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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 glimpse.glimpse.data_loading.Glimpse_tokenizer import glimpse_tokenizer
# === CONFIGURATION ===
MODEL_DIR = BASE_DIR / "alternative_topic" / "scideberta" / "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__":
find_topic() |