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"""
Script file used for performing inference with an existing model.
"""
import torch
import json
import huggingface_hub
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification
)
BIN_REPO = 'dlsmallw/NLPinitiative-Binary-Classification'
ML_REPO = 'dlsmallw/NLPinitiative-Multilabel-Regression'
## Class used to encapsulate and handle the logic for inference
class InferenceHandler:
def __init__(self, api_token):
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)
def get_config(self, repo_id):
config = None
if repo_id and self.api_token:
config = huggingface_hub.hf_hub_download(repo_id, filename='config.json', token=self.api_token)
return config
## Initializes a model and tokenizer for use in inference using the models path
def init_model_and_tokenizer(self, repo_id):
config = self.get_config(repo_id)
with open(config) as config_file:
config_json = json.load(config_file)
model_name = config_json['_name_or_path']
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(repo_id, token=self.api_token)
model.eval()
return tokenizer, model
## Handles logic used to encode the text for use in binary classification
def encode_binary(self, text):
bin_tokenized_input = self.bin_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
return bin_tokenized_input
## Handles logic used to encode the text for use in multilabel regression
def encode_multilabel(self, text):
ml_tokenized_input = self.ml_regr_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
return ml_tokenized_input
## Handles text encoding for both binary classification and multilabel regression
def encode_input(self, text):
bin_inputs = self.encode_binary(text)
ml_inputs = self.encode_multilabel(text)
return bin_inputs, ml_inputs
## Handles performing the full sentiment analysis (binary classification and multilabel regression)
def classify_text(self, text):
res_obj = {
'raw_text': text,
'text_sentiment': None,
'numerical_sentiment': None,
'category_sentiments': {
'Gender': None,
'Race': None,
'Sexuality': None,
'Disability': None,
'Religion': None,
'Unspecified': None
}
}
text_prediction, pred_class = self.discriminatory_inference(text)
res_obj['text_sentiment'] = text_prediction
res_obj['numerical_sentiment'] = pred_class
if pred_class == 1:
ml_infer_results = self.category_inference(text)
for idx, key in enumerate(res_obj['category_sentiments'].keys()):
res_obj['category_sentiments'][key] = ml_infer_results[idx]
return res_obj
## Handles logic for checking the binary classfication of the text
def discriminatory_inference(self, text):
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
## Handles logic for assessing the categories of discrimination
def category_inference(self, text):
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 |