Token Classification (Named Entity Recognition) Model

Token Classification model supports named entity recognition (NER) and other token level classification tasks, as long as the data follows the format specified below.

We’re going to use NER task throughout this section. NER, also referred to as entity chunking, identification or extraction, is the task of detecting and classifying key information (entities) in text. In other words, a NER model takes a piece of text as input and for each word in the text, the model identifies a category the word belongs to. For example, in a sentence: Mary lives in Santa Clara and works at NVIDIA, the model should detect that Mary is a person, Santa Clara is a location and NVIDIA is a company.

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from nemo.collections.nlp.models import TokenClassificationModel # to get the list of pre-trained models TokenClassificationModel.list_available_models() # Download and load the pre-trained BERT-based model model = TokenClassificationModel.from_pretrained("ner_en_bert") # try the model on a few examples model.add_predictions(['we bought four shirts from the nvidia gear store in santa clara.', 'NVIDIA is a company.'])

Note

We recommend you try this model in a Jupyter notebook (run on Google’s Colab.): NeMo/tutorials/nlp/Token_Classification_Named_Entity_Recognition.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 the model can be found here: NeMo/examples/nlp/token_classification/token_classification_train.py.

An example script on how to run evaluation and inference can be found here: NeMo/examples/nlp/token_classification/token_classification_evaluate.py.

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

For pre-training or fine-tuning of the model, the data should be split into 2 files:

  • text.txt

  • labels.txt

Each line of the text.txt file contains text sequences, where words are separated with spaces, i.e.: [WORD] [SPACE] [WORD] [SPACE] [WORD]. The labels.txt file contains corresponding labels for each word in text.txt, the labels are separated with spaces, i.e.: [LABEL] [SPACE] [LABEL] [SPACE] [LABEL]. Example of a text.txt file:

Jennifer is from New York City . She likes … …

Corresponding labels.txt file:

B-PER O O B-LOC I-LOC I-LOC O O O … …

To convert an IOB format (short for inside, outside, beginning) data to the format required for training, use examples/nlp/token_classification/data/import_from_iob_format.py.

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# For conversion from IOB format, for example, for CoNLL-2003 dataset: python import_from_iob_format.py --data_file=<PATH/TO/THE/FILE/IN/IOB/FORMAT>

Convert Dataset Required Arguments

  • --data_file: path to the file to convert from IOB to NeMo format

After running the above command, the data directory, where the --data_file is stored, should contain text_*.txt and labels_*.txt files. The default names for the training and evaluation in the conf/token_classification_config.yaml are the following:

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

In the Token Classification model, we are jointly training a classifier on top of a pre-trained language model, such as BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [NLP-NER2]. Unless the user provides a pre-trained checkpoint for the language model, the language model is initialized with the pre-trained model from HuggingFace Transformers.

Example of model configuration file for training the model can be found at: NeMo/examples/nlp/token_classification/conf/token_classification_config.yaml.

The specification can be roughly grouped into three categories:

  • Parameters that describe the training process: trainer

  • Parameters that describe the datasets: model.dataset, model.train_ds, model.validation_ds

  • Parameters that describe the model: model

More details about parameters in the spec file can be found below:

Parameter Data Type Description
model.dataset.data_dir | string Path to the data converted to the specified above format.
model.head.num_fc_layers | integer Number of fully connected layers.
model.head.fc_dropout | float Dropout to apply to the input hidden states.
model.head.activation | string Activation to use between fully connected layers.
model.punct_head.use_transrormer_init | bool Whether to initialize the weights of the classifier head with the same approach used in Transformer.
training_ds.text_file | string Name of the text training file located at data_dir.
training_ds.labels_file | string Name of the labels training file located at data_dir.
training_ds.num_samples | integer Number of samples to use from the training dataset, -1 - to use all.
validation_ds.text_file | string Name of the text file for evaluation, located at data_dir.
validation_ds.labels_file | string Name of the labels dev file located at data_dir.
validation_ds.num_samples | integer Number of samples to use from the dev set, -1 - to use all.

For more information, see Model NLP.

Example of the command for training the model:

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python token_classification_train.py \ model.dataset.data_dir=<PATH_TO_DATA_DIR> \ trainer.max_epochs=<NUM_EPOCHS> \ trainer.devices=[<CHANGE_TO_GPU(s)_YOU_WANT_TO_USE>] \ trainer.accelerator='gpu'

Required Arguments for Training

  • model.dataset.data_dir: path to the directory with pre-processed data.

Note

While the arguments are defined in the spec file, if you want to override these parameter definitions in the spec file and experiment with them, use the command-line to define the parameter. For example, the sample spec file mentioned above has validation_ds.batch_size set to 64. However, if the GPU utilization can be optimized further by using a larger batch size, override it to the desired value by adding the field validation_ds.batch_size=128 from the command-line. You can repeat this with any of the parameters defined in the sample spec file.

An example script on how to run inference can be found at examples/nlp/token_classification/token_classification_evaluate.py.

To run inference with the pre-trained model, run:

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python token_classification_evaluate.py \ pretrained_model=<PRETRAINED_MODEL>

Required Arguments for Inference

  • pretrained_model: pretrained Token Classification model from list_available_models() or path to a .nemo file. For example, ner_en_bert or your_model.nemo

An example script on how to evaluate the pre-trained model can be found at examples/nlp/token_classification/token_classification_evaluate.py.

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

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python token_classification_evaluate.py \ model.dataset.data_dir=<PATH/TO/DATA/DIR> \ pretrained_model=ner_en_bert \ model.test_ds.text_file=<text_*.txt> \ model.test_ds.labels_file=<labels_*.txt> \ model.dataset.max_seq_length=512

Required Arguments

  • pretrained_model: pretrained Token Classification model from list_available_models() or path to a .nemo file. For example, ner_en_bert or your_model.nemo

  • model.dataset.data_dir: 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 a classification report that includes the following metrics:

  • Precision

  • Recall

  • F1

For more information, see here.

[NLP-NER1]

Alexandra Antonova, Evelina Bakhturina, and Boris Ginsburg. Spellmapper: a non-autoregressive neural spellchecker for asr customization with candidate retrieval based on n-gram mappings. 2023. arXiv:2306.02317.

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.

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