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d3d4834 92e7042 d3d4834 86838b3 d3d4834 77c06b2 d3d4834 3f33192 | 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 | import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from pathlib import Path
import nltk
from tqdm import tqdm
import sys, os.path
nltk.download('punkt')
BASE_DIR = Path(__file__).resolve().parent.parent.parent
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')))
from dependencies.Glimpse_tokenizer import glimpse_tokenizer
# === CONFIGURATION ===
MODEL_DIR = BASE_DIR / "scibert" / "scibert_topic" / "final_model"
DATA_DIR = BASE_DIR / "glimpse" / "data" / "processed"
OUTPUT_DIR = BASE_DIR / "data" / "topic_scored"
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
# === Load model and tokenizer ===
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_DIR)
model.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# === Tokenize like GLIMPSE ===
# def tokenize_sentences(text: str) -> list:
# # same tokenization as in the original glimpse code
# text = text.replace('-----', '\n')
# sentences = nltk.sent_tokenize(text)
# sentences = [sentence for sentence in sentences if sentence != ""]
# return sentences
# === Label map (optional: for human-readable output) ===
id2label = {
# 0: "Evaluative",
# 1: "Structuring",
# 2: "Request",
# 3: "Fact",
# 4: "Social",
# 5: "Other",
0: "Substance",
1: "Clarity",
2: "Soundness/Correctness",
3: "Originality",
4: "Motivation/Impact",
5: "Meaningful Comparison",
6: "Replicability",
7: "NONE" # This is used for sentences that do not match any specific topic
}
def predict_topic(sentences):
inputs = tokenizer(sentences, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=1).cpu().tolist()
# Convert predictions to human-readable labels
predictions = [id2label[pred] for pred in predictions]
return predictions
def find_topic(start_year=2017, end_year=2021):
for year in range(start_year, end_year + 1):
print(f"Processing {year}...")
input_path = DATA_DIR / f"all_reviews_{year}.csv"
output_path = OUTPUT_DIR / f"topic_scored_reviews_{year}.csv"
df = pd.read_csv(input_path)
all_rows = []
for _, row in tqdm(df.iterrows(), total=len(df)):
review_id = row["id"]
text = row["text"]
sentences = glimpse_tokenizer(text)
if not sentences:
continue
labels = predict_topic(sentences)
for sentence, topic in zip(sentences, labels):
all_rows.append({"id": review_id, "sentence": sentence, "topic": topic})
output_df = pd.DataFrame(all_rows)
output_df.to_csv(output_path, index=False)
print(f"Saved topic-scored data to {output_path}")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Run topic scoring with SciBERT")
parser.add_argument("--start-year", type=int, default=None, help="Start year (default: auto-detect)")
parser.add_argument("--end-year", type=int, default=None, help="End year (default: auto-detect)")
args = parser.parse_args()
if args.start_year is not None and args.end_year is not None:
find_topic(start_year=args.start_year, end_year=args.end_year)
else:
import re
available = sorted(
int(re.search(r'all_reviews_(\d{4})\.csv', f.name).group(1))
for f in DATA_DIR.glob("all_reviews_*.csv")
if re.search(r'all_reviews_(\d{4})\.csv', f.name)
)
if available:
find_topic(start_year=min(available), end_year=max(available))
else:
print("No data files found in", DATA_DIR) |