g. a dependencies update), you may run `tox -r`.
+
+## Update documentation
+
+Documentation is built with [MkDocs](https://www.mkdocs.org/).
+
+```shell
+pip install -r doc-requirements.txt
+mkdocs serve
+```
+
+You can then write in Markdown in the relevant `docs/*.md` files, and see live output on http://localhost:8000.
diff --git a/docs/get_started/index.md b/docs/get_started/index.md
new file mode 100644
index 0000000000000000000000000000000000000000..1eba3b4f21f6ff76c1c714be5c423ca4c7b64c00
--- /dev/null
+++ b/docs/get_started/index.md
@@ -0,0 +1,17 @@
+# Get started
+
+To use DAN in your own environment, install it using pip:
+
+```shell
+pip install -e .
+```
+
+To learn more about the newly installed `teklia-dan` command, make sure to run:
+```shell
+teklia-dan --help
+```
+
+Get started with:
+
+* [Developments](development.md)
+* [Training workflow](training.md)
diff --git a/docs/get_started/training.md b/docs/get_started/training.md
new file mode 100644
index 0000000000000000000000000000000000000000..8eb5dc0c2c08b8f63d489cedfce0fc8962b717f6
--- /dev/null
+++ b/docs/get_started/training.md
@@ -0,0 +1,74 @@
+# Training workflow
+
+There are a several steps to follow when training a DAN model.
+
+## 1. Extract data
+
+The data must be extracted and formatted for training. To extract the data, DAN uses an Arkindex export database in SQLite format. You will need to:
+
+1. Structure the data into folders (`train` / `val` / `test`) in [Arkindex](https://arkindex.teklia.com/).
+2. [Export the project](https://doc.arkindex.org/howto/export/) in SQLite format.
+3. Extract the data with the [extract command](../usage/datasets/extract.md).
+4. Format the data with the [format command](../usage/datasets/format.md).
+
+At the end, you should have a tree structure like this:
+```
+output/
+├── charset.pkl
+├── labels.json
+├── split.json
+├── images
+│ ├── train
+│ ├── val
+│ └── test
+└── labels
+ ├── train
+ ├── val
+ └── test
+```
+
+## 2. Train
+
+The training command does not take any input parameters for now. To train a DAN model, you will therefore need to:
+
+1. Update the parameters from those listed in the [dedicated page](../usage/train/parameters.md). You will always need to update at least these variables:
+
+ - `dataset_name`, `dataset_level`, `dataset_variant` and `dataset_path`,
+ - `model_params.transfer_learning.*[checkpoint_path]` to finetune an existing model,
+ - `training_params.output_folder`.
+
+2. Train a DAN model with the [train command](../usage/train/index.md).
+
+## 3. Predict
+
+Once the training is complete, you can apply a trained DAN model on an image.
+
+To do this, you will need to:
+
+1. Create a `parameters.yml` file using the parameters saved during training in the `params` file, located in `{training_params.output_folder}/results`. This file should have the following format:
+```yml
+version: 0.0.1
+parameters:
+ mean: [float, float, float]
+ std: [float, float, float]
+ max_char_prediction: int
+ encoder:
+ input_channels: int
+ dropout: float
+ decoder:
+ enc_dim: int
+ l_max: int
+ dec_pred_dropout: float
+ attention_win: int
+ use_1d_pe: bool
+ use_lstm: bool
+ vocab_size: int
+ h_max: int
+ w_max: int
+ dec_num_layers: int
+ dec_dim_feedforward: int
+ dec_num_heads: int
+ dec_att_dropout: float
+ dec_res_dropout: float
+```
+2. Apply a trained DAN model on an image using the [predict command](../usage/predict.md).
diff --git a/docs/index.md b/docs/index.md
index 925c2f15a016e517a35b053e857686a6121e4244..9aef9d1a4782ab746be76f990c7553ee1982713e 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -9,36 +9,4 @@ The model uses a character-level attention to handle slanted lines:
Click [here](original_paper.md) to learn more about the model and how it fares against SOTA models.
-## Getting started
-
-To use DAN in your own environment, install it using pip:
-
-```shell
-pip install -e .
-```
-
-To learn more about the newly installed `teklia-dan` command, make sure to run:
-```shell
-teklia-dan --help
-```
-
-## Linter
-
-Code syntax is analyzed before submitting the code.\
-To run the linter tools suite you may use pre-commit.
-
-```shell
-pip install pre-commit
-pre-commit run -a
-```
-
-## Run tests
-
-Tests are executed with tox using [pytest](https://pytest.org).
