File size: 3,469 Bytes
9c6961c | 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import json
import re
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
from datasets import load_dataset
from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template
MODEL_DIR = "/home/mshahidul/readctrl_model/full_model/classifier_model"
TEST_DATA_PATH = "verified_combined_0-80_test.json"
MAX_SEQ_LENGTH = 4096
ACCURACY_OUTPUT_PATH = "accuracy_results.json"
SYSTEM_PROMPT = (
"You are an expert medical editor and Health Literacy specialist. "
"Classify the health literacy level of the provided text."
)
USER_PROMPT = """Classify the health literacy level of the rewritten text.
Labels:
- low_health_literacy: very simple, living-room language, minimal jargon.
- intermediate_health_literacy: standard public-friendly language, limited jargon.
- proficient_health_literacy: technical, clinical, or academic language.
Input:
Full Source Text:
<<<FULLTEXT>>>
Rewritten Text:
<<<DIFF_LABEL_TEXTS>>>
Output: Return only one label string from the list above."""
LABELS = {
"low_health_literacy",
"intermediate_health_literacy",
"proficient_health_literacy",
}
def build_user_prompt(fulltext: str, diff_label_texts: str) -> str:
return USER_PROMPT.replace("<<<FULLTEXT>>>", fulltext).replace(
"<<<DIFF_LABEL_TEXTS>>>", diff_label_texts
)
def extract_label(text: str) -> str:
match = re.search(
r"(low_health_literacy|intermediate_health_literacy|proficient_health_literacy)",
text,
)
return match.group(1) if match else ""
def main():
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=MODEL_DIR,
max_seq_length=MAX_SEQ_LENGTH,
load_in_4bit=False,
load_in_8bit=False,
)
tokenizer = get_chat_template(tokenizer, chat_template="qwen3-instruct")
dataset = load_dataset("json", data_files=TEST_DATA_PATH, split="train")
correct = 0
total = 0
for example in dataset:
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": build_user_prompt(
example["fulltext"], example["diff_label_texts"]
),
},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
outputs = model.generate(
**tokenizer(text, return_tensors="pt").to("cuda"),
max_new_tokens=20,
temperature=0.0,
top_p=1.0,
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
pred = extract_label(decoded)
print(f"Predicted: {pred}, Expected: {example['label']}")
if pred == example["label"]:
correct += 1
total += 1
accuracy = (correct / total) if total else 0.0
results = {
"accuracy": round(accuracy, 6),
"correct": correct,
"total": total,
"model_dir": MODEL_DIR,
"test_data_path": TEST_DATA_PATH,
}
with open(ACCURACY_OUTPUT_PATH, "w", encoding="utf-8") as handle:
json.dump(results, handle, ensure_ascii=True)
handle.write("\n")
print(f"Accuracy: {accuracy:.4f} ({correct}/{total})")
print(f"Saved accuracy info to {ACCURACY_OUTPUT_PATH}")
if __name__ == "__main__":
main()
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