README.md

---
language: en
tags:
- Text Classification
- TDAMM
- Multi-label Classification
- NASA
- Astrophysics
- Science Document Entity
base_model:
- nasa-impact/indus-sde-v0.2
library_name: transformers
license: apache-2.0
pipeline_tag: text-classification
---

# TDAMM Multi-Label Classification Model v2

The TDAMM (Time Domain Multi-Messenger Astronomy) model v2 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).

This is an updated version fine-tuned from [INDUS-SDE](https://huggingface.co/nasa-impact/indus-sde-v0.2), a domain-adapted language model for Scientific Content Curation & Discovery in noisy context.

## Model Description

- **Base Model:** [nasa-impact/indus-sde-v0.2](https://huggingface.co/nasa-impact/indus-sde-v0.2), fine-tuned for multi-label classification
- **Architecture:** RobertaForSequenceClassification
- **Task:** Multi-label classification (36 categories)
- **Training Data:** NASA and non-NASA documents related to TDAMM topics identified by SMEs (same data split as [v1](https://huggingface.co/nasa-impact/tdamm-classification))

## Changes from v1

- **New Base Model:** Fine-tuned from INDUS-SDE v0.2 (previously [astroBERT](https://huggingface.co/adsabs/astroBERT) in [v1](https://huggingface.co/nasa-impact/tdamm-classification))
- Leverages domain-adapted embeddings from INDUS-SDE for improved understanding of scientific document entities

## Performance Metrics

| Metric | Value |
|--------|-------|
| Eval Accuracy | 0.657 |
| Weighted Precision (threshold=0.5) | 0.854 |

### Model Comparison

| Model | Weighted Precision |
|-------|-------------------|
| ModernBERT-SDE | 45.2 |
| ModernBERT | 72.5 |
| INDUS | 73.4 |
| AstroBERT | 85.5 |
| **INDUS-SDE** | **85.3** |

*TDAMM classification performance (Weighted Precision). All models fine-tuned with focal loss. INDUS-SDE matches domain-specific AstroBERT despite no astrophysics-specific pretraining.*

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("nasa-impact/tdamm-classification-v2")
model = AutoModelForSequenceClassification.from_pretrained("nasa-impact/tdamm-classification-v2")

# Prepare input
text = "Your astronomical 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)
binary_predictions = (predictions > 0.5).int()

# Get predicted label indices
predicted_indices = torch.where(binary_predictions[0] == 1)[0].tolist()
print(f"Predicted indices: {predicted_indices}")
```

## Label Mapping During Inference

After obtaining predictions from the model, you 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)
```

## Related Models

- [TDAMM Classification v1](https://huggingface.co/nasa-impact/tdamm-classification) - Previous version based on astroBERT
- [INDUS-SDE v0.2](https://huggingface.co/nasa-impact/indus-sde-v0.2) - Base model for this fine-tuned version

## Citation

If you use this model, please cite:

```bibtex
@misc{tdamm-classification-v2,
  author = {NASA IMPACT},
  title = {TDAMM Multi-Label Classification Model v2},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/nasa-impact/tdamm-classification-v2}
}
```

## License

Apache 2.0