Token Classification (Named Entity Recognition)¶
Introduction¶
TokenClassification Model supports Named entity recognition (NER) and other token level classification tasks, as long as the data follows the format specified below. This model card will focus on the NER task.
Named entity recognition (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, an 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.
TLT provides a sample notebook to outline the end-to-end workflow on how to train a TokenClassification model using TLT and deploy it in Jarvis format on NGC resources.
Downloading Sample Spec Files¶
Before proceeding, download sample spec files that are needed for the rest of the subtasks.
tlt token_classification download_specs -r /results/token_classification/default_specs/ \
-o /specs/nlp/token_classification
Download Spec Required Arguments¶
-o: Path to where the spec files will be stored-r: Output directory to store logs
After running the above, the spec files would be stored under /specs/nlp/token_classification and you can modify them locally if this directory is mounted to your local folder in ~/.tlt_mounts.json file.
Data Input for Token Classification Model¶
For pre-training or fine-tuning of the model, the data should be split into two 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 … …
Dataset Conversion¶
To convert an IOB format (short for inside, outside, beginning) data to the format required for training:
# For conversion from IOB format, for example, for CoNLL-2003 dataset:
tlt token_classification dataset_convert [-h] \
-e /specs/nlp/token_classification/dataset_convert.yaml \
source_data_dir=/path/to/source_data_dir \
target_data_dir=/path/to/target_data_dir
The source_data_dir structure should look like this (test.txt is optional):
.
|--sourced_data_dir
|-- dev.txt
|-- test.txt
|-- train.txt
Note, the development set (or dev set) will be used to evaluate the performance of the model during model training. The hyper-parameters search and model selection should be based on the dev set, while the final evaluation of the selected model should be performed on the test set.
An example of a spec file for dataset conversion:
# Path to the folder containing the dataset source files
source_data_dir: ???
# Path to the output folder.
target_data_dir: ???
# list of file names inside source_data_dir in IOB format
list_of_file_names: ['train.txt','dev.txt']
# name of the file with training data inside sourse_data_dir
# train_file is used to generate label to label_id mapping
train_file_name: 'train.txt'
# Max sequence length use -1 to leave the examples's length as is,
# otherwise long examples will be split into multiple examples'
max_length: -1
Output log after running token_classification dataset_convert:
[NeMo I 2021-01-21 09:07:11 dataset_convert:133] Spec file:
source_data_dir: original/
list_of_file_names:
- train.txt
- dev.txt
train_file_name: train.txt
target_data_dir: original/output/
max_length: -1
[NeMo I token_classification_utils:54] Processing original/output/labels_train.txt
[NeMo I token_classification_utils:92] Labels mapping {'O': 0, 'B-LOC': 1, 'B-MISC': 2, 'B-ORG': 3, 'B-PER': 4, 'I-LOC': 5, 'I-MISC': 6, 'I-ORG': 7, 'I-PER': 8} saved to : original/output/label_ids.csv
[NeMo I token_classification_utils:101] Three most popular labels in original/output/labels_train.txt:
[NeMo I data_preprocessing:131] label: 0, 169578 out of 203621 (83.28%).
[NeMo I data_preprocessing:131] label: 1, 7140 out of 203621 (3.51%).
[NeMo I data_preprocessing:131] label: 4, 6600 out of 203621 (3.24%).
[NeMo I token_classification_utils:103] Total labels: 203621. Label frequencies - {0: 169578, 1: 7140, 4: 6600, 3: 6321, 8: 4528, 7: 3704, 2: 3438, 5: 1157, 6: 1155}
[NeMo I dataset_convert:173] Text and labels for train.txt saved to original/output/.
[NeMo I dataset_convert:174] Processing of train.txt is complete.
[NeMo I token_classification_utils:54] Processing original/output/labels_dev.txt
[NeMo I token_classification_utils:75] Using provided labels mapping {'O': 0, 'B-LOC': 1, 'B-MISC': 2, 'B-ORG': 3, 'B-PER': 4, 'I-LOC': 5, 'I-MISC': 6, 'I-ORG': 7, 'I-PER': 8}
[NeMo I token_classification_utils:98] original/output/labels_dev_label_stats.tsv found, skipping stats calculation.
