TDAMM Multi-Label Classification Model

The TDAMM (Time Domain Multi-Messenger Astronomy) model is created to categorize NASA’s time domain multi-messenger resources into one or more of 36 distinct categories identified by subject matter experts (SMEs)

Model Description

Base Model: astroBERT, fine-tuned for multi-label classification
Task: Multi-label classification
Training Data: A collection of 408 NASA and non-NASA documents related to TDAMM topics identified by SMEs

Data Distribution

Performance Analysis

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("nasa-impact/tdamm-classification")
model = AutoModelForSequenceClassification.from_pretrained("nasa-impact/tdamm-classification")

# Prepare input
text = "Your astronomical test text here"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.sigmoid(outputs.logits)

# Convert to binary predictions (threshold = 0.5)
predictions = (predictions > 0.5).int()

Label Mapping During Inference

After obtaining predictions from the model, we can map the predicted label indices to their actual names using the model.config.id2label dictionary

# Example usage
predicted_indices = [0, 2, 5]
predicted_labels = [model.config.id2label[idx] for idx in predicted_indices]
print(predicted_labels)