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
File size: 580 Bytes
56d9892 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"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]"
}
|