File size: 6,920 Bytes
82221ca fa7fec5 82221ca 23428ec 82221ca 946f92f a189dd1 946f92f fa7fec5 a189dd1 946f92f 23428ec 946f92f 23428ec a189dd1 82221ca 946f92f 82221ca 946f92f 82221ca 946f92f 82221ca 946f92f fa7fec5 82221ca fa7fec5 82221ca fa7fec5 82221ca 946f92f 82221ca 946f92f 82221ca a283e47 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
"""
Script file used for performing inference with an existing model.
"""
import torch
import nltk
from nltk.tokenize import sent_tokenize
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification
)
from scripts.config import (
BIN_REPO,
ML_REPO
)
class InferenceHandler:
"""A class that handles performing inference using the trained binary classification and multilabel regression models."""
def __init__(self, api_token: str):
"""Constructor for instantiating an InferenceHandler object.
Parameters
----------
api_token : str
A Hugging Face token with read/write access privileges to allow exporting the trained models (default is None).
"""
self.api_token = api_token
self.bin_tokenizer, self.bin_model = self._init_model_and_tokenizer(BIN_REPO)
self.ml_regr_tokenizer, self.ml_regr_model = self._init_model_and_tokenizer(ML_REPO)
nltk.download('punkt_tab')
def _init_model_and_tokenizer(self, repo_id: str):
"""Initializes a model and tokenizer for use in inference using the models path.
Parameters
----------
repo_id : str
The repository id (i.e., <owner username>/<repository name>).
Returns
-------
tuple[PreTrainedTokenizer | PreTrainedTokenizerFast, PreTrainedModel]
A tuple containing the tokenizer and model objects.
"""
tokenizer = AutoTokenizer.from_pretrained(repo_id, token=self.api_token)
model = AutoModelForSequenceClassification.from_pretrained(repo_id, token=self.api_token)
model.eval()
return tokenizer, model
def _encode_binary(self, text: str):
"""Preprocesses and tokenizes the input text for binary classification.
Parameters
----------
text : str
The input text to be preprocessed and tokenized.
Returns
-------
BatchEncoding
The preprocessed and tokenized input text.
"""
bin_tokenized_input = self.bin_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
return bin_tokenized_input
def _encode_multilabel(self, text: str):
"""Preprocesses and tokenizes the input text for multilabel regression.
Parameters
----------
text : str
The input text to be preprocessed and tokenized.
Returns
-------
BatchEncoding
The preprocessed and tokenized input text.
"""
ml_tokenized_input = self.ml_regr_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
return ml_tokenized_input
def _encode_input(self, text: str):
"""Preprocesses and tokenizes the input text sentiment classification (both models).
Parameters
----------
text : str
The input text to be preprocessed and tokenized.
Returns
-------
tuple[BatchEncoding, BatchEncoding]
A tuple containing preprocessed and tokenized input text for both the binary and multilabel regression models.
"""
bin_inputs = self._encode_binary(text)
ml_inputs = self._encode_multilabel(text)
return bin_inputs, ml_inputs
def classify_text(self, input: str):
"""Performs inference on the input text to determine the binary classification and the multilabel regression for the categories.
Determines whether the text is discriminatory. If it is discriminatory, it will then perform regression on the input text to determine the
assesed percentage that each category applies.
Parameters
----------
input : str
The input text to be classified.
Returns
-------
dict[str, Any]
The resulting classification and regression values for each category.
"""
result = {
'text_input': input,
'results': []
}
sent_res_arr = []
sentences = sent_tokenize(input)
for sent in sentences:
text_prediction, pred_class = self.discriminatory_inference(sent)
sent_result = {
'sentence': sent,
'binary_classification': {
'classification': text_prediction,
'prediction_class': pred_class
},
'multilabel_regression': None
}
if pred_class == 1:
ml_results = {
"Gender": None,
"Race": None,
"Sexuality": None,
"Disability": None,
"Religion": None,
"Unspecified": None
}
ml_infer_results = self.category_inference(sent)
for idx, key in enumerate(ml_results.keys()):
ml_results[key] = min(max(ml_infer_results[idx], 0.0), 1.0)
sent_result['multilabel_regression'] = ml_results
sent_res_arr.append(sent_result)
result['results'] = sent_res_arr
return result
def discriminatory_inference(self, text: str):
"""Performs inference on the input text to determine the binary classification.
Parameters
----------
text : str
The input text to be classified.
Returns
-------
tuple[str, Number]
A tuple consisting of the string classification (Discriminatory or Non-Discriminatory) and the numeric prediction class (1 or 0).
"""
bin_inputs = self._encode_binary(text)
with torch.no_grad():
bin_logits = self.bin_model(**bin_inputs).logits
probs = torch.nn.functional.softmax(bin_logits, dim=-1)
pred_class = torch.argmax(probs).item()
bin_label_map = {0: "Non-Discriminatory", 1: "Discriminatory"}
bin_text_pred = bin_label_map[pred_class]
return bin_text_pred, pred_class
def category_inference(self, text: str):
"""Performs inference on the input text to determine the regression values for the categories of discrimination.
Parameters
----------
text : str
The input text to be classified.
Returns
-------
list[float]
A tuple consisting of the string classification (Discriminatory or Non-Discriminatory) and the numeric prediction class (1 or 0).
"""
ml_inputs = self._encode_multilabel(text)
with torch.no_grad():
ml_outputs = self.ml_regr_model(**ml_inputs).logits
ml_op_list = ml_outputs.squeeze().tolist()
results = []
for item in ml_op_list:
results.append(max(0.0, item))
return results |