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WNUT 17 training
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{
"_name_or_path": "/home/jupyter/bertweet-base",
"architectures": [
"RobertaForTokenClassification"
],
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"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "B-corporation",
"2": "I-corporation",
"3": "B-creative-work",
"4": "I-creative-work",
"5": "B-group",
"6": "I-group",
"7": "B-location",
"8": "I-location",
"9": "B-person",
"10": "I-person",
"11": "B-product",
"12": "I-product"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
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"LABEL_1": 1,
"LABEL_10": 10,
"LABEL_11": 11,
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"LABEL_6": 6,
"LABEL_7": 7,
"LABEL_8": 8,
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},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 130,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"tokenizer_class": "BertweetTokenizer",
"torch_dtype": "float32",
"transformers_version": "4.17.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 64001
}