README.md

---
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

<img src="https://cdn-uploads.huggingface.co/production/uploads/67804a0abd67e99d000342e1/oOZ3PhRsh6TDEfaSTTpxa.png" width="70%" alt="Distribution 1">
<img src="https://cdn-uploads.huggingface.co/production/uploads/67804a0abd67e99d000342e1/kKpL5XWCtgWiXHLAAmGz5.png" width="70%" alt="Distribution 2">
<img src="https://cdn-uploads.huggingface.co/production/uploads/67804a0abd67e99d000342e1/hJQt5iBKYsVPSHQLIH2RG.png" width="50%" alt="Distribution 3">

## Performance Analysis

<img src="https://cdn-uploads.huggingface.co/production/uploads/67804a0abd67e99d000342e1/aX8X-b7dehTwaA-opBulN.png" width="70%" alt="Threshold 1">

## 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)
```