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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 | |
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| # 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 | |
| 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() | |