[NeMo I dataset_convert:173] Text and labels for dev.txt saved to original/output/.
[NeMo I dataset_convert:174] Processing of dev.txt is complete.
Convert Dataset Required Arguments¶
-e: The experiment specification file.source_data_dir- path to the raw datatarget_data_dir- path to store the processed files
Convert Dataset Optional Arguments¶
-h, --help: Show this help message and exitlist_of_file_names: List of files insource_data_dirfor conversion
Parameter |
Datatype |
Default |
Description |
Supported Values |
|---|---|---|---|---|
source_data_dir |
string |
– | Path to the dataset source data directory |
– |
|
target_data_dir |
string |
– | Path to the dataset target data directory |
– |
|
list_of_file_names |
List of strings |
[‘train.txt’,’dev.txt’] |
List of files for conversion |
– |
train_file_name |
string |
‘train.txt’ |
Name of the file with training data inside sourse_data_dir train_file is used to generate string label to integer label_id mapping |
|
After the conversion, the target_data_dir should contain the following files:
.
|--target_data_dir
|-- label_ids.csv
|-- labels_dev.txt
|-- labels_test.txt
|-- labels_train.txt
|-- text_dev.txt
|-- text_test.txt
|-- text_train.txt
Note
The target_data_dir contains file label_ids.csv. This file lists all the labels present in the train data. Each label is written on a separate line. Additionally, a special padding token - O - used to mark input that should be tagged as “no label” for the task. For example, for NER task, words that do not belong to any entities would have O label. During training, this label_ids.csv file is going to be used to create mapping from a text label to an integer.
Example of the label_ids.csv file:
O
B-GPE
B-LOC
B-MISC
B-ORG
B-PER
B-TIME
I-GPE
I-LOC
I-MISC
I-ORG
I-PER
I-TIME
During training, the text labels will be mapped to integers. Each label will have an id that corresponds to the line number of the text label. The above file, would be converted like so:
{'O': 0,
'B-GPE': 1,
'B-LOC': 2,
'B-MISC': 3,
'B-ORG': 4,
'B-PER': 5,
'B-TIME': 6,
'I-GPE': 7,
'I-LOC': 8,
'I-MISC': 9,
'I-ORG': 10,
'I-PER': 11,
'I-TIME': 12}
Training a Token Classification Model¶
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.
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 spec for training:
trainer:
max_epochs: 5
# Specifies parameters for the Token Classification model
model:
tokenizer:
tokenizer_name: ${model.language_model.pretrained_model_name} # or sentencepiece
vocab_file: null # path to vocab file
tokenizer_model: null # only used if tokenizer is sentencepiece
special_tokens: null
# Pre-trained language model such as BERT
language_model:
pretrained_model_name: bert-base-uncased
lm_checkpoint: null
config_file: null # json file, precedence over config
config: null
# Specifies parameters of the token classification head that follows a BERT-based language-model
head:
num_fc_layers: 2
fc_dropout: 0.5
activation: 'relu'
use_transformer_init: True
# Path to file with label_ids, generated with dataset_convert.py.
# Those labels are used by the model as labels (names of target classes, their number).
label_ids: ???
# Path to directory containing both finetuning and validation data.
data_dir: ???
# Specifies the parameters of the dataset to be used for training.
training_ds:
text_file: text_train.txt
labels_file: labels_train.txt
batch_size: 64
num_samples: -1 # number of samples to be considered, -1 means all the dataset
# Specifies the parameters of the dataset to be used for validation.
validation_ds:
text_file: text_dev.txt
labels_file: labels_dev.txt
batch_size: 64
num_samples: -1 # number of samples to be considered, -1 means all the dataset
# The parameters for the training optimizer, including learning rate, lr schedule, etc.
optim:
name: adam
lr: 5e-5
weight_decay: 0.00
# scheduler setup
sched:
name: WarmupAnnealing
# Scheduler params
warmup_steps: null
warmup_ratio: 0.1
last_epoch: -1
# pytorch lightning args
monitor: val_loss
reduce_on_plateau: false
The specification can be roughly grouped into three categories:
Parameters that describe the training process
Parameters that describe the datasets, and
Parameters that describe the model.
