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README.eval_brief.md

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+# relm_v1
+## 概述(relm_v1)
+ - macro-correct, 中文拼写纠错CSC测评(文本纠错), 权重使用
+ - 项目地址在[https://github.com/yongzhuo/macro-correct](https://github.com/yongzhuo/macro-correct)
+ - 本模型权重为relm_v1, 使用relm架构, 其特点是将生成任务转化为句对任务(MLM); sent1 + [mask]*n  -->>  sent1 + sent1_correct
+ - 如何使用: 1.使用transformers调用; 2.使用[macro-correct](https://github.com/yongzhuo/macro-correct)项目调用; 详情见***三、调用(Usage)***;
+
+## 目录
+* [一、测评(Test)](#一、测评(Test))
+* [二、结论(Conclusion)](#二、结论(Conclusion))
+* [三、调用(Usage)](#三、调用(Usage))
+* [四、论文(Paper)](#四、论文(Paper))
+* [五、参考(Refer)](#五、参考(Refer))
+* [六、引用(Cite)](#六、引用(Cite))
+
+
+## 一、测评(Test)
+### 1.1 测评数据来源
+地址为[Macropodus/csc_eval_public](https://huggingface.co/datasets/Macropodus/csc_eval_public), 所有训练数据均来自公网或开源数据, 训练数据为1千万左右, 混淆词典较大;
+``` 
+1.gen_de3.json(5545): '的地得'纠错, 由人民日报/学习强国/chinese-poetry等高质量数据人工生成;
+2.lemon_v2.tet.json(1053): relm论文提出的数据, 多领域拼写纠错数据集(7个领域), ; 包括game(GAM), encyclopedia (ENC), contract (COT), medical care(MEC), car (CAR), novel (NOV), and news (NEW)等领域;
+3.acc_rmrb.tet.json(4636): 来自NER-199801(人民日报高质量语料);
+4.acc_xxqg.tet.json(5000): 来自学习强国网站的高质量语料;
+5.gen_passage.tet.json(10000): 源数据为qwen生成的好词好句, 由几乎所有的开源数据汇总的混淆词典生成;
+6.textproof.tet.json(1447): NLP竞赛数据, TextProofreadingCompetition;
+7.gen_xxqg.tet.json(5000): 源数据为学习强国网站的高质量语料, 由几乎所有的开源数据汇总的混淆词典生成;
+8.faspell.dev.json(1000): 视频字幕通过OCR后获取的数据集; 来自爱奇艺的论文faspell;
+9.lomo_tet.json(5000): 主要为音似中文拼写纠错数据集; 来自腾讯; 人工标注的数据集CSCD-NS;
+10.mcsc_tet.5000.json(5000): 医学拼写纠错; 来自腾讯医典APP的真实历史日志; 注意论文说该数据集只关注医学实体的纠错, 常用字等的纠错并不关注;
+11.ecspell.dev.json(1500): 来自ECSpell论文, 包括(law/med/gov)等三个领域;
+12.sighan2013.dev.json(1000): 来自sighan13会议;
+13.sighan2014.dev.json(1062): 来自sighan14会议;
+14.sighan2015.dev.json(1100): 来自sighan15会议;
+```
+### 1.2 测评数据预处理
+```
+测评数据都经过 全角转半角,繁简转化,标点符号标准化等操作;
+```
+
+### 1.3 其他说明
+```
+1.指标带common的极为宽松指标, 同开源项目pycorrector的评估指标;
+2.指标带strict的极为严格指标, 同开源项目[wangwang110/CSC](https://github.com/wangwang110/CSC);
+3.macbert4mdcspell_v1模型为训练使用mdcspell架构+bert的mlm-loss, 但是推理的时候只用bert-mlm;
+4.acc_rmrb/acc_xxqg数据集没有错误, 用于评估模型的误纠率(过度纠错);
+5.qwen25_1-5b_pycorrector的模型为shibing624/chinese-text-correction-1.5b, 其训练数据包括了lemon_v2/mcsc_tet/ecspell的验证集和测试集, 其他的bert类模型的训练不包括验证集和测试集;
+```
+
+
+## 二、重要指标
+### 2.1 F1(common_cor_f1)
