Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use KingTechnician/deberta-v3-base_LOGIC_Native with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingTechnician/deberta-v3-base_LOGIC_Native with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KingTechnician/deberta-v3-base_LOGIC_Native")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KingTechnician/deberta-v3-base_LOGIC_Native") model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/deberta-v3-base_LOGIC_Native") - Notebooks
- Google Colab
- Kaggle
File size: 1,706 Bytes
483de36 | 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 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | {
"architectures": [
"DebertaV2ForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 1,
"dtype": "float16",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "ad hominem",
"1": "ad populum",
"2": "appeal to emotion",
"3": "circular reasoning",
"4": "equivocation",
"5": "fallacy of credibility",
"6": "fallacy of extension",
"7": "fallacy of logic",
"8": "fallacy of relevance",
"9": "false causality",
"10": "false dilemma",
"11": "faulty generalization",
"12": "intentional"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"ad hominem": 0,
"ad populum": 1,
"appeal to emotion": 2,
"circular reasoning": 3,
"equivocation": 4,
"fallacy of credibility": 5,
"fallacy of extension": 6,
"fallacy of logic": 7,
"fallacy of relevance": 8,
"false causality": 9,
"false dilemma": 10,
"faulty generalization": 11,
"intentional": 12
},
"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": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"pooler_dropout": 0,
"pooler_hidden_act": "gelu",
"pooler_hidden_size": 768,
"pos_att_type": [
"p2c",
"c2p"
],
"position_biased_input": false,
"position_buckets": 256,
"relative_attention": true,
"share_att_key": true,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"type_vocab_size": 0,
"use_cache": false,
"vocab_size": 128100
}
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