Text Classification
Transformers
Safetensors
English
deberta-v2
prompt-injection
security
span-detection
guardrails
ai-safety
agents
llm-security
text-embeddings-inference
Instructions to use Unplug-AI/unplug-tiny-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Unplug-AI/unplug-tiny-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Unplug-AI/unplug-tiny-v1")# Load model directly from transformers import AutoTokenizer, DebertaV2ForDualHead tokenizer = AutoTokenizer.from_pretrained("Unplug-AI/unplug-tiny-v1") model = DebertaV2ForDualHead.from_pretrained("Unplug-AI/unplug-tiny-v1") - Notebooks
- Google Colab
- Kaggle
File size: 1,140 Bytes
fdb710c | 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"architectures": [
"DebertaV2ForDualHead"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 1,
"doc_loss_weight": 1.0,
"doc_positive_index": 1,
"dtype": "float32",
"dual_head": true,
"dual_head_backbone": "deberta-v2",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 384,
"id2label": {
"0": "O",
"1": "B-INJ",
"2": "I-INJ"
},
"initializer_range": 0.02,
"intermediate_size": 1536,
"label2id": {
"B-INJ": 1,
"I-INJ": 2,
"O": 0
},
"layer_norm_eps": 1e-07,
"legacy": true,
"max_position_embeddings": 512,
"max_relative_positions": -1,
"model_type": "deberta-v2",
"norm_rel_ebd": "layer_norm",
"num_attention_heads": 6,
"num_doc_labels": 2,
"num_hidden_layers": 12,
"pad_token_id": 0,
"pooler_dropout": 0,
"pooler_hidden_act": "gelu",
"pooler_hidden_size": 384,
"pos_att_type": [
"p2c",
"c2p"
],
"position_biased_input": false,
"position_buckets": 256,
"relative_attention": true,
"share_att_key": true,
"transformers_version": "4.57.6",
"type_vocab_size": 0,
"vocab_size": 128100
}
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