Text Classification
setfit
Safetensors
sentence-transformers
English
bert
code-security
dlp
secret-detection
vulnerability-detection
Eval Results (legacy)
text-embeddings-inference
Instructions to use spidercob/code-risk-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use spidercob/code-risk-classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("spidercob/code-risk-classifier") - sentence-transformers
How to use spidercob/code-risk-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("spidercob/code-risk-classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 611 Bytes
4893b77 | 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 26 | {
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"BertModel"
],
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"classifier_dropout": null,
"dtype": "float32",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.57.6",
"type_vocab_size": 2,
"use_cache": true,
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}
|