Text Classification
Transformers
Safetensors
English
deberta-v2
digital-humanities
historical-text
ai-text-detection
deberta-v3
chronologic
Eval Results (legacy)
text-embeddings-inference
Instructions to use chronologic/chronologic-authenticity-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chronologic/chronologic-authenticity-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chronologic/chronologic-authenticity-deberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chronologic/chronologic-authenticity-deberta") model = AutoModelForSequenceClassification.from_pretrained("chronologic/chronologic-authenticity-deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 928 Bytes
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"architectures": [
"DebertaV2ForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"dtype": "float32",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "synthetic"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"synthetic": 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": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"pooler_dropout": 0,
"pooler_hidden_act": "gelu",
"pooler_hidden_size": 1024,
"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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