More details about parameters in the spec file are provided below:
Parameter |
Data Type |
Default |
Description |
data_dir |
string |
– |
Path to the data converted to the specified above format |
trainer.max_epochs |
integer |
5 |
Maximum number of epochs to train the model |
model.label_ids |
string |
– |
Path to the string labels to integer mapping (is generated during the dataset conversion step) |
model.tokenizer.tokenizer_name |
string |
Will be filled automatically based on model.language_model.pretrained_model_name |
Tokenizer name |
model.tokenizer.vocab_file |
string |
null |
Path to tokenizer vocabulary |
model.tokenizer.tokenizer_model |
string |
null |
Path to tokenizer model (only for sentencepiece tokenizer) |
model.language_model.pretrained_model_name |
string |
bert-base-uncased |
Pre-trained language model name (choose from bert-base-cased and bert-base-uncased) |
model.language_model.lm_checkpoint |
string |
null |
Path to the pre-trained language model checkpoint |
model.language_model.config_file |
string |
null |
Path to the pre-trained language model config file |
model.language_model.config |
dictionary |
null |
Config of the pre-trained language model |
model.head.num_fc_layers |
integer |
2 |
Number of fully connected layers |
model.head.fc_dropout |
float |
0.5 |
Activation to use between fully connected layers |
model.head.activation |
string |
‘relu’ |
Dropout to apply to the input hidden states |
model.punct_head.use_transrormer_init |
bool |
True |
Whether to initialize the weights of the classifier head with the same approach used in Transformer |
training_ds.text_file |
string |
text_train.txt |
Name of the text training file located at data_dir |
training_ds.labels_file |
string |
labels_train.txt |
Name of the labels training file located at data_dir |
training_ds.shuffle |
bool |
True |
Whether to shuffle the training data |
training_ds.num_samples |
integer |
-1 |
Number of samples to use from the training dataset; -1 means all |
training_ds.batch_size |
integer |
64 |
Training data batch size |
validation_ds.text_file |
string |
text_dev.txt |
Name of the text file for evaluation, located at data_dir |
validation_ds.labels_file |
string |
labels_dev.txt |
Name of the labels dev file located at data_dir |
validation_ds.shuffle |
bool |
False |
Whether to shuffle the dev data |
validation_ds.num_samples |
integer |
-1 |
Number of samples to use from the dev set; -1 means all |
validation_ds.batch_size |
integer |
64 |
Dev set batch size |
optim.name |
string |
adam |
Optimizer to use for training |
optim.lr |
float |
5e-5 |
Learning rate to use for training |
optim.weight_decay |
float |
0 |
Weight decay to use for training |
optim.sched.name |
string |
WarmupAnnealing |
Warmup schedule |
optim.sched.warmup_ratio |
float |
0.1 |
Warmup ratio |
Example of the command for training the model:
tlt token_classification train [-h] \
-e /specs/nlp/token_classification/train.yaml \
-r /results/token_classification/train/ \
-g 1 \
-k $KEY
data_dir=/path/to/data_dir \
model.label_ids=/path/to/label_ids.csv \
trainer.max_epochs=5 \
training_ds.num_samples=-1 \
validation_ds.num_samples=-1
Required Arguments for Training¶
-e: The experiment specification file to set up training.-r: Path to the directory to store the results/logs. Note, the trained-model.tlt would be saved in thisspecified folder under a subfolder checkpoints; in our case it will be saved here: /results/token_classification/train/checkpoints/trained-model.tlt
-k: Encryption keydata_dir: Path to the data_dir with the processed data files.model.label_ids: Path to the label_ids.csv file, usually stored at data_dir
Optional Arguments¶
-h, --help: Show this help message and exit-g: The number of GPUs to be used in evaluation in a multi-GPU scenario (default: 1).Other arguments to override fields in the specification file.
