Punctuation and Capitalization Model

Automatic Speech Recognition (ASR) systems typically generate text with no punctuation and capitalization of the words. There are two issues with non-punctuated ASR output:

  • it could be difficult to read and understand

  • models for some downstream tasks, such as named entity recognition, machine translation, or text-to-speech, are usually trained on punctuated datasets and using raw ASR output as the input to these models could deteriorate their performance

Quick Start Guide

from nemo.collections.nlp.models import PunctuationCapitalizationModel

# to get the list of pre-trained models
PunctuationCapitalizationModel.list_available_models()

# Download and load the pre-trained BERT-based model
model = PunctuationCapitalizationModel.from_pretrained("punctuation_en_bert")

# try the model on a few examples
model.add_punctuation_capitalization(['how are you', 'great how about you'])

Model Description

For each word in the input text, the Punctuation and Capitalization model:

  • predicts a punctuation mark that should follow the word (if any). By default, the model supports commas, periods, and question marks.

  • predicts if the word should be capitalized or not

In the Punctuation and Capitalization model, we are jointly training two token-level classifiers on top of a pre-trained language model, such as BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [NLP-PUNCT1].

Note

We recommend you try this model in a Jupyter notebook (run on Google’s Colab.): NeMo/tutorials/nlp/Punctuation_and_Capitalization.ipynb.

Connect to an instance with a GPU (Runtime -> Change runtime type -> select GPU for the hardware accelerator).

An example script on how to train and evaluate the model can be found at: NeMo/examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py.

The default configuration file for the model can be found at: NeMo/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml.

The script for inference can be found at: NeMo/examples/nlp/token_classification/punctuate_capitalize_infer.py.

Raw Data Format

The Punctuation and Capitalization model can work with any text dataset, although it is recommended to balance the data, especially for the punctuation task. Before pre-processing the data to the format expected by the model, the data should be split into train.txt and dev.txt (and optionally test.txt). Each line in the train.txt/dev.txt/test.txt should represent one or more full and/or truncated sentences.

Example of the train.txt/dev.txt file:

When is the next flight to New York?
The next flight is ...
....

The source_data_dir structure should look similar to the following:

.
|--sourced_data_dir
  |-- dev.txt
  |-- train.txt

NeMo Data Format

The Punctuation and Capitalization model expects the data in the following format:

The training and evaluation data is divided into 2 files: - text.txt - labels.txt

Each line of the text.txt file contains text sequences, where words are separated with spaces.

[WORD] [SPACE] [WORD] [SPACE] [WORD], for example:

when is the next flight to new york
the next flight is ...
...

The labels.txt file contains corresponding labels for each word in text.txt, the labels are separated with spaces. Each label in labels.txt file consists of 2 symbols:

  • the first symbol of the label indicates what punctuation mark should follow the word (where O means no punctuation needed)

  • the second symbol determines if a word needs to be capitalized or not (where U indicates that the word should be upper cased, and O - no capitalization needed)

By default, the following punctuation marks are considered: commas, periods, and question marks; the remaining punctuation marks were removed from the data. This can be changed by introducing new labels in the labels.txt files.

Each line of the labels.txt should follow the format: [LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt). For example, labels for the above text.txt file should be:

OU OO OO OO OO OO OU ?U
OU OO OO OO ...
...

The complete list of all possible labels used in this tutorial are:

  • OO

  • .O

  • ?O

  • OU

  • <blank space>

  • .U

  • ?U

Converting Raw Data to NeMo Format

To pre-process the raw text data, stored under sourced_data_dir (see the Raw Data Format section), run the following command:

python examples/nlp/token_classification/data/prepare_data_for_punctuation_capitalization.py \
       -s <PATH/TO/THE/SOURCE/FILE> \
       -o <PATH/TO/THE/OUTPUT/DIRECTORY>

Required Argument for Dataset Conversion

  • -s or --source_file: path to the raw file

  • -o or --output_dir - path to the directory to store the converted files

After the conversion, the output_dir should contain labels_*.txt and text_*.txt files. The default names for the training and evaluation in the conf/punctuation_capitalization_config.yaml are the following:

.
|--output_dir
  |-- labels_dev.txt
  |-- labels_train.txt
  |-- text_dev.txt
  |-- text_train.txt

Tarred dataset

Tokenization and encoding of data is quite costly for punctuation and capitalization task. If your dataset contains a lot of samples (~4M) you may use tarred dataset. A tarred dataset is a collection of .tar files which contain batches ready for passing into a model. Tarred dataset is not loaded into memory entirely, but in small pieces, which do not overflow memory. Tarred dataset relies on webdataset.

