multi_run.py

import logging

import pandas as pd

from simpletransformers.classification import (
    MultiLabelClassificationModel,
    MultiLabelClassificationArgs,
)

logging.basicConfig(level=logging.INFO)
transformers_logger = logging.getLogger("transformers")
transformers_logger.setLevel(logging.WARNING)


# Train and Evaluation data needs to be in a Pandas Dataframe of two columns. The first column is the text with type str, and the second column in the label with type int.
train_data = [["Example sentence 1 for multilabel", [1, 1, 1, 1, 0, 1]]] + [
    ["This thing is entirely different from the other thing. ", [0, 1, 1, 0, 0, 0]]
]
train_df = pd.DataFrame(train_data, columns=["text", "labels"])


eval_data = [
    ["Example sentence belonging to class 1", [1, 1, 1, 1, 0, 1]],
    [
        "This thing should be entirely different from the other thing. ",
        [0, 0, 0, 0, 1, 0],
    ],
]
eval_df = pd.DataFrame(eval_data, columns=["text", "labels"])

model_args = MultiLabelClassificationArgs()
model_args.train_custom_parameters_only = True
model_args.reprocess_input_data = True
model_args.overwrite_output_dir = True
model_args.learning_rate = 3e-3
model_args.custom_parameter_groups = [
    {
        "params": ["classifier.weight"],
        "lr": 1e-3,
    },
    {
        "params": ["classifier.bias"],
        "lr": 1e-3,
        "weight_decay": 0.0,
    },
    {
        "params": ["classifier_1.weight"],
        "lr": 1e-3,
    },
    {
        "params": ["classifier_1.bias"],
        "lr": 1e-3,
        "weight_decay": 0.0,
    },
]

# train_args = {
#     "reprocess_input_data": True,
#     "overwrite_output_dir": True,
#     "evaluate_during_training": True,
#     "evaluate_during_training_verbose": True,
#     "logging_steps": 50,
#     "num_train_epochs": 1,
#     # "evaluate_during_training_steps": 1,
#     "best_model_dir": "TESTING BEST",
#     "wandb_project": "test-new-project",
#     "no_cache": True,
#     "use_early_stopping": True,
#     "max_seq_length": 3,
#     # "sliding_window": True,
#     # "fp16": False,
# }

# Create a MultiLabelClassificationModel
model = MultiLabelClassificationModel(
    "roberta",
    "pdelobelle/robbert-v2-dutch-base",
    num_labels=6,
    use_cuda=False,
    args=model_args,
    cuda_device=1,
)

# Train the model
model.train_model(train_df)

# # Evaluate the model
result, model_outputs, wrong_predictions = model.eval_model(eval_df)
print(result)

# print("\nmodel_outputs from model.eval_model()")
# print(model_outputs)

predictions, raw_outputs = model.predict(
    [
        "Example sentence belonging to class 1",
        "This thing should be entirely different from the other thing.",
        "This thing should be entirely different from the other thing.",
    ]
)
print(predictions)
print(raw_outputs)
exit()

predictions, raw_outputs = model.predict(
    [
        "Example sentence belonging to class 1",
        "This thing should be entirely different from the other thing.",
    ]
)

print("\npredictions from model.predict()")
print(predictions)

print("\nraw_outputs from model.predict()")
print(raw_outputs)


model.args["threshold"] = [0.9, 0.6, 0.5, 0.3, 0.2, 0.5]
print("\nAfter variable thresholds.")

predictions, raw_outputs = model.predict(
    [
        "Example sentence belonging to class 1",
        "This thing should be entirely different from the other thing.",
    ]
)

print("\npredictions from model.predict()")
print(predictions)

print("\nraw_outputs from model.predict()")
print(raw_outputs)


# ### SAVE AND LOAD TEST ###

# model = MultiLabelClassificationModel('albert', 'outputs/checkpoint-5', use_cuda=False, pos_weight=[1, 1, 1, 1, 1, 1], args=train_args)

# # Evaluate the model
# result, model_outputs, wrong_predictions = model.eval_model(eval_df)
# print(result)

# print('\nmodel_outputs from model.eval_model()')
# print(model_outputs)

# predictions, raw_outputs = model.predict(['Example sentence belonging to class 1', 'This thing should be entirely different from the other thing.'])

# print('\npredictions from model.predict()')
# print(predictions)

# print('\nraw_outputs from model.predict()')
# print(raw_outputs)


# model.args['threshold'] = [0.9, 0.6, 0.5, 0.3, 0.2, 0.5]
# print('\nAfter variable thresholds.')

# predictions, raw_outputs = model.predict(['Example sentence belonging to class 1', 'This thing should be entirely different from the other thing.'])

# print('\npredictions from model.predict()')
# print(predictions)

# print('\nraw_outputs from model.predict()')
# print(raw_outputs)