Note
While the arguments are defined in the spec file, if you wish to override these parameter definitions in the spec file and experiment with them, you may do so over command line by simply defining the param. For example, the sample spec file mentioned above has validation_ds.batch_size set to 64. However, if you see that the GPU utilization can be optimized further by using larger a batch size, you may override to the desired value, by adding the field validation_ds.batch_size=128 over command line.
You may repeat this with any of the parameters defined in the sample spec file.
Snippets of the output log from executing the token_classification train command:
# complete model's spec file will be shown
[NeMo I train:93] Spec file:
restore_from: ???
exp_manager:
explicit_log_dir: /results/token_classification/train/
exp_dir: null
name: trained-model
version: null
use_datetime_version: true
resume_if_exists: true
resume_past_end: false
resume_ignore_no_checkpoint: true
create_tensorboard_logger: false
summary_writer_kwargs: null
create_wandb_logger: false
wandb_logger_kwargs: null
create_checkpoint_callback: true
checkpoint_callback_params:
filepath: null
monitor: val_loss
verbose: true
save_last: true
save_top_k: 3
save_weights_only: false
mode: auto
period: 1
prefix: null
postfix: .tlt
save_best_model: false
files_to_copy: null
model:
tokenizer:
tokenizer_name: ...
...
[NeMo I exp_manager:186] Experiments will be logged at /results/token_classification/train/
# The dataset will be processed and tokenized
[NeMo I token_classification_model:61] Reusing label_ids file found at data_dir/label_ids.csv.
Using bos_token, but it is not set yet.
Using eos_token, but it is not set yet.
[NeMo I token_classification_model:105] Setting model.dataset.data_dir to data_dir.
[NeMo I 2021-01-21 17:57:14 token_classification_utils:54] Processing data_dir/labels_train.txt
[NeMo I 2021-01-21 17:57:14 token_classification_utils:75] Using provided labels mapping {'O': 0, 'B-GPE': 1, 'B-LOC': 2, 'B-MISC': 3, 'B-ORG': 4, 'B-PER': 5, 'B-TIME': 6, 'I-GPE': 7, 'I-LOC': 8, 'I-MISC': 9, 'I-ORG': 10, 'I-PER': 11, 'I-TIME': 12}
[NeMo I 2021-01-21 17:57:15 token_classification_utils:101] Three most popular labels in data_dir/labels_train.txt:
[NeMo I 2021-01-21 17:57:15 data_preprocessing:131] label: 0, 18417 out of 21717 (84.80%).
[NeMo I 2021-01-21 17:57:15 data_preprocessing:131] label: 2, 829 out of 21717 (3.82%).
[NeMo I 2021-01-21 17:57:15 data_preprocessing:131] label: 6, 433 out of 21717 (1.99%).
