File size: 5,628 Bytes
e7b7078 | 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 | import re
import os
import json
import torch
import torch.nn as nn
from urllib.parse import urlparse
from transformers import AutoModel, AutoConfig, AutoTokenizer
from transformers.modeling_outputs import SequenceClassifierOutput
PROFILE_SLUGS = re.compile(
r'/(profile|store|shop|freelancers?|biz|therapists?|counsellors?|'
r'restaurants?|menu|cottage|actors?|celebrants?|broker-finder|'
r'users?|usr|sellers?|vendors?|merchants?|dealers?|agents?|'
r'members?|str|book|booking|appointments?)(/|$)', re.IGNORECASE
)
NUM_TABULAR_FEATURES = 6
NUMERIC_ID_IN_PATH = re.compile(r'/\d{3,}(/|$)')
TABULAR_HIDDEN_SIZE = 128
KNOWN_PLATFORMS_PATH = os.path.join(os.path.dirname(__file__), "known_platforms.json")
with open(KNOWN_PLATFORMS_PATH) as _f:
KNOWN_PLATFORMS = set(json.load(_f))
try:
import tldextract
_get_registered_domain = lambda url: tldextract.extract(url).registered_domain.lower()
_tld = lambda url: tldextract.extract(url).suffix.lower()
except ImportError:
_get_registered_domain = lambda url: '.'.join(urlparse(url).netloc.lower().split('.')[-2:])
_tld = lambda url: urlparse(url).netloc.lower().split('.')[-1]
_subdomain_dot_count = lambda url: max(0, urlparse(url).netloc.count('.') - 1)
_path_depth = lambda url: len([s for s in urlparse(url).path.split('/') if s])
extract_tabular_features = lambda url: [
1.0 if PROFILE_SLUGS.search(urlparse(url).path.lower()) else 0.0,
1.0 if _get_registered_domain(url) in KNOWN_PLATFORMS else 0.0,
min(_path_depth(url) / 10.0, 1.0),
min(_subdomain_dot_count(url) / 3.0, 1.0),
1.0 if NUMERIC_ID_IN_PATH.search(urlparse(url).path) else 0.0,
1.0 if _tld(url) == 'jp' else 0.0,
]
class UrlBertWithTabular(nn.Module):
def __init__(self, bert_model_name, num_labels, num_tabular_features=NUM_TABULAR_FEATURES):
super().__init__()
self.bert = AutoModel.from_pretrained(bert_model_name)
self.hidden_size = self.bert.config.hidden_size
self.num_labels = num_labels
self.num_tabular_features = num_tabular_features
self.tabular_proj = nn.Sequential(
nn.Linear(num_tabular_features, TABULAR_HIDDEN_SIZE),
nn.ReLU(),
nn.Dropout(0.1),
)
self.classifier = nn.Linear(self.hidden_size + TABULAR_HIDDEN_SIZE, num_labels)
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, tabular_features=None, **kwargs):
bert_output = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
cls_output = bert_output.last_hidden_state[:, 0, :]
tabular_proj = self.tabular_proj(tabular_features.float())
combined = torch.cat([cls_output, tabular_proj], dim=1)
logits = self.classifier(combined)
return SequenceClassifierOutput(logits=logits)
@classmethod
def from_pretrained(cls, save_directory):
with open(os.path.join(save_directory, "tabular_config.json")) as f:
tabular_config = json.load(f)
bert_config = AutoConfig.from_pretrained(save_directory)
model = cls.__new__(cls)
nn.Module.__init__(model)
model.bert = AutoModel.from_config(bert_config)
model.hidden_size = bert_config.hidden_size
model.num_labels = tabular_config["num_labels"]
model.num_tabular_features = tabular_config["num_tabular_features"]
model.tabular_proj = nn.Sequential(
nn.Linear(model.num_tabular_features, TABULAR_HIDDEN_SIZE),
nn.ReLU(),
nn.Dropout(0.1),
)
model.classifier = nn.Linear(model.hidden_size + TABULAR_HIDDEN_SIZE, model.num_labels)
safetensors_path = os.path.join(save_directory, "model.safetensors")
bin_path = os.path.join(save_directory, "pytorch_model.bin")
if os.path.exists(safetensors_path):
from safetensors.torch import load_file
state_dict = load_file(safetensors_path)
else:
state_dict = torch.load(bin_path, map_location="cpu", weights_only=True)
model.load_state_dict(state_dict)
return model
LABEL_MAP = {0: "official_website", 1: "platform"}
class EndpointHandler:
def __init__(self, path=""):
self.model = UrlBertWithTabular.from_pretrained(path)
self.model.eval()
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def __call__(self, data):
inputs = data.get("inputs", data)
if isinstance(inputs, str):
inputs = [inputs]
encodings = self.tokenizer(
inputs, padding=True, truncation=True, max_length=128, return_tensors="pt"
).to(self.device)
tabular = torch.tensor(
[extract_tabular_features(url) for url in inputs], dtype=torch.float32
).to(self.device)
with torch.no_grad():
outputs = self.model(
input_ids=encodings["input_ids"],
attention_mask=encodings["attention_mask"],
tabular_features=tabular,
)
probs = torch.softmax(outputs.logits, dim=-1)
results = []
for i in range(len(inputs)):
scores = probs[i].tolist()
predictions = [
{"label": LABEL_MAP.get(j, f"LABEL_{j}"), "score": scores[j]}
for j in range(len(scores))
]
predictions.sort(key=lambda x: x["score"], reverse=True)
results.append(predictions)
return results
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