Tasfiya025/Multi-Label-Scientific-Abstract-Classifier

SciAbstract-MultiLabel-BERT-Base

📝 Overview

SciAbstract-MultiLabel-BERT-Base is a specialized multi-label text classification model fine-tuned on scientific paper abstracts. It simultaneously classifies an abstract into its Primary Topic and determines the underlying Sentiment/Impact of the research findings (e.g., highly positive breakthrough, negative result/concern).

The model is based on the robust bert-base-uncased architecture and is ideal for automating the categorization and high-level assessment of large volumes of academic literature.

🧠 Model Architecture

The model uses the BertForSequenceClassification head, configured for a multi-label setup.

• Base Model: bert-base-uncased
• Input: Scientific abstract text.
• Output: A 17-dimensional vector of logits, where each dimension corresponds to one of the 17 potential labels (12 Topics + 5 Sentiments). The model uses a sigmoid activation function for the final layer to handle the multi-label nature, allowing it to predict multiple positive labels (e.g., one Topic and one Sentiment) for a single input.
• Loss Function: Binary Cross-Entropy with Logits (BCEWithLogitsLoss).
• Labels:
  • Topics: Materials Science, Neuroscience, Computer Science, Ecology, Astrophysics, Medicine, etc.
  • Sentiments: Highly Positive, Positive, Moderately Negative, Negative, Highly Negative.

🚀 Intended Use

• Automated Document Triage: Rapidly categorize new research papers for subject-matter experts.
• Literature Review: Filter and prioritize papers based on topic and the detected impact (sentiment) of the findings.
• Trend Analysis: Track the volume of positive vs. negative research outcomes within specific scientific fields over time.

⚠️ Limitations

• Multi-Label Complexity: The model may struggle with abstracts that span highly ambiguous or highly interdisciplinary topics not well-represented in the training data.
• Sentiment Scope: The sentiment classification is specifically tailored to the tone of scientific findings (e.g., success of an experiment, critical failure, potential concern) and may not generalize well to general public sentiment.
• Maximum Length: Input text is truncated to 512 tokens (the BERT standard). Extremely long abstracts may lose critical information.

💻 Example Code

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "Your-HF-Username/SciAbstract-MultiLabel-BERT-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Sample abstract
abstract = "Development of a quantum entanglement system achieving coherence for over 10 seconds at room temperature, a significant breakthrough for quantum computing."

# Tokenize input
inputs = tokenizer(abstract, return_tensors="pt", truncation=True, padding=True)

# Make prediction
with torch.no_grad():
    logits = model(**inputs).logits

# Apply sigmoid to get probabilities for each label
probabilities = torch.sigmoid(logits).squeeze()

# Get the label IDs and names
id2label = model.config.id2label
predicted_labels = [id2label[i] for i, prob in enumerate(probabilities) if prob > 0.5] # Threshold at 0.5

print(f"Abstract: {abstract}")
print("-" * 30)
print(f"Predicted Labels: {predicted_labels}")
# Expected Output Example: ['Topic: Physics', 'Sentiment: Highly Positive']