[NeMo I 2021-01-21 17:57:15 token_classification_utils:103] Total labels: 21717. Label frequencies - {0: 18417, 2: 829, 6: 433, 4: 357, 11: 352, 5: 349, 1: 338, 10: 281, 8: 181, 12: 142, 3: 21, 9: 12, 7: 5}
[NeMo I 2021-01-21 17:57:15 token_classification_utils:112] Class Weights: {0: 0.09070632901875775, 2: 2.015124802820822, 6: 3.858056493160419, 4: 4.679379444085327, 11: 4.7458479020979025, 5: 4.786643156270664, 1: 4.942421483841602, 10: 5.9449767314535995, 8: 9.229494262643433, 12: 11.764355362946912, 3: 79.54945054945055, 9: 139.21153846153845, 7: 334.10769230769233}
[NeMo I 2021-01-21 17:57:15 token_classification_utils:116] Class weights saved to data_dir/labels_train_weights.p
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:116] Setting Max Seq length to: 64
[NeMo I 2021-01-21 17:57:19 data_preprocessing:295] Some stats of the lengths of the sequences:
[NeMo I 2021-01-21 17:57:19 data_preprocessing:301] Min: 6 | Max: 64 | Mean: 26.357 | Median: 26.0
[NeMo I 2021-01-21 17:57:19 data_preprocessing:303] 75 percentile: 32.00
[NeMo I 2021-01-21 17:57:19 data_preprocessing:304] 99 percentile: 51.00
[NeMo W 2021-01-21 17:57:19 token_classification_dataset:145] 0 are longer than 64
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:148] *** Example ***
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:149] i: 0
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:150] subtokens: [CLS] new zealand ' s cricket team has scored a morale - boost ##ing win over bangladesh in the first of three one - day internationals in new zealand . [SEP]
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:151] loss_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:152] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:153] subtokens_mask: 0 1 1 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:155] labels: 0 2 8 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 6 12 12 12 12 12 0 0 2 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:19 token_classification_dataset:264] features saved to data_dir/cached_text_train.txt_BertTokenizer_128_30522_-1
[NeMo I 2021-01-21 17:57:19 token_classification_utils:54] Processing data_dir/labels_dev.txt
[NeMo I 2021-01-21 17:57:19 token_classification_utils:75] Using provided labels mapping {'O': 0, 'B-GPE': 1, 'B-LOC': 2, 'B-MISC': 3, 'B-ORG': 4, 'B-PER': 5, 'B-TIME': 6, 'I-GPE': 7, 'I-LOC': 8, 'I-MISC': 9, 'I-ORG': 10, 'I-PER': 11, 'I-TIME': 12}
[NeMo I 2021-01-21 17:57:20 token_classification_utils:101] Three most popular labels in data_dir/labels_dev.txt:
[NeMo I 2021-01-21 17:57:20 data_preprocessing:131] label: 0, 18266 out of 21775 (83.89%).
[NeMo I 2021-01-21 17:57:20 data_preprocessing:131] label: 2, 809 out of 21775 (3.72%).
[NeMo I 2021-01-21 17:57:20 data_preprocessing:131] label: 6, 435 out of 21775 (2.00%).
[NeMo I 2021-01-21 17:57:20 token_classification_utils:103] Total labels: 21775. Label frequencies - {0: 18266, 2: 809, 6: 435, 4: 418, 11: 414, 5: 392, 1: 351, 10: 351, 8: 174, 12: 146, 7: 8, 3: 8, 9: 3}
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:116] Setting Max Seq length to: 70
[NeMo I 2021-01-21 17:57:24 data_preprocessing:295] Some stats of the lengths of the sequences:
[NeMo I 2021-01-21 17:57:24 data_preprocessing:301] Min: 7 | Max: 70 | Mean: 26.437 | Median: 26.0
[NeMo I 2021-01-21 17:57:24 data_preprocessing:303] 75 percentile: 33.00
[NeMo I 2021-01-21 17:57:24 data_preprocessing:304] 99 percentile: 50.00
[NeMo W 2021-01-21 17:57:24 token_classification_dataset:145] 0 are longer than 70
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:148] *** Example ***
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:149] i: 0
