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c26f561 | 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 | import numpy as np, re, joblib, torch
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import backend as K
from transformers import BertTokenizer, BertModel
from sentence_transformers import SentenceTransformer
class FeatureAttention(keras.layers.Layer):
def __init__(self, **kw): super().__init__(**kw)
def build(self, s): self.W = self.add_weight(shape=(s[-1],), initializer="ones", trainable=True)
def call(self, x): return x * tf.nn.softmax(self.W)
@keras.saving.register_keras_serializable()
def focal_loss(g=2.0, a=0.5):
def fn(y, p):
y = tf.squeeze(tf.cast(y, tf.int32))
p = K.clip(p, K.epsilon(), 1-K.epsilon())
ce = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=tf.math.log(p+K.epsilon()))
idx = tf.stack([tf.range(tf.shape(p)[0], dtype=tf.int32), y], axis=-1)
return K.mean(a * K.pow(1-tf.gather_nd(p,idx), g) * ce)
return fn
class PathOraPredictor:
def __init__(self, model_path="pathora_model.keras", le_path="label_encoder.joblib"):
self.model = keras.models.load_model(model_path, custom_objects={"FeatureAttention":FeatureAttention,"loss_fn":focal_loss()})
self.le = joblib.load(le_path)
self.device = torch.device("cpu")
self.tokenizer = BertTokenizer.from_pretrained("bert-resume-classifier-final")
self.bert = BertModel.from_pretrained("bert-resume-classifier-final").to(self.device)
self.bert.eval()
self.st = SentenceTransformer("all-MiniLM-L6-v2")
def predict(self, text, top_n=5):
text = re.sub(r"<[^>]+>", " ", re.sub(r"\s+", " ", text)).strip()
enc = self.tokenizer(text, padding=True, truncation=True, max_length=128, return_tensors="pt")
with torch.no_grad():
out = self.bert(**enc)
bert_emb = out.last_hidden_state[:, 0, :].numpy()
st_emb = self.st.encode([text], convert_to_numpy=True)
emb = np.concatenate([bert_emb, st_emb], axis=1)
probs = self.model.predict(emb, verbose=0)[0]
top = np.argsort(probs)[::-1][:top_n]
return [(self.le.classes_[i], float(probs[i])) for i in top]
if __name__ == "__main__":
p = PathOraPredictor()
for s in ["Python developer, TensorFlow, AWS.", "Financial analyst, CFA, Excel.", "Nurse, ICU, BLS."]:
print("Text:", s)
for cat, conf in p.predict(s):
print(" {}: {:.1%}".format(cat, conf))
print() |