For creating of tarred dataset you will need data in NeMo format:

python examples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py \
    --text <PATH/TO/LOWERCASED/TEXT/WITHOUT/PUNCTUATION> \
    --labels <PATH/TO/LABELS/IN/NEMO/FORMAT> \
    --output_dir <PATH/TO/DIRECTORY/WITH/OUTPUT/TARRED/DATASET> \
    --num_batches_per_tarfile 100

All tar files contain similar amount of batches, so up to --num_batches_per_tarfile - 1 batches will be discarded during tarred dataset creation.

Beside .tar files with batches, the examples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py script will create metadata JSON file, and 2 .csv files with punctuation and capitalization label vocabularies. To use tarred dataset you will need to pass path to a metadata file of your dataset in a config parameter model.train_ds.tar_metadata_file and set a config parameter model.train_ds.use_tarred_dataset=true.

Training Punctuation and Capitalization Model

The language model is initialized with the a pre-trained model from HuggingFace Transformers, unless the user provides a pre-trained checkpoint for the language model. To train model from scratch, you will need to provide HuggingFace configuration in one of parameters model.language_model.config_file, model.language_model.config. An example of a model configuration file for training the model can be found at: NeMo/examples/nlp/token_classification/conf/punctuation_capitalization_config.yaml.

A configuration file is a .yaml file which contains all parameters for model creation, training, testing, validation. A structure of the configuration file for training and testing is described in the Run config section. Some of parameters are required in a punctuation-and-capitalization .yaml config. Default values of required parameters are ???. If you omit any of other parameters, they will be initialized according to default values from following tables.

Run config

An example of a config file is here.

Run config. The main config passed to a script punctuation_capitalization_train_evaluate.py

Parameter

Data type

Default value

Description

pretrained_model

string

null

Can be an NVIDIA’s NGC cloud model or a path to a .nemo checkpoint. You can get list of possible cloud options by calling a method list_available_models().

name

string

'Punctuation_and_Capitalization'

A name of the model. Used for naming output directories and .nemo checkpoints.

do_training

bool

true

Whether to perform training of the model.

do_testing

bool

false

Whether ot perform testing of the model after training.

model

model config

model config

A configuration for the PunctuationCapitalizationModel.

trainer

trainer config

Parameters of pytorch_lightning.Trainer.

exp_manager

exp manager config

A configuration with various NeMo training options such as output directories, resuming from checkpoint, tensorboard and W&B logging, and so on. For possible options see Experiment Manager description and class exp_manager.

Model config

Location of model config in parent config

Parent config

Key in parent config

Run config

model

A configuration of PunctuationCapitalizationModel model.

Model config

Parameter

Data type

Default value

Description

class_labels

class labels config

class labels config

Cannot be omitted in .yaml config. The class_labels parameter containing a dictionary with names of label id files used in .nemo checkpoints. These file names can also be used for passing label vocabularies to the model. If you wish to use class_labels for passing vocabularies, please provide path to vocabulary files in model.common_dataset_parameters.label_vocab_dir parameter.

common_dataset_parameters

common dataset parameters config

common dataset parameters config

Label ids and loss mask information.

train_ds

data config with string in ds_item

null

A configuration for creating training dataset and data loader. Cannot be omitted in .yaml config if training is performed.

validation_ds

data config with string OR list of strings in ds_item

null

A configuration for creating validation datasets and data loaders.

test_ds

data config with string OR list of strings in ds_item

null

A configuration for creating test datasets and data loaders. Cannot be omitted in .yaml config if testing is performed.

punct_head

head config

head config

A configuration for creating punctuation MLP head that is applied to a language model outputs.

capit_head

head config

head config

A configuration for creating capitalization MLP head that is applied to a language model outputs.

tokenizer

tokenizer config

tokenizer config

A configuration for creating source text tokenizer.

language_model

language model config

language model config

A configuration of a BERT-like language model which serves as a model body.

optim

optimization config

null

A configuration of optimizer, learning rate scheduler, and L2 regularization. Cannot be omitted in .yaml config if training is performed. For more information see Optimization and primer tutorial.

