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"""
Copyright 2024 Kyle Luoma
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from transformers import AutoTokenizer, CanineForSequenceClassification, pipeline
import torch
import pandas as pd
import tokenprocessing as tp
class CanineIdentifierClassifier:
"""
A classifier for identifying word naturalness using a pre-trained text analysis model.
Classifies words as Regular (label N1), Low (label N2), or Least (label N3) natural.
Attributes:
model_name (str): The name of the model used for classification.
checkpoint (int): The checkpoint number of the model.
id2label (dict): A dictionary mapping label IDs to label names.
label2id (dict): A dictionary mapping label names to label IDs.
classifier (pipeline): The sentiment analysis pipeline used for classification.
identifiers (pd.DataFrame): A DataFrame containing identifiers to classify.
Methods:
__init__(identifiers=pd.DataFrame()):
Initializes the classifier with the given identifiers DataFrame.
do_batch_job(ident_df=None, save_as_excel=False, make_tag=True):
Performs batch classification on the given DataFrame of identifiers.
Args:
ident_df (pd.DataFrame, optional): The DataFrame of identifiers to classify. Defaults to None.
save_as_excel (bool, optional): Whether to save the results as an Excel file. Defaults to False.
make_tag (bool, optional): Whether to add a token tag to the text before classification. Defaults to True.
classify_identifier(identifier, make_tag=True):
Classifies a single identifier.
Args:
identifier (str): The identifier to classify.
make_tag (bool, optional): Whether to add a token tag to the identifier before classification. Defaults to True.
Returns:
list: The classification result.
"""
def __init__(self, identifiers = pd.DataFrame()):
self.model_name = "kyleluoma/SNAILS-word-naturalness-classifier"
self.checkpoint = 5590
self.id2label = {0: "N1", 1: "N2", 2: "N3"}
self.label2id = {"N1": 0, "N2": 1, "N3": 2}
self.classifier = pipeline(
"sentiment-analysis",
model = "kyleluoma/SNAILS-word-naturalness-classifier",
device=0
)
self.identifiers = identifiers
def do_batch_job(self, ident_df: pd.DataFrame = None, save_as_excel: bool = False, make_tag: bool = True):
"""
Processes a batch of text data through a classifier and optionally saves the results to an Excel file.
Args:
ident_df (pd.DataFrame, optional): DataFrame containing the text data to be classified.
If None, uses self.identifiers. Defaults to None.
save_as_excel (bool, optional): If True, saves the results to an Excel file. Defaults to False.
make_tag (bool, optional): If True, appends a token tag to the text before classification. Defaults to True.
Returns:
None
"""
auto_scores = []
if ident_df == None:
ident_df = self.identifiers
for row in ident_df.itertuples():
if make_tag:
pred = classifier(row.text + tp.make_token_tag(row.text))
else:
pred = self.classifier(row.text)
print(pred)
auto_scores.append(pred[0]['label'])
ident_df["prediction"] = auto_scores
if save_as_excel:
ident_df[['text', 'prediction', 'category']].to_excel(
f"./classifier-inference-results/{self.model_name}-cp-{self.checkpoint}.xlsx",
index=False
)
def classify_identifier(self, identifier: str, make_tag: bool = True):
"""
Classifies the given identifier using the classifier.
Args:
identifier (str): The identifier to classify.
make_tag (bool, optional): If True, appends a token tag to the identifier before classification. Defaults to True.
Returns:
The classification result of the identifier.
"""
identifier = str(identifier)
if make_tag:
identifier += (" " + tp.make_token_tag(identifier))
pred = self.classifier(identifier)
# print("Classifying", identifier, "as", pred)
return pred
if __name__ == "__main__":
classifier = CanineIdentifierClassifier()
print(classifier.classify_identifier("WinterWeather"))
print(classifier.classify_identifier("WntrWthr"))
print(classifier.classify_identifier("WWth"))
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