-
-```shell
-pip install tox
-tox
-```
-
-To recreate tox virtual environment (e.g. a dependencies update), you may run `tox -r`
+[Get started with DAN](get_started/index.md) now!
diff --git a/docs/usage/predict.md b/docs/usage/predict.md
index a15d425ce7392fa8cc8d4b0c9d6f833d3e3443fd..0532c603a85179d98345c59a279cb85a5d3db425 100644
--- a/docs/usage/predict.md
+++ b/docs/usage/predict.md
@@ -1,29 +1,29 @@
# Predict
-Use the `teklia-dan predict` command to predict a trained DAN model on an image.
+Use the `teklia-dan predict` command to apply a trained DAN model on an image.
## Description of parameters
-| Parameter | Description | Type | Default |
-| --------------------------- | -------------------------------------------------------------------------------------------- | ------- | ------------- |
-| `--image` | Path to the image to predict. Must not be provided with `--image-dir`. | `Path` | |
-| `--image-dir` | Path to the folder where the images to predict are stored. Must not be provided with `--image`. | `Path` | |
-| `--image-extension` | The extension of the images in the folder. Ignored if `--image-dir` is not provided. | `str` | .jpg |
-| `--model` | Path to the model to use for prediction | `Path` | |
-| `--parameters` | Path to the YAML parameters file. | `Path` | |
-| `--charset` | Path to the charset file. | `Path` | |
-| `--output` | Path to the output folder. Results will be saved in this directory. | `Path` | |
-| `--scale` | Image scaling factor before feeding it to DAN. | `float` | `1.0` |
-| `--confidence-score` | Whether to return confidence scores. | `bool` | `False` |
-| `--confidence-score-levels` | Level to return confidence scores. Should be any combination of `["line", "word", "char"]`. | `str` | |
-| `--attention-map` | Whether to plot attention maps. | `bool` | `False` |
-| `--attention-map-scale` | Image scaling factor before creating the GIF. | `float` | `0.5` |
-| `--attention-map-level` | Level to plot the attention maps. Should be in `["line", "word", "char"]`. | `str` | `"line"` |
-| `--predict-objects` | Whether to return polygons coordinates. | `bool` | `False` |
-| `--word-separators` | List of word separators. | `list` | `[" ", "\n"]` |
-| `--line-separators` | List of line separators. | `list` | `["\n"]` |
-| `--threshold-method` | Method to use for attention mask thresholding. Should be in `["otsu", "simple"]`. | `str` | `"otsu"` |
-| `--threshold-value ` | Threshold to use for the "simple" thresholding method. | `int` | `0` |
+| Parameter | Description | Type | Default |
+| --------------------------- | ----------------------------------------------------------------------------------------------- | ------- | ------------- |
+| `--image` | Path to the image to predict. Must not be provided with `--image-dir`. | `Path` | |
+| `--image-dir` | Path to the folder where the images to predict are stored. Must not be provided with `--image`. | `Path` | |
+| `--image-extension` | The extension of the images in the folder. Ignored if `--image-dir` is not provided. | `str` | .jpg |
+| `--model` | Path to the model to use for prediction | `Path` | |
+| `--parameters` | Path to the YAML parameters file. | `Path` | |
+| `--charset` | Path to the charset file. | `Path` | |
+| `--output` | Path to the output folder. Results will be saved in this directory. | `Path` | |
+| `--scale` | Image scaling factor before feeding it to DAN. | `float` | `1.0` |
+| `--confidence-score` | Whether to return confidence scores. | `bool` | `False` |
+| `--confidence-score-levels` | Level to return confidence scores. Should be any combination of `["line", "word", "char"]`. | `str` | |
+| `--attention-map` | Whether to plot attention maps. | `bool` | `False` |
+| `--attention-map-scale` | Image scaling factor before creating the GIF. | `float` | `0.5` |
+| `--attention-map-level` | Level to plot the attention maps. Should be in `["line", "word", "char"]`. | `str` | `"line"` |
+| `--predict-objects` | Whether to return polygons coordinates. | `bool` | `False` |
+| `--word-separators` | List of word separators. | `list` | `[" ", "\n"]` |
+| `--line-separators` | List of line separators. | `list` | `["\n"]` |
+| `--threshold-method` | Method to use for attention mask thresholding. Should be in `["otsu", "simple"]`. | `str` | `"otsu"` |
+| `--threshold-value ` | Threshold to use for the "simple" thresholding method. | `int` | `0` |
## Examples
diff --git a/docs/usage/train/index.md b/docs/usage/train/index.md
index 2627b06902f5f353e7a05eb497550767edb5925d..2f316e7f1a01c24773d074e3a2bb3ee5ca713b1c 100644
--- a/docs/usage/train/index.md
+++ b/docs/usage/train/index.md
@@ -18,7 +18,7 @@ To train DAN on documents:
To train DAN on lines, run `teklia-dan train document` with a line dataset.
