utils/get_intent.py

from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
import torch
from torch.nn import functional as F
import numpy as np
import json



label2id= json.load(
 open('data/categories_refined.json', 'r')
)
id2label= {}
for key in label2id.keys():
 id2label[label2id[key]] = key
 


model_name= "intent_classification_model/checkpoint-1216"
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForSequenceClassification.from_pretrained(model_name).to("cuda")


# probabilities = 1 / (1 + np.exp(-logit_score))
def logit2prob(logit):
 # odds =np.exp(logit)
 # prob = odds / (1 + odds)
 prob= 1/(1+ np.exp(-logit))
 return np.round(prob, 3)



 
def get_top_intent(keyword: str):
 '''
 Returns score list
 '''
 inputs = tokenizer(keyword, return_tensors="pt").to("cuda")
 with torch.no_grad():
 logits = model(**inputs).logits
 
 # print("logits: ", logits)
 # predicted_class_id = logits.argmax().item()
 
 # get probabilities using softmax from logit score and convert it to numpy array
 # probabilities_scores = F.softmax(logits.cpu(), dim = -1).numpy()[0]
 individual_probabilities_scores = logit2prob(logits.cpu().numpy()[0])
 
 score_list= []
 
 for i in range(5):
 label= model.config.id2label[i]
 
 score= individual_probabilities_scores[i]
 score_list.append(
 (label, score)
 )
 # if score>=0.5: 
 # score_list.append(
 # (id2label[i], score)
 # )
 
 
 score_list.sort(
 key= lambda x: x[1], reverse=True
 )
 
 return score_list