Sentence Similarity
Transformers
Safetensors
cybersecurity
cti
yara
contrastive-learning
dual-encoder
Instructions to use shaswatamitra/falcon-yara-dual-all-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shaswatamitra/falcon-yara-dual-all-mpnet-base-v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shaswatamitra/falcon-yara-dual-all-mpnet-base-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "config": { | |
| "run": 4, | |
| "batch": 70, | |
| "epochs": 50, | |
| "lr": 2e-05, | |
| "schedule": "constant", | |
| "temperature": 0.07 | |
| }, | |
| "train_loss": [ | |
| 0.060152317951655106, | |
| 0.01781781367248013, | |
| 0.01585694437048265, | |
| 0.012834095227576437, | |
| 0.011322954886903365, | |
| 0.012782377046754672 | |
| ], | |
| "val_loss": [ | |
| 0.011172595322326137, | |
| 0.01013999234419316, | |
| 0.011375776392621143, | |
| 0.011123350105966287, | |
| 0.011085889988559453, | |
| 0.011549806042845981 | |
| ], | |
| "best_epoch": 2, | |
| "best_val_loss": 0.01013999234419316 | |
| } |