Regression
The goal of regression in natural language processing is to predict a single, continuous target value for each example in the dataset. A transformer-based regression model typically consists of a transformer model with a fully-connected layer on top of it. The fully-connected layer will have a single output neuron which predicts the target.
Note: You must configure the model’s args dict and set regression
to True
.
Note: You must set num_labels
to 1
.
Tip: Refer to ClassificationModel for details on configuring a classification model.
Minimal Start
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from simpletransformers.classification import ClassificationModel, ClassificationArgs
import pandas as pd
import logging
logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)
# Preparing train data
train_data = [
["Aragorn was the heir of Isildur", 1.0],
["Frodo was the heir of Isildur", 0.0],
["Pippin is stronger than Merry", 0.3],
]
train_df = pd.DataFrame(train_data)
train_df.columns = ["text", "labels"]
# Preparing eval data
eval_data = [
["Theoden was the king of Rohan", 1.0],
["Merry was the king of Rohan", 0.0],
["Aragorn is stronger than Boromir", 0.5],
]
eval_df = pd.DataFrame(eval_data)
eval_df.columns = ["text", "labels"]
# Enabling regression
# Setting optional model configuration
model_args = ClassificationArgs()
model_args.num_train_epochs = 1
model_args.regression = True
# Create a ClassificationModel
model = ClassificationModel(
"roberta",
"roberta-base",
num_labels=1,
args=model_args
)
# Train the model
model.train_model(train_df)
# Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
# Make predictions with the model
predictions, raw_outputs = model.predict(["Sam was a Wizard"])