gobbli.augment.bert.model module¶
-
class
gobbli.augment.bert.model.
BERTMaskedLM
(data_dir=None, load_existing=False, use_gpu=False, nvidia_visible_devices='all', logger=None, **kwargs)[source]¶ Bases:
gobbli.model.base.BaseModel
,gobbli.augment.base.BaseAugment
BERT-based data augmenter. Applies masked language modeling to generate predictions for missing tokens using a trained BERT model.
Create a model.
- Parameters
data_dir¶ (
Optional
[Path
]) – Optional path to a directory used to store model data. If not given, a unique directory under GOBBLI_DIR will be created and used.load_existing¶ (
bool
) – If True,data_dir
should be a directory that was previously used to create a model. Parameters will be loaded to match the original model, and user-specified model parameters will be ignored. If False, the data_dir must be empty if it already exists.use_gpu¶ (
bool
) – If True, use the nvidia-docker runtime (https://github.com/NVIDIA/nvidia-docker) to expose NVIDIA GPU(s) to the container. Will cause an error if the computer you’re running on doesn’t have an NVIDIA GPU and/or doesn’t have the nvidia-docker runtime installed.nvidia_visible_devices¶ (
str
) – Which GPUs to make available to the container; ignored ifuse_gpu
is False. If not ‘all’, should be a comma-separated string: ex.1,2
.logger¶ (
Optional
[Logger
]) – If passed, use this logger for logging instead of the default module-level logger.**kwargs¶ – Additional model-specific parameters to be passed to the model’s
init()
method.
-
augment
(X, times=5, p=0.1)[source]¶ Return additional texts for each text in the passed array.
- Parameters
X¶ (
List
[str
]) – Input texts.times¶ (
int
) – How many texts to generate per text in the input.p¶ (
float
) – Probability of considering each token in the input for replacement. Note that some tokens aren’t able to be replaced by a given augmentation method and will be ignored, so the actual proportion of replaced tokens in your input may be much lower than this number.
- Return type
List
[str
]- Returns
Generated texts (length =
times * len(X)
).
-
build
()¶ Perform any pre-setup that needs to be done before running the model (building Docker images, etc).
-
property
class_weights_dir
¶ The root directory used to store initial model weights (before fine-tuning). These should generally be some pretrained weights made available by model developers. This directory will NOT be created by default; models should download their weights and remove the weights directory if the download doesn’t finish properly.
Most models making use of this directory will have multiple sets of weights and will need to store those in subdirectories under this directory.
- Return type
Path
- Returns
The path to the class-wide weights directory.
-
data_dir
()¶ - Return type
Path
- Returns
The main data directory unique to this instance of the model.
-
property
host_cache_dir
¶ Directory to be used for downloaded transformers files. Should be the same across all instances of the class, since these are generally static model weights/config files that can be reused.
-
property
image_tag
¶ - Return type
str
- Returns
The Docker image tag to be used for the BERT container.
-
property
info_path
¶ - Return type
Path
- Returns
The path to the model’s info file, containing information about the model including the type of model, gobbli version it was trained using, etc.
-
init
(params)[source]¶ See
gobbli.model.base.BaseModel.init()
.BERTMaskedLM parameters:
bert_model
(str
): Name of a pretrained BERT model to use. See the transformers docs for supported values.diversity
: 0 < diversity <= 1; determines the likelihood of selecting replacement words based on their predicted probability. At 1, the most probable words are most likely to be selected as replacements. As diversity decreases, likelihood of selection becomes less dependent on predicted probability.n_probable
: The number of probable tokens to consider for replacement.batch_size
: Number of documents to run through the BERT model at once.
-
property
logger
¶ - Return type
Logger
- Returns
A logger for derived models to use.
-
property
metadata_path
¶ - Return type
Path
- Returns
The path to the model’s metadata file containing model-specific parameters.
-
classmethod
model_class_dir
()¶ - Return type
Path
- Returns
A directory shared among all classes of the model.
-
property
weights_dir
¶ The directory containing weights for a specific instance of the model. This is the class weights directory by default, but subclasses might define this property to return a subdirectory based on a set of pretrained model weights.
- Return type
Path
- Returns
The instance-specific weights directory.