Class labels config

Location of class labels config in parent configs

Parent config

Key in parent config

Run config

model.class_labels

Model config

class_labels

Class labels config

Parameter

Data type

Default value

Description

punct_labels_file

string

???

A name of a punctuation labels file. This parameter cannot be omitted in .yaml config. This name is used as a name of label ids file in .nemo checkpoint. It also can be used for passing label vocabulary to the model. If punct_labels_file is used as a vocabulary file, then you should provide parameter label_vocab_dir in common dataset parameters (model.common_dataset_parameters.label_vocab_dir in run config). Each line of punct_labels_file file contains 1 label. The values are sorted, <line number>==<label id>, starting from 0. A label with 0 id must contain neutral label which has to be equal to a pad_label parameter in common dataset parameters.

capit_labels_file

string

???

Same as punct_labels_file for capitalization labels.

Common dataset parameters config

Location of common dataset parameters config in parent config

Parent config

Key in parent config

Run config

model.common_dataset_config

Model config

common_dataset_config

A common dataset parameters config which includes label and loss mask information. If you omit parameters punct_label_ids, capit_label_ids, label_vocab_dir, then labels will be inferred from a training dataset or loaded from a checkpoint.

Parameters ignore_extra_tokens and ignore_start_end are responsible for forming loss mask. A loss mask defines on which tokens loss is computed.

Common dataset parameters config

Parameter

Data type

Default value

Description

pad_label

string

???

This parameter cannot be omitted in .yaml config. The pad_label parameter contains label used for punctuation and capitalization label padding. It also serves as a neutral label for both punctuation and capitalization. If any of punct_label_ids, capit_label_ids parameters is provided, then pad_label must have 0 id in them. In addition, if label_vocab_dir is provided, then pad_label must be on the first lines in files class_labels.punct_labels_file and class_labels.capit_labels_file.

ignore_extra_tokens

bool

false

Whether to compute loss on not first tokens in words. If this parameter is true, then loss mask is false for all tokens in a word except the first.

ignore_start_end

bool

true

If false, then loss is computed on [CLS] and [SEP] tokens.

punct_label_ids

Dict[str, int]

null

A dictionary with punctuation label ids. pad_label must have 0 id in this dictionary. You can omit this parameter and pass label ids through class_labels.punct_labels_file or let the model to infer label ids from dataset or load them from checkpoint.

capit_label_ids

Dict[str, int]

null

Same as punct_label_ids for capitalization labels.

label_vocab_dir

string

null

A path to directory which contains class labels files. See ClassLabelsConfig. If this parameter is provided, then labels will be loaded from files which are located in label_vocab_dir and have names specified in model.class_labels configuration section. A label specified in pad_label has to be on the first lines of model.class_labels files.

Data config

Location of data configs in parent configs

Parent config

Keys in parent config

Run config

model.train_ds, model.validation_ds, model.test_ds

Model config

train_ds, validation_ds, test_ds

For convenience, items of data config are described in 4 tables: common parameters for both regular and tarred datasets, parameters which are applicable only to regular dataset, parameters which are applicable only to tarred dataset, parameters for PyTorch data loader.

Parameters for both regular (BertPunctuationCapitalizationDataset) and tarred (BertPunctuationCapitalizationTarredDataset) datasets

Parameter

Data type

Default value

Description

use_tarred_dataset

bool

???

This parameter cannot be omitted in .yaml config. The use_tarred_dataset parameter specifies whether to use tarred dataset or regular dataset. If true, then you should provide ds_item, tar_metadata_file parameters. Otherwise, you should provide parameters ds_item, text_file, labels_file, tokens_in_batch parameters.

ds_item

string OR list of strings (only if used in model.validation_ds or model.test_ds)

???