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:150] subtokens: [CLS] hamas refuses to recognize israel , and has vowed to undermine palestinian leader mahmoud abbas ' s efforts to make peace with the jewish state . [SEP]
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:151] loss_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:152] input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:153] subtokens_mask: 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:155] labels: 0 4 0 0 0 2 0 0 0 0 0 0 1 0 5 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[NeMo I 2021-01-21 17:57:24 token_classification_dataset:264] features saved to data_dir/cached_text_dev.txt_BertTokenizer_128_30522_-1
[NeMo I 2021-01-21 17:00:09 modelPT:830] Optimizer config = Adam (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
lr: 5e-05
weight_decay: 0.0
)
[NeMo I 2021-01-21 17:00:09 lr_scheduler:621] Scheduler "<nemo.core.optim.lr_scheduler.WarmupAnnealing object at 0x7f3b6d05f400>"
will be used during training (effective maximum steps = 16) -
Parameters :
(warmup_steps: null
warmup_ratio: 0.1
last_epoch: -1
max_steps: 16
)
initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/1
[NeMo I 2021-01-21 17:00:11 modelPT:704] No optimizer config provided, therefore no optimizer was created
110 M Trainable params
0 Non-trainable params
110 M Total params
Validation sanity check: 50%|████████████████████████████▌ | 1/2 [00:00<00:00, 1.47it/s][NeMo I 2021-01-21 17:00:13 token_classification_model:178]
label precision recall f1 support
O (label_id: 0) 82.08 100.00 90.16 2300
B-GPE (label_id: 1) 0.00 0.00 0.00 41
B-LOC (label_id: 2) 0.00 0.00 0.00 119
B-MISC (label_id: 3) 0.00 0.00 0.00 2
B-ORG (label_id: 4) 0.00 0.00 0.00 71
B-PER (label_id: 5) 0.00 0.00 0.00 62
B-TIME (label_id: 6) 0.00 0.00 0.00 56
I-GPE (label_id: 7) 0.00 0.00 0.00 4
I-LOC (label_id: 8) 0.00 0.00 0.00 18
I-MISC (label_id: 9) 0.00 0.00 0.00 0
I-ORG (label_id: 10) 0.00 0.00 0.00 52
I-PER (label_id: 11) 0.00 0.00 0.00 61
I-TIME (label_id: 12) 0.00 0.00 0.00 16
-------------------
micro avg 82.08 82.08 82.08 2802
macro avg 6.84 8.33 7.51 2802
weighted avg 67.38 82.08 74.01 2802
Training: 0it [00:00, ?it/s]
[NeMo I 2021-01-21 17:00:38 train:124] Experiment logs saved to 'output'
[NeMo I 2021-01-21 17:00:38 train:127] Trained model saved to 'output/checkpoints/trained-model.tlt'
INFO: Internal process exited
Important Parameters¶
Below is the list of parameters that could help improve the model:
classification head parameters:
the number of layers in the classification head (model.head.num_fc_layers)
dropout value between layers (model.head.fc_dropout)
optimizer (model.optim.name, for example, adam)
learning rate (model.optim.lr, for example, 5e-5)
Fine-tuning a Model on a Different Dataset¶
In the previous section, Training a token classification model,
the Token Classification (NER) model was initialized with a pre-trained language model, but the classifiers were trained from scratch. Now that a user has trained the Token Classification model successfully (e.g., called trained-model.tlt), there may be scenarios where users are required to retrain this trained-model.tlt on a new smaller dataset. TLT conversational AI applications provide a separate tool called fine-tune to enable this.
Note
Labels from the dataset that is used for fine-tuning, should be a subset of the labels of the pre-trained .tlt model. If it is not the case, use the tlt token_classification train with your data.
Example for spec for fine-tuning of the model:
trainer:
max_epochs: 5
data_dir: ???
# Fine-tuning settings: training dataset.
finetuning_ds:
num_samples: -1 # number of samples to be considered, -1 means all the dataset
# Fine-tuning settings: validation dataset.
validation_ds:
num_samples: -1 # number of samples to be considered, -1 means all the dataset
# Fine-tuning settings: different optimizer.
optim:
name: adam
lr: 1e-5
Parameter |
Data Type |
Default |
Description |
data_dir |
string |
– |
Path to the data converted to the specified above format |
trainer.max_epochs |
integer |
5 |
Maximum number of epochs to train the model |
finetuning_ds.text_file |
string |
text_train.txt |
Name of the text training file located at data_dir |
finetuning_ds.labels_file |
string |
labels_train.txt |
Name of the labels training file located at data_dir |
finetuning_ds.shuffle |
bool |
True |
Whether to shuffle the training data |
finetuning_ds.num_samples |
integer |
-1 |
Number of samples to use from the training dataset; -1 means all |
finetuning_ds.batch_size |
integer |
64 |
Training data batch size |
validation_ds.text_file |
string |
text_dev.txt |
Name of the text file for evaluation, located at data_dir |
validation_ds.labels_file |
string |
labels_dev.txt |
Name of the labels dev file located at data_dir |
validation_ds.shuffle |
bool |
False |
Whether to shuffle the dev data |
validation_ds.num_samples |
integer |
-1 |
Number of samples to use from the dev set; -1 means all |
validation_ds.batch_size |
integer |
64 |
Dev set batch size |
optim.name |
string |
adam |
Optimizer to use for training |
optim.lr |
float |
1e-5 |
Learning rate to use for training |
Use the following command to fine-tune the model:
tlt token_classification finetune [-h] \
-e /specs/nlp/token_classification/finetune.yaml \
-r /results/token_classification/finetune/ \
-m /results/token_classification/train/checkpoints/trained-model.tlt \
-g 1 \
data_dir=PATH_TO_DATA \
trainer.max_epochs=5 \
-k $KEY
Required Arguments for Fine-tuning¶
-h, --help: Show this help message and exit-e: The experiment specification file to set up fine-tuning.-r: Path to the directory to store the results/logs. Note, the finetuned-model.tlt would be saved in thisspecified folder under a subfolder checkpoints; in our case it will be saved here: /results/token_classification/train/checkpoints/trained-model.tlt
-m: Path to the pre-trained model to use for fine-tuning.data_dir: Path to data directory with the pre-processed data to use for fine-tuning-k: Encryption key
Optional Arguments¶
-g: The number of GPUs to be used in evaluation in a multi-GPU scenario (default: 1).Other arguments to override fields in the specification file.
Output log for the tlt token_calssification finetune command:
Model restored from '/path/to/trained-model.tlt'
# The rest of the log is similar to the output log snippet for :code:`token_classification train`.
Evaluating a Trained Model¶
Spec example to evaluate the pre-trained model:
restore_from: trained-model.tlt
data_dir: ???
# Test settings: dataset.
test_ds:
text_file: text_dev.txt
labels_file: labels_dev.txt
batch_size: 1
shuffle: false
num_samples: -1 # number of samples to be considered, -1 means the whole the dataset
Use the following command to evaluate the model:
tlt token_classification evaluate [-h] \
-e /specs/nlp/token_classification/evaluate.yaml \
-r /results/token_classification/evaluate/ \
-g 1 \
-m /results/token_classification/train/checkpoints/trained-model.tlt \
-k $KEY \
data_dir=/path/to/data_dir
Required Arguments for Evaluation¶
-e: The experiment specification file to set up evaluation.-r: Path to the directory to store the results.data_dir: Path to data directory with the pre-processed data to use for evaluation-m: Path to the pre-trained model checkpoint for evaluation. Should be a.tltfile.-k: Encryption key
Optional Arguments for Evaluation¶
-h, --help: Show this help message and exit
Parameter |
Data Type |
Default |
Description |
restore_from |
string |
trained-model.tlt |
Path to the pre-trained model |
data_dir |
string |
– |
Path to the data converted to the specified above format |
test_ds.text_file |
string |
text_dev.txt |
Name of the text file to run evaluation on located at data_dir |
test_ds.labels_file |
string |
labels_dev.txt |
Name of the labels dev file located at data_dir |
test_ds.shuffle |
bool |
False |
Whether to shuffle the dev data |
test_ds.num_samples |
integer |
-1 |
Number of samples to use from the dev set; -1 means all |
test_ds.batch_size |
integer |
64 |
Dev set batch size |
token_classification evaluate generates a classification report that includes the following metrics:
PrecisionRecallF1
More details about these metrics can be found here.
