Feature Extraction
Transformers
Safetensors
English
modernbert
cybersecurity
APT
threat-intelligence
contrastive-learning
embeddings
attribution
MITRE-ATTACK
CTI
ModernBERT
Eval Results (legacy)
text-embeddings-inference
Instructions to use selfconstruct3d/FALCON with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selfconstruct3d/FALCON with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="selfconstruct3d/FALCON")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/FALCON") model = AutoModel.from_pretrained("selfconstruct3d/FALCON") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "is_local": true, | |
| "mask_token": "[MASK]", | |
| "max_length": 128, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 8192, | |
| "model_specific_special_tokens": {}, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "[PAD]", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "tokenizer_class": "TokenizersBackend", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |