zzzlift
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Browse files- README.md +25 -0
- added_tokens.json +13 -0
- config.json +34 -0
- generation_config.json +7 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +13 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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language: zh
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---
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## roberta-chinese-homie-large
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This is a Chinese pre-training model RoBERTa, pre-trained on a large-scale corpus. It is suitable for fine-tuning on specific downstream tasks, or as a parameter initialization for pre-training, which can improve performance. Due to excessive alchemy, it is not suitable for Fill Mask directly, unless you have performed a small amount of pre-training.
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I don't know what homie means, but someone calls me that. I believe this is an interesting natural language processing. I don't know what homie means, but I've been called that and I can feel the meaning.
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## Bibtex entry and citation info
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Please cite if you find it helpful.
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```
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@article{zhu2023metaaid,
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title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
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author={Zhu, Hongyin},
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journal={arXiv preprint arXiv:2302.13173},
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year={2023}
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}
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```
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---
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license: other
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---
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added_tokens.json
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{
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"[/heading]": 21131,
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"[/triple]": 21130,
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"[heading]": 21129,
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"[t1]": 21133,
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"[t2]": 21134,
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"[t3]": 21135,
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"[t4]": 21136,
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"[t5]": 21137,
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"[t6]": 21138,
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"[t]": 21132,
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"[triple]": 21128
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}
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config.json
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{
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"_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"classifier_dropout": null,
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"directionality": "bidi",
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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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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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"output_past": true,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.27.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 21139
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.27.1"
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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 sha256:19e9013ceb964b149d4c2f715f9a547a450364953b91b097c9ec4233aed168c2
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size 1302364075
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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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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": "/root/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext-large/snapshots/a25cc9e05974bd9687e528edd516f2cfdb3f5db9/special_tokens_map.json",
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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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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:12f9d1ffc9458f0aa5eae6026c8e03a5a13929481d0780342fd266988b9a5dba
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size 1007
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vocab.txt
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