+| model/common_cor_f1| avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
+|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector| 45.8| 42.44| 42.89| 31.49| 46.31| 26.06| 32.7| 44.83| 27.93| 55.51| 70.89| 61.72| 66.81 |
+| bert4csc_v1| 62.28| 93.73| 61.99| 44.79| 68.0| 35.03| 48.28| 61.8| 64.41| 79.11| 77.66| 51.01| 61.54 |
+| macbert4csc_v1| 68.55| 96.67| 65.63| 48.4| 75.65| 38.43| 51.76| 70.11| 80.63| 85.55| 81.38| 57.63| 70.7 |
+| macbert4csc_v2| 68.6| 96.74| 66.02| 48.26| 75.78| 38.84| 51.91| 70.17| 80.71| 85.61| 80.97| 58.22| 69.95 |
+| macbert4mdcspell_v1| 71.1| 96.42| 70.06| 52.55| 79.61| 43.37| 53.85| 70.9| 82.38| 87.46| 84.2| 61.08| 71.32 |
+| relm_v1| 54.12| 89.86| 51.79| 38.4| 63.74| 30.6| 31.95| 49.82| 64.7| 73.57| 66.4| 39.87| 48.8 |
+| qwen25_1-5b_pycorrector| 45.11| 27.29| 89.48| 14.61| 83.9| 13.84| 18.2| 36.71| 96.29| 88.2| 36.41| 15.64| 20.73 |
+
+### 2.2 acc(common_cor_acc)
+| model/common_cor_acc| avg| gen_de3| lemon_v2| gen_passage| text_proof| gen_xxqg| faspell| lomo_tet| mcsc_tet| ecspell| sighan2013| sighan2014| sighan2015 |
+|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector| 48.26| 26.96| 28.68| 34.16| 55.29| 28.38| 22.2| 60.96| 57.16| 67.73| 55.9| 68.93| 72.73 |
+| bert4csc_v1| 60.76| 88.21| 45.96| 43.13| 68.97| 35.0| 34.0| 65.86| 73.26| 81.8| 64.5| 61.11| 67.27 |
+| macbert4csc_v1| 65.34| 93.56| 49.76| 44.98| 74.64| 36.1| 37.0| 73.0| 83.6| 86.87| 69.2| 62.62| 72.73 |
+| macbert4csc_v2| 65.22| 93.69| 50.14| 44.92| 74.64| 36.26| 37.0| 72.72| 83.66| 86.93| 68.5| 62.43| 71.73 |
+| macbert4mdcspell_v1| 67.15| 93.09| 54.8| 47.71| 78.09| 39.52| 38.8| 71.92| 84.78| 88.27| 73.2| 63.28| 72.36 |
+| relm_v1| 51.9| 81.71| 36.18| 37.04| 63.99| 29.34| 22.9| 51.98| 74.1| 76.0| 50.3| 45.76| 53.45 |
+| qwen25_1-5b_pycorrector| 46.09| 15.82| 81.29| 22.96| 82.17| 19.04| 12.8| 50.2| 96.4| 89.13| 22.8| 27.87| 32.55 |
+
+### 2.3 acc(acc_true, thr=0.75)
+| model/acc               | avg| acc_rmrb| acc_xxqg |
+|:------------------------|:-----------------|:-----------------|:-----------------|
+| macbert4csc_pycorrector | 99.24| 99.22| 99.26 |
+| bert4csc_v1             | 98.71| 98.36| 99.06 |
+| macbert4csc_v1          | 97.72| 96.72| 98.72 |
+| macbert4csc_v2          | 97.89| 96.98| 98.8 |
+| macbert4mdcspell_v1     | 97.75| 96.51| 98.98 |
+| relm_v1                 | 93.47| 90.21| 96.74 |
+| qwen25_1-5b_pycorrector | 82.0| 77.14| 86.86 |
