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---
language: en
tags:
- astronomy
- TDAMM
- classification
- multi-label
- NASA
- astrophysics
---
# TDAMM Multi-Label Classification Model
This model performs multi-label classification for Time Domain and Multi-Messenger Astronomy (TDAMM) topics.
## Model Description
Base Model: astroBERT
Task: Multi-label classification
Training Data: NASA and non-NASA documents related to TDAMM topics
## 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()
```
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