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# ###########################################################################
#
# CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
# (C) Cloudera, Inc. 2022
# All rights reserved.
#
# Applicable Open Source License: Apache 2.0
#
# NOTE: Cloudera open source products are modular software products
# made up of hundreds of individual components, each of which was
# individually copyrighted. Each Cloudera open source product is a
# collective work under U.S. Copyright Law. Your license to use the
# collective work is as provided in your written agreement with
# Cloudera. Used apart from the collective work, this file is
# licensed for your use pursuant to the open source license
# identified above.
#
# This code is provided to you pursuant a written agreement with
# (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
# this code. If you do not have a written agreement with Cloudera nor
# with an authorized and properly licensed third party, you do not
# have any rights to access nor to use this code.
#
# Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
# contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
# KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
# WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
# IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
# AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
# ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
# OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
# CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
# RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
# BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
# DATA.
#
# ###########################################################################
import os
from typing import List
from collections import defaultdict
from dataclasses import dataclass
import numpy as np
@dataclass
class StyleAttributeData:
source_attribute: str
target_attribute: str
examples: List[str]
cls_model_path: str
seq2seq_model_path: str
sbert_model_path: str = "sentence-transformers/all-MiniLM-L6-v2"
hf_base_url: str = "https://huggingface.co/"
def __post_init__(self):
self._make_attribute_selection_string()
self._make_attribute_AND_string()
self._make_attribute_THAN_string()
def _make_attribute_selection_string(self):
self.attribute_selecting_string = (
f"{self.source_attribute}-{self.target_attribute}"
)
def _make_attribute_AND_string(self):
self.attribute_AND_string = (
f"**{self.source_attribute}** and **{self.target_attribute}**"
)
def _make_attribute_THAN_string(self):
self.attribute_THAN_string = (
f"**{self.source_attribute}** than **{self.target_attribute}**"
)
def build_model_url(self, model_type: str):
"""
Build a complete HuggingFace url for the given `model_type`.
Args:
model_type (str): "cls", "seq2seq", "sbert"
"""
attr_name = f"{model_type}_model_path"
return os.path.join(self.hf_base_url, getattr(self, attr_name))
# instantiate data classes & collect all data class instances
DATA_PACKET = {
"subjective-to-neutral": StyleAttributeData(
source_attribute="subjective",
target_attribute="neutral",
examples=[
"another strikingly elegant four-door design for the bentley s3 continental came from james.",
"the band plays an engaging and contagious rhythm known as brega pop and calypso.",
"chemical abstracts service (cas), a prominent division of the american chemical society, is the world's leading source of chemical information.",
"the final fight scene is with the martial arts great, master ninja sho kosugi.",
],
cls_model_path="cffl/bert-base-styleclassification-subjective-neutral",
seq2seq_model_path="cffl/bart-base-styletransfer-subjective-to-neutral",
),
"informal-to-formal": StyleAttributeData(
source_attribute="informal",
target_attribute="formal",
examples=[
"that was funny LOL",
"btw - ur avatar looks familiar",
"i loooooooooooooooooooooooove going to the movies.",
"haha, thatd be dope",
],
cls_model_path="cointegrated/roberta-base-formality",
seq2seq_model_path="prithivida/informal_to_formal_styletransfer",
),
}
def format_classification_results(id2label: dict, cls_result):
"""
Formats classification output to be plotted using Altair.
Args:
id2label (dict): Transformer model's label dictionary
cls_result (List): Classification pipeline output
"""
labels = [v for k, v in id2label.items()]
format_cls_result = []
for i in range(len(labels)):
temp = defaultdict()
temp["type"] = labels[i].capitalize()
temp["value"] = round(cls_result[0]["distribution"][i], 4)
if i == 0:
temp["percentage_start"] = 0
temp["percentage_end"] = temp["value"]
else:
temp["percentage_start"] = 1 - temp["value"]
temp["percentage_end"] = 1
format_cls_result.append(temp)
return format_cls_result
def string_to_list_string(text: str):
return np.expand_dims(np.array(text), axis=0).tolist()
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