-## Additional page
+## Additional pages
* [Jean Zay tutorial](jeanzay.md)
* [Data augmentation](augmentation.md)
diff --git a/docs/usage/train/parameters.md b/docs/usage/train/parameters.md
index ce10c3299fd5d0ab8624cf15e30a668246be295f..e2aad9531cad0f0775e33fa93ae9d9ac5283a6a2 100644
--- a/docs/usage/train/parameters.md
+++ b/docs/usage/train/parameters.md
@@ -1,22 +1,23 @@
# Training configuration
-All hyperparameters are specified and editable in the training scripts (meaning are in comments). This page introduces some useful keys and their description.
+All hyperparameters are specified and editable in the training scripts `dan/ocr/document/train.py::get_config` (descriptions are in comments). This page introduces some useful keys and theirs descriptions.
## Dataset parameters
-| Parameter | Description | Type | Default |
-| --------------------------------------- | -------------------------------------------------------------------------------------- | ------------ | ---------------------------------------------------- |
-| `dataset_name` | Name of the dataset. | `str` | |
-| `dataset_level` | Level of the dataset. Should be named after the element type. | `str` | |
-| `dataset_variant` | Variant of the dataset. Usually empty for HTR datasets, `"_sem"` for HTR+NER datasets. | `str` | |
-| `dataset_path` | Path to the dataset. | `str` | |
-| `dataset_params.config.dataset_manager` | Dataset manager class. | custom class | `OCRDatasetManager` |
-| `dataset_params.config.dataset_class` | Dataset class. | custom class | `OCRDataset` |
-| `dataset_params.config.datasets` | Dataset dictionary with the dataset name as key and dataset path as value. | `dict` | |
-| `dataset_params.config.load_in_memory` | Load all images in CPU memory. | `str` | `True` |
-| `dataset_params.config.worker_per_gpu` | Number of parallel processes per gpu for data loading. | `int` | `4` |
-| `dataset_params.config.preprocessings` | List of pre-processing functions to apply to input images. | `list` | (see [dedicated section](#data-preprocessing)) |
-| `dataset_params.config.augmentation` | Whether to use data augmentation on the training set. | `bool` | `True` (see [dedicated section](#data-augmentation)) |
+| Parameter | Description | Type | Default |
+| -------------------------------------- | -------------------------------------------------------------------------------------- | ------ | ---------------------------------------------------- |
+| `dataset_name` | Name of the dataset. | `str` | |
+| `dataset_level` | Level of the dataset. Should be named after the element type. | `str` | |
+| `dataset_variant` | Variant of the dataset. Usually empty for HTR datasets, `"_sem"` for HTR+NER datasets. | `str` | |
+| `dataset_path` | Path to the dataset. | `str` | |
+| `dataset_params.config.load_in_memory` | Load all images in CPU memory. | `bool` | `True` |
+| `dataset_params.config.worker_per_gpu` | Number of parallel processes per gpu for data loading. | `int` | `4` |
+| `dataset_params.config.preprocessings` | List of pre-processing functions to apply to input images. | `list` | (see [dedicated section](#data-preprocessing)) |
+| `dataset_params.config.augmentation` | Whether to use data augmentation on the training set. | `bool` | `True` (see [dedicated section](#data-augmentation)) |
+
+!!! warning
+ The variables `dataset_name`, `dataset_level`, `dataset_variant` and `dataset_path` must have values such that the data is located in `{dataset_path}/{dataset_name}_{dataset_level}{dataset_variant}`.
