Text Classification
Transformers
PyTorch
JAX
roberta
code_x_glue_cc_defect_detection
code
security
vulnerability-detection
codebert
apache-2.0
Instructions to use mangsense/codebert_java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mangsense/codebert_java with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mangsense/codebert_java")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mangsense/codebert_java") model = AutoModelForSequenceClassification.from_pretrained("mangsense/codebert_java") - Notebooks
- Google Colab
- Kaggle
File size: 599 Bytes
411d58a | 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 | {
"_name_or_path": "microsoft/codebert-base",
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"output_past": true,
"pad_token_id": 1,
"type_vocab_size": 1,
"vocab_size": 50265
}
|