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local.py
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# (C) Copyright IBM Corp. 2024.
# 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.
################################################################################
import os
from data_processing.data_access import DataAccessLocal
from dpk_hap.transform import HAPTransform
# create parameters
input_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), "../test-data/input"))
output_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), "../output"))
local_conf = {
"input_folder": input_folder,
"output_folder": output_folder,
}
hap_params = {
"model_name_or_path": "ibm-granite/granite-guardian-hap-38m",
"annotation_column": "hap_score",
"doc_text_column": "contents",
"inference_engine": "CPU",
"max_length": 512,
"batch_size": 128,
}
if __name__ == "__main__":
data_access = DataAccessLocal(local_conf)
hap_params["data_access"] = data_access
# Use the local data access to read a parquet table.
table, _ = data_access.get_table(os.path.join(input_folder, "test1.parquet"))
print(f"input table: {table}")
# Create and configure the transform.
transform = HAPTransform(hap_params)
# Transform the table
table_list, metadata = transform.transform(table)
for tb in table_list:
print(f"\noutput table: {tb}")
print(f"output metadata : {metadata}")