+
### Data preprocessing
@@ -123,33 +124,33 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
## Model parameters
-| Name | Description | Type | Default |
-| ----------------------------------------- | ------------------------------------------------------------------------------------ | ------------- | ----------------------------------------------------------------- |
-| `model_params.models.encoder` | Encoder class. | custom class | `FCN_encoder` |
-| `model_params.models.decoder` | Decoder class. | custom class | `GlobalHTADecoder` |
-| `model_params.transfer_learning.encoder` | Model to load for the encoder [state_dict_name, checkpoint_path, learnable, strict]. | `list` | `["encoder", "pretrained_models/dan_rimes_page.pt", True, True]` |
-| `model_params.transfer_learning.decoder` | Model to load for the decoder [state_dict_name, checkpoint_path, learnable, strict]. | `list` | `["encoder", "pretrained_models/dan_rimes_page.pt", True, False]` |
-| `model_params.transfered_charset` | Transfer learning of the decision layer based on charset of the model to transfer. | `bool` | `True` |
-| `model_params.additional_tokens` | For decision layer = [<eot>, ], only for transferred charset. | `int` | `1` |
-| `model_params.input_channels` | Number of channels of input image. | `int` | `3` |
-| `model_params.dropout` | Dropout probability in the encoder. | `float` | `0.5` |
-| `model_params.enc_dim` | Dimension of features extracted by the encoder. | `int` | `256` |
-| `model_params.nb_layers` | Number of layers in the encoder. | `int` | `5` |
-| `model_params.h_max` | Maximum height for encoder output (for 2D positional embedding). | `int` | `500` |
-| `model_params.w_max` | Maximum width for encoder output (for 2D positional embedding). | `int` | `1000` |
-| `model_params.l_max` | Maximum predicted sequence length (for 1D positional embedding). | `int` | `15000` |
-| `model_params.dec_num_layers` | Number of transformer decoder layers. | `int` | `8` |
-| `model_params.dec_num_heads` | Number of heads in transformer decoder layers. | `int` | `4` |
-| `model_params.dec_res_dropout` | Dropout probability in transformer decoder layers. | `int` | `0.1` |
-| `model_params.dec_pred_dropout` | Dropout rate before decision layer. | `float` | `0.1` |
-| `model_params.dec_att_dropout` | Dropout rate in multi head attention. | `float` | `0.1` |
-| `model_params.dec_dim_feedforward` | Number of dimensions for feedforward layer in transformer decoder layers. | `int` | `256` |
-| `model_params.use_2d_pe` | Whether to use 2D positional embedding. | `bool` | `True` |
-| `model_params.use_1d_pe` | Whether to use 1D positional embedding. | `bool` | `True` |
-| `model_params.use_lstm` | Whether to use a LSTM layer in the decoder. | `bool` | `False` |
-| `model_params.attention_win` | Length of attention window. | `int` | `100` |
-| `model_params.dropout_scheduler.function` | Curriculum dropout scheduler. | custom class. | `100` |
-| `model_params.dropout_scheduler.T` | Exponential factor. | `float` | `5e4` |
+| Name | Description | Type | Default |
+| ----------------------------------------- | ------------------------------------------------------------------------------------ | ------------ | ----------------------------------------------------------------- |
+| `model_params.models.encoder` | Encoder class. | custom class | `FCN_encoder` |
+| `model_params.models.decoder` | Decoder class. | custom class | `GlobalHTADecoder` |
+| `model_params.transfer_learning.encoder` | Model to load for the encoder [state_dict_name, checkpoint_path, learnable, strict]. | `list` | `["encoder", "pretrained_models/dan_rimes_page.pt", True, True]` |
+| `model_params.transfer_learning.decoder` | Model to load for the decoder [state_dict_name, checkpoint_path, learnable, strict]. | `list` | `["encoder", "pretrained_models/dan_rimes_page.pt", True, False]` |
+| `model_params.transfered_charset` | Transfer learning of the decision layer based on charset of the model to transfer. | `bool` | `True` |
+| `model_params.additional_tokens` | For decision layer = [<eot>, ], only for transferred charset. | `int` | `1` |
+| `model_params.input_channels` | Number of channels of input image. | `int` | `3` |
+| `model_params.dropout` | Dropout probability in the encoder. | `float` | `0.5` |
+| `model_params.enc_dim` | Dimension of features extracted by the encoder. | `int` | `256` |
+| `model_params.nb_layers` | Number of layers in the encoder. | `int` | `5` |
+| `model_params.h_max` | Maximum height for encoder output (for 2D positional embedding). | `int` | `500` |
+| `model_params.w_max` | Maximum width for encoder output (for 2D positional embedding). | `int` | `1000` |
+| `model_params.l_max` | Maximum predicted sequence length (for 1D positional embedding). | `int` | `15000` |
+| `model_params.dec_num_layers` | Number of transformer decoder layers. | `int` | `8` |
