Training#
Training on Framenet 1.7#
If you want to train a new model on the Framenet 1.7 dataset yourself, you can run the training script like below:
python -m frame_semantic_transformer.scripts.train \
--base-model t5-base \
--use-gpu \
--batch-size 8 \
--epochs 10 \
--learning-rate 5e-5 \
--output-dir ./outputs
Training uses Pytorch Lightning behind the scenes, and will place tensorboard logs into ./lightning_logs as it trains.
If you need more control, you can also directly import the train() method from frame_semantic_transformer.train and run training directly in code.
Training on custom datasets#
By default FrameSemanticTransformer assumes you want to train on the framenet 1.7 dataset, and will download this dataset during training and inference. If you’d like to train on a different dataset, for example a different language version of framenet, or a custom frame dataset, you’ll need to provide custom data loaders to load the data for your frames. Specifically, this requires extending an instance of TrainingLoader to load your training data, and InstanceLoader to load all the frames and lexical units from your custom dataset.
These loaders have the following signatures:
class InferenceLoader(ABC):
def setup(self) -> None:
"""
Perform any setup required, e.g. downloading needed data
"""
pass
@abstractmethod
def load_frames(self) -> list[Frame]:
"""
Load the full list of frames to be used during inference
"""
pass
@abstractmethod
def normalize_lexical_unit_text(self, lu: str) -> str:
"""
Normalize a lexical unit like "takes.v" to "take".
"""
pass
def prioritize_lexical_unit(self, lu: str) -> bool:
"""
Check if the lexical unit is relatively rare, so that it should be considered "high information"
"""
return len(self.normalize_lexical_unit_text(lu)) >= 6
class TrainingLoader(ABC):
def setup(self) -> None:
"""
Perform any setup required, e.g. downloading needed data.
"""
pass
@abstractmethod
def get_augmentations(self) -> list[DataAugmentation]:
"""
Get a list of augmentations to apply to the training data
"""
pass
@abstractmethod
def load_training_data(self) -> list[FrameAnnotatedSentence]:
"""
Load the training data
"""
pass
@abstractmethod
def load_validation_data(self) -> list[FrameAnnotatedSentence]:
"""
Load the validation data
"""
pass
@abstractmethod
def load_test_data(self) -> list[FrameAnnotatedSentence]:
"""
Load the test data
"""
pass
The most difficult part of this is returning instances of Frame for the load_frames method of InstanceLoader, and FrameAnnotatedSentence from the TrainingLoader. These are simple Python dataclasses with the following signatures:
@dataclass
class Frame:
"""
Representation of a FrameNet frame
For training on your own data, you can use this class to represent your own frames
"""
name: str
core_elements: list[str]
non_core_elements: list[str]
lexical_units: list[str]
@dataclass
class FrameAnnotatedSentence:
"""
Representation of a sentence with annotations for use in training
If training on your own data, you'll need to create instances of this class for your training sentences
"""
text: str
annotations: list[FrameAnnotation]
@dataclass
class FrameAnnotation:
"""
A single frame occuring in a sentence
"""
frame: str
trigger_locs: list[int]
frame_elements: list[FrameElementAnnotation]
@dataclass
class FrameElementAnnotation:
"""
A single frame element in a frame annotation.
Includes the name of the frame element and the start and end locations of the frame element in the sentence
"""
name: str
start_loc: int
end_loc: int
Hopefully the meaning of the fields in the Frame dataclass should be obvious when looking at a sample FrameNet Frame.
The FrameAnnotatedSentence class is a bit trickier, as this represents an annotated training sample. The text field should be a single sentence, and all start_loc, end_loc, and trigger_locs are indices which refer to positions in the text.
FrameAnnotation refers to a single frame inside of the sentence. There may be multiple frames in a sentence, which is why the annotations field on FrameAnnotatedSentence is a list of FrameAnnotation`s. The `trigger_locs field in FrameAnnotation is just the start locations of any triggers in the sentence for the frame. End locations of triggers are not used currently by FrameSemanticTransformer as it makes the labeling more complicated. There is an implicit assumptions here, which is that a single location in a sentence can only be a trigger for 1 frame.
FrameElement refers to the location of a frame element in the sentence for the frame being annotated. Frame elements do require both start and end locations in the sentence.
For instance, for the sentence “It was no use trying the lift”, we have 2 frames “Attempt_means” at index 14 (the word “trying”), and “Connecting_architecture” at index 25 (the word “lift”). “Attempt_means” has a single frame element “Means” with text “the lift” (index 21 - 29), and “Connecting_architecture” likewise also has a single frame element “Part” with text “lift” (index 25 - 29). This would look like the following when turned into a FrameAnnotatedSentence instance:
annotated_sentence = FrameAnnotatedSentence(
text="It was no use trying the lift",
annotations=[
FrameAnnotation(
frame="Attempt_means",
trigger_locs=[14],
frame_elements=[
FrameElementAnnotation(
name="Means",
start_loc=21,
end_loc=29,
)
]
),
FrameAnnotation(
frame="Connecting_architecture",
trigger_locs=[25],
frame_elements=[
FrameElementAnnotation(
name="Part",
start_loc=25,
end_loc=29,
)
]
)
]
)
After creating custom TrainingLoader and InferenceLoader classes, you’ll need to pass these classes in when training a new model and when running inference after training. An example of this is shown below:
from frame_semantic_transformer import TrainingLoader, InferenceLoader, FrameSemanticTransformer
from frame_semantic_transformer.training import train
class MyCustomInferenceLoader(InferenceLoader):
...
class MyCustomTrainingLoader(TrainingLoader):
...
my_inference_loader = MyCustomInferenceLoader()
my_training_loader = MyCustomTrainingLoader()
my_model, my_tokenizer = train(
base_model=f"t5-small",
batch_size=32,
max_epochs=16,
lr=5e-5,
inference_loader=my_inference_loader,
training_loader=my_training_loader,
)
my_model.save_pretrained('./my_model')
my_tokenizer.save_pretrained('./my_model')
# after training...
frame_transformer = FrameSemanticTransformer('./my_model', inference_loader=my_inference_loader)
frame_transformer.detect_frames(...)
You can see examples of how these classes are implemented for the default framenet 1.7 by looking at Framenet17InferenceLoader.py and Framenet17TrainingLoader.py. There’s also an example of creating custom loaders for Swedish in the following Colab notebook: 
If you have trouble creating and using custom loader classes please don’t hesitate to open an issue!