| 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() | |
| ``` | |