---
language: en
tags:
- Text Classification
- TDAMM
- Multi-label Classification
- NASA
- Astrophysics
base_model:
- adsabs/astroBERT
library_name: transformers
license: apache-2.0
---
# 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
```python
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
```python
# Example usage
predicted_indices = [0, 2, 5]
predicted_labels = [model.config.id2label[idx] for idx in predicted_indices]
print(predicted_labels)
```