+| `model_params.dec_num_heads` | Number of heads in transformer decoder layers. | `int` | `4` |
+| `model_params.dec_res_dropout` | Dropout probability in transformer decoder layers. | `int` | `0.1` |
+| `model_params.dec_pred_dropout` | Dropout rate before decision layer. | `float` | `0.1` |
+| `model_params.dec_att_dropout` | Dropout rate in multi head attention. | `float` | `0.1` |
+| `model_params.dec_dim_feedforward` | Number of dimensions for feedforward layer in transformer decoder layers. | `int` | `256` |
+| `model_params.use_2d_pe` | Whether to use 2D positional embedding. | `bool` | `True` |
+| `model_params.use_1d_pe` | Whether to use 1D positional embedding. | `bool` | `True` |
+| `model_params.use_lstm` | Whether to use a LSTM layer in the decoder. | `bool` | `False` |
+| `model_params.attention_win` | Length of attention window. | `int` | `100` |
+| `model_params.dropout_scheduler.function` | Curriculum dropout scheduler. | custom class | `exponential_dropout_scheduler` |
+| `model_params.dropout_scheduler.T` | Exponential factor. | `float` | `5e4` |
## Training parameters
@@ -158,17 +159,17 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
| ------------------------------------------------------- | --------------------------------------------------------------------------- | ------------ | ------------------------------------------- |
| `training_params.output_folder` | Directory for checkpoint and results. | `str` | |
| `training_params.max_nb_epochs` | Maximum number of epochs before stopping training. | `int` | `800` |
-| `training_params.max_training_time` | Maximum time (in seconds) before stopping training. | `int` | `350000` |
+| `training_params.max_training_time` | Maximum time (in seconds) before stopping training. | `int` | `164160` |
| `training_params.load_epoch` | Model to load. Should be either `"best"` (evaluation) or `last` (training). | `str` | `"last"` |
| `training_params.interval_save_weights` | Step to save weights. Set to `None` to keep only best and last epochs. | `int` | `None` |
| `training_params.batch_size` | Mini-batch size for the training loop. | `int` | `2` |
| `training_params.use_ddp` | Whether to use DistributedDataParallel. | `bool` | `False` |
| `training_params.ddp_port` | DDP port. | `int` | `20027` |
-| `training_params.use_amp` | Whether to enable automatic mix-precision. | `int` | `torch.cuda.device_count()` |
-| `training_params.nb_gpu` | Number of GPUs to train DAN. | `str` | |
+| `training_params.use_amp` | Whether to enable automatic mix-precision. | `bool` | `True` |
+| `training_params.nb_gpu` | Number of GPUs to train DAN. | `int` | `torch.cuda.device_count()` |
| `training_params.optimizers.all.class` | Optimizer class. | custom class | `Adam` |
| `training_params.optimizers.all.args.lr` | Learning rate for the optimizer. | `float` | `0.0001` |
-| `training_params.optimizers.all.args.amsgrad` | Whether to use AMSGrad optimization. | custom class | `False` |
+| `training_params.optimizers.all.args.amsgrad` | Whether to use AMSGrad optimization. | `bool` | `False` |
| `training_params.lr_schedulers` | Learning rate schedulers. | custom class | `None` |
| `training_params.eval_on_valid` | Whether to evaluate and log metrics on the validation set during training. | `bool` | `True` |
| `training_params.eval_on_valid_interval` | Interval (in epochs) to evaluate during training. | `int` | `5` |
@@ -176,7 +177,7 @@ For a detailed description of all augmentation transforms, see the [dedicated pa
| `training_params.expected_metric_value` | Best value for the focus metric. Should be either `"high"` or `"low"`. | `low` | `cer` |
| `training_params.set_name_focus_metric` | Dataset to focus on to select best weights. | `str` | |
| `training_params.train_metrics` | List of metrics to compute during training. | `list` | `["loss_ce", "cer", "wer", "wer_no_punct"]` |
-| `training_params.train_metrics` | List of metrics to compute during validation. | `list` | `["cer", "wer", "wer_no_punct"]` |
+| `training_params.eval_metrics` | List of metrics to compute during validation. | `list` | `["cer", "wer", "wer_no_punct"]` |
| `training_params.force_cpu` | Whether to train on CPU (for debugging). | `bool` | `False` |
| `training_params.max_char_prediction` | Maximum number of characters to predict. | `int` | `1000` |
| `training_params.label_noise_scheduler.min_error_rate` | Minimum ratio of teacher forcing. | `float` | `0.2` |
diff --git a/mkdocs.yml b/mkdocs.yml
index 3c7787d4cb9616024eb7203881731e841bf4ad30..79cc177cf8abda07c90289b4c3457208be422780 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -53,6 +53,10 @@ plugins:
nav:
- Home: index.md
- Original implementation: original_paper.md
+ - Get started:
+ - get_started/index.md
+ - Development: get_started/development.md
+ - Training: get_started/training.md
- Usage:
- usage/index.md
- Datasets: