Spaces:
Sleeping
Sleeping
File size: 3,067 Bytes
2cba4b1 f9714b1 2cba4b1 94de7c5 f9714b1 94de7c5 7538db6 f9714b1 2cba4b1 94de7c5 f9714b1 94de7c5 84af19a 94de7c5 f9714b1 94de7c5 f9714b1 94de7c5 |
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 |
import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np
import requests
import os
class IntentClassifier:
def __init__(self):
self.id2label = {0: 'information_intent',
1: 'yelp_intent',
2: 'navigation_intent',
3: 'travel_intent',
4: 'purchase_intent',
5: 'weather_intent',
6: 'translation_intent',
7: 'unknown'}
self.label2id = {label: id for id, label in self.id2label.items()}
self.tokenizer = AutoTokenizer.from_pretrained("Mozilla/mobilebert-uncased-finetuned-LoRA-intent-classifier")
model_url = "https://huggingface.co/Mozilla/mobilebert-uncased-finetuned-LoRA-intent-classifier/resolve/main/onnx/model_quantized.onnx"
model_dir_path = "models"
model_path = f"{model_dir_path}/mobilebert-uncased-finetuned-LoRA-intent-classifier_model_quantized.onnx"
if not os.path.exists(model_dir_path):
os.makedirs(model_dir_path)
if not os.path.exists(model_path):
print("Downloading ONNX model...")
response = requests.get(model_url)
with open(model_path, "wb") as f:
f.write(response.content)
print("ONNX model downloaded.")
# Load the ONNX model
self.ort_session = ort.InferenceSession(model_path)
def find_intent(self, sequence, verbose=False):
inputs = self.tokenizer(sequence,
return_tensors="np", # ONNX requires inputs in NumPy format
padding="max_length", # Pad to max length
truncation=True, # Truncate if the text is too long
max_length=64)
# Convert inputs to NumPy arrays
onnx_inputs = {k: v for k, v in inputs.items()}
# Run the ONNX model
logits = self.ort_session.run(None, onnx_inputs)[0]
# Get the prediction
prediction = np.argmax(logits, axis=1)[0]
probabilities = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
rounded_probabilities = np.round(probabilities, decimals=3)
pred_result = self.id2label[prediction]
proba_result = dict(zip(self.label2id.keys(), rounded_probabilities[0].tolist()))
if verbose:
print(sequence + " -> " + pred_result)
print(proba_result, "\n")
return pred_result, proba_result
def main():
text_list = [
'floor repair cost',
'pet store near me',
'who is the us president',
'italian food',
'sandwiches for lunch',
"cheese burger cost",
"What is the weather today",
"what is the capital of usa",
"cruise trip to carribean",
]
cls = IntentClassifier()
for sequence in text_list:
cls.find_intent(sequence, verbose=True)
if __name__ == '__main__':
main()
|