This parameter cannot be omitted in .yaml config. The ds_item parameter contains a path to a directory with tar_metadata_file file (if use_tarred_dataset=true) or text_file and labels_file (if use_tarred_dataset=false). For validation_ds or test_ds you may specify a list of paths in ds_item. If ds_item is a list, then evaluation will be performed on several datasets. To override ds_item config parameter with a list use following syntax: python punctuation_capitalization_train_evaluate.py model.validation_ds.ds_item=[path1,path2] (no spaces after = sign).

label_info_save_dir

string

null

A path to a directory where files created during dataset processing are stored. These files include label id files and label stats files. By default, it is a directory containing text_file or tar_metadata_file. You may need this parameter if dataset directory is read-only and thus does not allow saving anything near dataset files.

Parameters for regular (BertPunctuationCapitalizationDataset) dataset

Parameter

Data type

Default value

Description

text_file

string

null

This parameter cannot be omitted in .yaml config if use_tarred_dataset=false. The text_file parameter is a name of a source text file which is located in ds_item directory.

labels_file

string

null

This parameter cannot be omitted in .yaml config if use_tarred_dataset=false. The labels_file parameter is a name of a file with punctuation and capitalization labels in NeMo format. It has is located in ds_item directory.

tokens_in_batch

int

null

This parameter cannot be omitted in .yaml config if use_tarred_dataset=false. The tokens_in_batch parameter contains a number of tokens in a batch including paddings and special tokens ([CLS], [SEP], [UNK]). This config does not have batch_size parameter.

max_seq_length

int

512

Max number of tokens in a source sequence. max_seq_length includes [CLS] and [SEP] tokens. Sequences which are too long will be clipped by removal of tokens from the end of a sequence.

num_samples

int

-1

A number of samples loaded from text_file and labels_file which are used in the dataset. If this parameter equals -1, then all samples are used.

use_cache

bool

true

Whether to use pickled features which are already present in cache_dir. For large not tarred datasets, pickled features may considerably reduce time required for training to start. Tokenization of source sequences is not fast because sequences are split into words before tokenization. For even larger datasets (~4M), tarred datasets are recommended. If pickled features are missing, then new pickled features file will be created regardless of the value of use_cache parameter because pickled features are required for distributed training.

cache_dir

string

null

A path to a directory containing cache or directory where newly created cache is saved. By default, it is a directory containing text_file. You may need this parameter if cache for a dataset is going to be created and the dataset directory is read-only. cache_dir and label_info_save_dir are separate parameters for the case when a cache is ready and this cache is stored in a read-only directory. In such a case you will separate label_info_save_dir.

get_label_frequences

bool

false

Whether to show and save label frequencies. Frequencies are showed if verbose parameter is true. If get_label_frequencies=true, then frequencies are saved into label_info_save_dir.

verbose

bool

true

If true, then progress messages and examples of acquired features are printed.

n_jobs

int

0

Number of workers used for features creation (tokenization, label encoding, and clipping). If 0, then multiprocessing is not used; if null, then n_jobs will be equal to the number of CPU cores. WARNING: there can be weird deadlocking errors with some tokenizers (e.g. SentencePiece) if n_jobs is greater than zero.

Parameters for tarred (BertPunctuationCapitalizationTarredDataset) dataset

Parameter

Data type

Default value

Description

tar_metadata_file

string

null

This parameter cannot be omitted in .yaml config if use_tarred_dataset=true. The tar_metadata_file is a path to metadata file of tarred dataset. A tarred metadata file and other parts of tarred dataset are usually created by the script examples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py

tar_shuffle_n

int

1

The size of shuffle buffer of webdataset. The number of batches which are permuted.

Parameters for PyTorch torch.utils.data.DataLoader

Parameter

Data type

Default value

Description

shuffle

bool

true

Shuffle batches every epoch. For usual training datasets, the parameter activates batch repacking every epoch. For tarred dataset it would be only batches permutation.

drop_last

bool

false

In cases when data parallelism is used, drop_last defines the way data pipeline behaves when some replicas are out of data and some are not. If drop_last is True, then epoch ends in the moment when any replica runs out of data. If drop_last is False, then the replica will replace missing batch with a batch from a pool of batches that the replica has already processed. If data parallelism is not used, then parameter drop_last does not do anything. For more information see torch.utils.data.distributed.DistributedSampler

pin_memory

bool

true

See this parameter documentation in torch.utils.data.DataLoader

num_workers

int

8

See this parameter documentation in torch.utils.data.DataLoader

persistent_memory

bool

true

See this parameter documentation in torch.utils.data.DataLoader

Head config

Location of head configs in parent configs

Parent config

Keys in parent config

Run config

model.punct_head, model.capit_head

Model config

punct_head, capit_head

This config defines a multilayer perceptron which is applied to outputs of a language model. Number of units in the hidden layer is equal to the dimension of the language model.

