output / config.json
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{
"_name_or_path": "avsolatorio/GIST-large-Embedding-v0",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "CE",
"1": "ENV",
"2": "BME",
"3": "PE",
"4": "METAL",
"5": "ME",
"6": "EE",
"7": "CPE",
"8": "OPTIC",
"9": "NANO",
"10": "CHE",
"11": "MATENG",
"12": "AGRI",
"13": "EDU",
"14": "IE",
"15": "SAFETY",
"16": "MATH",
"17": "MATSCI"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"AGRI": 12,
"BME": 2,
"CE": 0,
"CHE": 10,
"CPE": 7,
"EDU": 13,
"EE": 6,
"ENV": 1,
"IE": 14,
"MATENG": 11,
"MATH": 16,
"MATSCI": 17,
"ME": 5,
"METAL": 4,
"NANO": 9,
"OPTIC": 8,
"PE": 3,
"SAFETY": 15
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "multi_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.38.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}