Text Classification
Transformers
Safetensors
English
deberta-v2
deberta-v3
human value detection
schwartz values
moral values
political text
retrieval augmented classification
rag
multi-label classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use VictorYeste/value-context-rag-deberta-v3-base-doc-rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VictorYeste/value-context-rag-deberta-v3-base-doc-rag with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VictorYeste/value-context-rag-deberta-v3-base-doc-rag")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VictorYeste/value-context-rag-deberta-v3-base-doc-rag") model = AutoModelForSequenceClassification.from_pretrained("VictorYeste/value-context-rag-deberta-v3-base-doc-rag") - Notebooks
- Google Colab
- Kaggle
File size: 2,114 Bytes
4284792 | 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 71 72 73 74 75 76 77 78 79 80 81 82 | {
"architectures": [
"DebertaV2ForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": null,
"dtype": "float32",
"eos_token_id": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "Self-direction: thought",
"1": "Self-direction: action",
"2": "Stimulation",
"3": "Hedonism",
"4": "Achievement",
"5": "Power: dominance",
"6": "Power: resources",
"7": "Face",
"8": "Security: personal",
"9": "Security: societal",
"10": "Tradition",
"11": "Conformity: rules",
"12": "Conformity: interpersonal",
"13": "Humility",
"14": "Benevolence: caring",
"15": "Benevolence: dependability",
"16": "Universalism: concern",
"17": "Universalism: nature",
"18": "Universalism: tolerance"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"Achievement": 4,
"Benevolence: caring": 14,
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"Conformity: interpersonal": 12,
"Conformity: rules": 11,
"Face": 7,
"Hedonism": 3,
"Humility": 13,
"Power: dominance": 5,
"Power: resources": 6,
"Security: personal": 8,
"Security: societal": 9,
"Self-direction: action": 1,
"Self-direction: thought": 0,
"Stimulation": 2,
"Tradition": 10,
"Universalism: concern": 16,
"Universalism: nature": 17,
"Universalism: tolerance": 18
},
"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,
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"pooler_hidden_act": "gelu",
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"pos_att_type": [
"p2c",
"c2p"
],
"position_biased_input": false,
"position_buckets": 256,
"problem_type": "multi_label_classification",
"relative_attention": true,
"share_att_key": true,
"tie_word_embeddings": true,
"transformers_version": "5.2.0",
"type_vocab_size": 0,
"vocab_size": 128100
}
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