Head config

Parameter

Data type

Default value

Description

num_fc_layers

int

1

A number of hidden layers in the multilayer perceptron.

fc_dropout

float

0.1

A dropout used in the MLP.

activation

string

'relu'

An activation used in hidden layers.

use_transformer_init

bool

true

Whether to initialize the weights of the classifier head with the approach that was used for language model initialization.

Language model config

Location of language model config in parent configs

Parent config

Key in parent config

Run config

model.language_model

Model config

language_model

A configuration of a language model which serves as a model body. BERT-like HuggingFace models are supported. Provide a valid pretrained_model_name and, optionally, you may reinitialize model via config_file or config.

Alternatively you can initialize the language model using lm_checkpoint.

Language model config

Parameter

Data type

Default value

Description

pretrained_model_name

string

???

This parameter cannot be omitted in .yaml config. The pretrained_model_name parameter contains a name of HuggingFace pretrained model. For example, 'bert-base-uncased'.

config_file

string

null

A path to a file with HuggingFace model config which is used to reinitialize the language model.

config

dict

null

A HuggingFace config which is used to reinitialize the language model.

lm_checkpoint

string

null

A path to a torch checkpoint of the language model.

Tokenizer config

Location of tokenizer config in parent configs

Parent config

Key in parent config

Run config

model.tokenizer

Model config

tokenizer

A configuration of a source text tokenizer.

Language model config

Parameter

Data type

Default value

Description

tokenizer_name

string

???

This parameter cannot be omitted in .yaml config. The tokenizer_name parameter containing a name of the tokenizer used for tokenization of source sequences. Possible options are 'sentencepiece', 'word', 'char', HuggingFace tokenizers (e.g. 'bert-base-uncased'). For more options see function nemo.collections.nlp.modules.common.get_tokenizer. The tokenizer must have properties cls_id, pad_id, sep_id, unk_id.

vocab_file

string

null

A path to vocabulary file which is used in 'word', 'char', and HuggingFace tokenizers.

special_tokens

Dict[str, str]

null

A dictionary with special tokens passed to constructors of 'char', 'word', 'sentencepiece', and various HuggingFace tokenizers.

tokenizer_model

string

null

A path to a tokenizer model required for 'sentencepiece' tokenizer.

Model training

For more information, refer to the Model NLP section.

To train the model from scratch, run:

python examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py \
       model.train_ds.ds_item=<PATH/TO/TRAIN/DATA_DIR> \
       model.train_ds.text_file=<NAME_OF_TRAIN_INPUT_TEXT_FILE> \
       model.train_ds.labels_file=<NAME_OF_TRAIN_LABELS_FILE> \
       model.validation_ds.ds_item=<PATH/TO/DEV/DATA_DIR> \
       model.validation_ds.text_file=<NAME_OF_DEV_TEXT_FILE> \
       model.validation_ds.labels_file=<NAME_OF_DEV_LABELS_FILE> \
       trainer.devices=[0,1] \
       trainer.accelerator='gpu' \
       optim.name=adam \
       optim.lr=0.0001

The above command will start model training on GPUs 0 and 1 with Adam optimizer and learning rate of 0.0001; and the trained model is stored in the nemo_experiments/Punctuation_and_Capitalization folder.

To train from the pre-trained model, run:

python examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py \
       model.train_ds.ds_item=<PATH/TO/TRAIN/DATA_DIR> \
       model.train_ds.text_file=<NAME_OF_TRAIN_INPUT_TEXT_FILE> \
       model.train_ds.labels_file=<NAME_OF_TRAIN_LABELS_FILE> \
       model.validation_ds.ds_item=<PATH/TO/DEV/DATA/DIR> \
       model.validation_ds.text_file=<NAME_OF_DEV_TEXT_FILE> \
       model.validation_ds.labels_file=<NAME_OF_DEV_LABELS_FILE> \
       pretrained_model=<PATH/TO/SAVE/.nemo>

Note

All parameters defined in the configuration file can be changed with command arguments. For example, the sample config file mentioned above has validation_ds.tokens_in_batch set to 15000. However, if you see that the GPU utilization can be optimized further by using a larger batch size, you may override to the desired value by adding the field validation_ds.tokens_in_batch=30000 over the command-line. You can repeat this with any of the parameters defined in the sample configuration file.

