Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/distilbert-base-uncased-nvidia-aegis-v2-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 771 Bytes
77c8dbf | 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 27 28 29 30 31 32 33 34 35 36 37 | {
"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"bos_token_id": null,
"dim": 768,
"dropout": 0.1,
"dtype": "float32",
"eos_token_id": null,
"hidden_dim": 3072,
"id2label": {
"0": "safe",
"1": "unsafe"
},
"initializer_range": 0.02,
"label2id": {
"safe": 0,
"unsafe": 1
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"tie_word_embeddings": true,
"transformers_version": "5.2.0",
"use_cache": false,
"vocab_size": 30522
}
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