Output log for token_classification evaluate (note, the values below are for demonstration purposes only):
label precision recall f1 support
O (label_id: 0) 83.89 100.00 91.24 18266
B-GPE (label_id: 1) 0.00 0.00 0.00 351
B-LOC (label_id: 2) 0.00 0.00 0.00 809
B-MISC (label_id: 3) 0.00 0.00 0.00 8
B-ORG (label_id: 4) 0.00 0.00 0.00 418
B-PER (label_id: 5) 0.00 0.00 0.00 392
B-TIME (label_id: 6) 0.00 0.00 0.00 435
I-GPE (label_id: 7) 0.00 0.00 0.00 8
I-LOC (label_id: 8) 0.00 0.00 0.00 174
I-MISC (label_id: 9) 0.00 0.00 0.00 3
I-ORG (label_id: 10) 0.00 0.00 0.00 351
I-PER (label_id: 11) 0.00 0.00 0.00 414
I-TIME (label_id: 12) 0.00 0.00 0.00 146
-------------------
micro avg 83.89 83.89 83.89 21775
macro avg 6.45 7.69 7.02 21775
weighted avg 70.37 83.89 76.53 21775
Testing: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:39<00:00, 25.59it/s]
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'f1': tensor(7.0182, device='cuda:0'),
'precision': tensor(6.4527, device='cuda:0'),
'recall': tensor(7.6923, device='cuda:0'),
'test_loss': tensor(1.0170, device='cuda:0')}
Running Inference using a Trained Model¶
During inference, a batch of input sentences, listed in the spec files, are passed through the trained model to add token classification label.
To run inference on the model, specify the list of examples in the spec, for example:
input_batch:
- 'We bought four shirts from the Nvidia gear store in Santa Clara.'
- 'Nvidia is a company.'
To run inference:
tlt token_classification infer [-h] \
-e /specs/nlp/token_classification/infer.yaml \
-r /results/token_classification/infer/ \
-g 1 \
-m /results/token_classification/checkpoints/trained-model.tlt \
-k $KEY
Required Arguments for Inference¶
-e: The experiment specification file to set up inference. This requires theinput_batchwith the list of examples to run inference on.-r: Path to the directory to store the results.-m: Path to the pre-trained model checkpoint from which to infer. Should be a.tltfile.-k: Encryption key
Optional Arguments¶
-h, --help: Show this help message and exit-g: The number of GPUs to be used for fine-tuning in a multi-GPU scenario (default: 1).Other arguments to override fields in the specification file.
Output log sample:
Query : we bought four shirts from the nvidia gear store in santa clara.
Result: we bought four shirts from the nvidia[B-LOC] gear store in santa[B-LOC] clara[I-LOC].
Nvidia is a company.
Result: Nvidia[B-ORG] is a company.
Model Export¶
A pre-trained model could be exported to JARVIS format (this format contains model checkpoint along with model artifacts required for successful deployment of the trained .tlt models to Jarvis Services). For more details about Jarvis, see this.
An example of the spec file for model export:
# Name of the .tlt EFF archive to be loaded/model to be exported.
restore_from: trained-model.tlt
# Set export format: JARVIS
export_format: JARVIS
# Output EFF archive containing model checkpoint and artifacts required for Jarvis Services
export_to: exported-model.ejrvs
Parameter |
Data Type |
Default |
Description |
restore_from |
string |
trained-model.tlt |
Path to the pre-trained model |
export_format |
string |
JARVIS |
Export format: JARVIS |
export_to |
string |
exported-model.ejrvs |
Path to the exported model |
To export a pre-trained model for deployment, run:
### For export to Jarvis format
tlt token_classification export [-h] \
-e /specs/nlp/token_classification/export.yaml \
-r /results/token_classification/export/ \
-m /results/token_classification/checkpoints/trained-model.tlt \
-k $KEY
export_format=JARVIS
Required Arguments for Export¶
-e: The experiment specification file to set up inference. This requires theinput_batchwith the list of examples to run inference on.-r: Path to the directory to store the results.-m: Path to the pre-trained model checkpoint from which to infer. Should be a.tltfile.-k: Encryption key
Optional Arguments for Export¶
-h, --help: Show this help message and exitexport_to: To change the default name of the exported model
Output log:
Spec file:
restore_from: path/to/trained-model.tlt
export_to: exported-model.ejrvs
export_format: JARVIS
exp_manager:
task_name: export
explicit_log_dir: /results/token_classification/export/
encryption_key: $KEY
Experiment logs saved to '/results/token_classification/export/'
Exported model to '/results/token_classification/export/exported-model.ejrvs'