Inference

Inference is performed by a script examples/nlp/token_classification/punctuate_capitalize_infer.py

python punctuate_capitalize_infer.py \
    --input_manifest <PATH/TO/INPUT/MANIFEST> \
    --output_manifest <PATH/TO/OUTPUT/MANIFEST> \
    --pretrained_name punctuation_en_bert \
    --max_seq_length 64 \
    --margin 16 \
    --step 8

<PATH/TO/INPUT/MANIFEST> is a path to NeMo ASR manifest with text in which you need to restore punctuation and capitalization. If manifest contains 'pred_text' key, then 'pred_text' elements will be processed. Otherwise, punctuation and capitalization will be restored in 'text' elements.

<PATH/TO/OUTPUT/MANIFEST> is a path to NeMo ASR manifest into which result will be saved. The text with restored punctuation and capitalization is saved into 'pred_text' elements if 'pred_text' key is present in the input manifest. Otherwise result will be saved into 'text' elements.

Alternatively you can pass data for restoring punctuation and capitalization as plain text. See help for parameters --input_text and --output_text of the script punctuate_capitalize_infer.py.

The script punctuate_capitalize_infer.py can restore punctuation and capitalization in a text of arbitrary length. Long sequences are split into segments --max_seq_length - 2 tokens each (this number does not include [CLS] and [SEP] tokens). Each segment starts and ends with [CLS] and [SEP] tokens correspondingly. Every segment is offset to the previous one by --step tokens. For example, if every character is a token, --max_seq_length=5, --step=2, then text "hello" will be split into segments [['[CLS]', 'h', 'e', 'l', '[SEP]'], ['[CLS]', 'l', 'l', 'o', '[SEP]']].

If segments overlap, then predicted probabilities for a token present in several segments are multiplied before before selecting the best candidate.

Splitting leads to pour performance of a model near edges of segments. Use parameter --margin to discard --margin probabilities predicted for --margin tokens near segment edges. For example, if every character is a token, --max_seq_length=5, --step=1, --margin=1, then text "hello" will be split into segments [['[CLS]', 'h', 'e', 'l', '[SEP]'], ['[CLS]', 'e', 'l', 'l', '[SEP]'], ['[CLS]', 'l', 'l', 'o', '[SEP]']]. Before calculating final predictions, probabilities for tokens marked by asterisk are removed: [['[CLS]', 'h', 'e', 'l'*, '[SEP]'*], ['[CLS]'*, 'e'*, 'l', 'l'*, '[SEP]'*], ['[CLS]'*, 'l'*, 'l', 'o', '[SEP]']]

Model Evaluation

Model evaluation is performed by the same script examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py as training.

Use :ref`config<run-config-lab>` parameter do_training=false to disable training and parameter do_testing=true to enable testing. If both parameters do_training and do_testing are true, then model is trained and then tested.

To start evaluation of the pre-trained model, run:

python punctuation_capitalization_train_evaluate.py \
       +model.do_training=false \
       +model.to_testing=true \
       model.test_ds.ds_item=<PATH/TO/TEST/DATA/DIR>  \
       pretrained_model=punctuation_en_bert \
       model.test_ds.text_file=<NAME_OF_TEST_INPUT_TEXT_FILE> \
       model.test_ds.labels_file=<NAME_OF_TEST_LABELS_FILE>

Required Arguments

  • pretrained_model: pretrained Punctuation and Capitalization model from list_available_models() or path to a .nemo file. For example: punctuation_en_bert or your_model.nemo.

  • model.test_ds.ds_item: path to the directory that containes model.test_ds.text_file and model.test_ds.labels_file

During evaluation of the test_ds, the script generates two classification reports: one for capitalization task and another one for punctuation task. This classification reports include the following metrics:

  • Precision

  • Recall

  • F1

More details about these metrics can be found here.

References

NLP-PUNCT1

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.