add model
Browse files- config.json +49 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- vocab.txt +0 -0
config.json
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{
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"_name_or_path": "cross-encoder/ms-marco-MiniLM-L-6-v2",
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"architectures": [
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"ListformerModelForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"attention_window_size": 4,
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"base_max_position_embeddings": 512,
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"cls_doc_attention": true,
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"cls_other_cls_attention": false,
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"cls_query_attention": true,
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"cls_token_id": 101,
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"depth": 100,
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"doc_cls_attention": true,
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"doc_other_cls_attention": false,
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"doc_query_attention": true,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"input_type_embedding": false,
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"intermediate_size": 1536,
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"label2id": {
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"LABEL_0": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"normalize_output": false,
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"query_cls_attention": false,
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"query_doc_attention": false,
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"rank_position_embedding": false,
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"save_step": 100000,
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"sbert_ce_default_activation_function": "torch.nn.modules.linear.Identity",
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"token_type_embedding": true,
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"torch_dtype": "float32",
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"transformers_version": "4.28.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:58d33775f80a8e1da0950618fb7929eb4c928186223296c74e3e3fa457a83826
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size 90894261
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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