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import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np
import requests
import os
class LocationFinder:
def __init__(self):
self.tokenizer = AutoTokenizer.from_pretrained("Mozilla/distilbert-uncased-NER-LoRA")
model_url = "https://huggingface.co/Mozilla/distilbert-uncased-NER-LoRA/resolve/main/onnx/model_quantized.onnx"
model_dir_path = "models"
model_path = f"{model_dir_path}/distilbert-uncased-NER-LoRA"
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_location(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)
input_feed = {
'input_ids': inputs['input_ids'].astype(np.int64),
'attention_mask': inputs['attention_mask'].astype(np.int64),
}
# Run inference with the ONNX model
outputs = self.ort_session.run(None, input_feed)
logits = outputs[0] # Assuming the model output is logits
probabilities = np.exp(logits) / np.sum(np.exp(logits), axis=-1, keepdims=True)
predicted_ids = np.argmax(logits, axis=-1)
predicted_probs = np.max(probabilities, axis=-1)
# Define the threshold for NER probability
threshold = 0.6
# Define the label map for city, state, citystate, etc.
label_map = {
0: "O", # Outside any named entity
1: "B-PER", # Beginning of a person entity
2: "I-PER", # Inside a person entity
3: "B-ORG", # Beginning of an organization entity
4: "I-ORG", # Inside an organization entity
5: "B-CITY", # Beginning of a city entity
6: "I-CITY", # Inside a city entity
7: "B-STATE", # Beginning of a state entity
8: "I-STATE", # Inside a state entity
9: "B-CITYSTATE", # Beginning of a city_state entity
10: "I-CITYSTATE", # Inside a city_state entity
}
tokens = self.tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
# Initialize lists to hold detected entities
city_entities = []
state_entities = []
city_state_entities = []
for token, predicted_id, prob in zip(tokens, predicted_ids[0], predicted_probs[0]):
if prob > threshold:
if token in ["[CLS]", "[SEP]", "[PAD]"]:
continue
if label_map[predicted_id] in ["B-CITY", "I-CITY"]:
# Handle the case of continuation tokens (like "##" in subwords)
if token.startswith("##") and city_entities:
city_entities[-1] += token[2:] # Remove "##" and append to the last token
else:
city_entities.append(token)
elif label_map[predicted_id] in ["B-STATE", "I-STATE"]:
if token.startswith("##") and state_entities:
state_entities[-1] += token[2:]
else:
state_entities.append(token)
elif label_map[predicted_id] in ["B-CITYSTATE", "I-CITYSTATE"]:
if token.startswith("##") and city_state_entities:
city_state_entities[-1] += token[2:]
else:
city_state_entities.append(token)
# Combine city_state entities and split into city and state if necessary
if city_state_entities:
city_state_str = " ".join(city_state_entities)
city_state_split = city_state_str.split(",") # Split on comma to separate city and state
city_res = city_state_split[0].strip() if city_state_split[0] else None
state_res = city_state_split[1].strip() if len(city_state_split) > 1 else None
else:
# If no city_state entities, use detected city and state entities separately
city_res = " ".join(city_entities).strip() if city_entities else None
state_res = " ".join(state_entities).strip() if state_entities else None
# Return the detected city and state as separate components
return {
'city': city_res,
'state': state_res
}
if __name__ == '__main__':
query = "weather in san francisco, ca"
loc_finder = LocationFinder()
entities = loc_finder.find_location(query)
print(f"query = {query} => {entities}")
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