+
+## 二、结论(Conclusion)
+```
+1.macbert4csc_v1/macbert4csc_v2/macbert4mdcspell_v1等模型使用多种领域数据训练, 比较均衡, 也适合作为第一步的预训练模型, 可用于专有领域数据的继续微调;
+2.比较macbert4csc_pycorrector/bertbase4csc_v1/macbert4csc_v2/macbert4mdcspell_v1, 观察表2.3, 可以发现训练数据越多, 准确率提升的同时, 误纠率也会稍微高一些;
+3.MFT(Mask-Correct)依旧有效, 不过对于数据量足够的情形提升不明显, 可能也是误纠率升高的一个重要原因;
+4.训练数据中也存在文言文数据, 训练好的模型也支持文言文纠错;
+5.训练好的模型对"地得的"等高频错误具有较高的识别率和纠错率;
+```
+
+## 三、调用(Usage)
+### 3.1 使用macro-correct
+```
+# !/usr/bin/python
+# -*- coding: utf-8 -*-
+# @time    : 2021/2/29 21:41
+# @author  : Mo
+# @function: tet csc of relm_v1, 纠错
+
+
+import traceback
+import time
+import os
+
+from macro_correct.pytorch_user_models.csc.relm.predict import RelmPredict
+
+
+path_model_dir = "../../macro_correct/output/text_correction/relm_v1"
+# path_model_dir = "Macropodus/relm_v1"
+
+model = RelmPredict(path_model_dir, path_model_dir)
+texts = ["真麻烦你了。希望你们好好的跳无",
+         "少先队员因该为老人让坐",
+         "机七学习是人工智能领遇最能体现智能的一个分知",
+         "一只小鱼船浮在平净的河面上"
+         ]
+texts = [{"source": t} for t in texts]
+res = model.predict(texts)
+for r in res:
+    print(r)
+"""
+{'source': '真麻烦你了。希望你们好好的跳无', 'target': '真麻烦你了。希望你们好好跳舞', 'errors': [['的', '跳', 12], ['跳', '舞', 13]]}
+{'source': '少先队员因该为老人让坐', 'target': '少先队员应该为老人让座', 'errors': [['因', '应', 4], ['坐', '座', 10]]}
+{'source': '机七学习是人工智能领遇最能体现智能的一个分知', 'target': '机器学习是人工智能领域最能体现智能的一个分', 'errors': [['七', '器', 1], ['遇', '域', 10]]}
+{'source': '一只小鱼船浮在平净的河面上', 'target': '一只小鱼船浮在平静的河面上', 'errors': [['净', '静', 8]]}
+"""
+```
+
+### 3.2 使用 transformers
+```
+# !/usr/bin/python
+# -*- coding: utf-8 -*-
+# @time    : 2021/2/29 21:41
+# @author  : Mo
+# @function: transformers直接加载bert类模型测试
+
+
+import traceback
+import time
+import sys
+import os
+os.environ["USE_TORCH"] = "1"
+from transformers import BertConfig, BertTokenizer, BertForMaskedLM
+import torch
+
+
+pretrained_model_name_or_path = "Macropodus/relm_v1"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+max_len = 128
+
+print("load model, please wait a few minute!")
+tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path)
+bert_config = BertConfig.from_pretrained(pretrained_model_name_or_path)
+model = BertForMaskedLM.from_pretrained(pretrained_model_name_or_path)
+model.to(device)
+print("load model success!")
+
+texts = [
+    "机七学习是人工智能领遇最能体现智能的一个分知",
+    "我是练习时长两念半的鸽仁练习生蔡徐坤",
+    "我是练习时长两年半的鸽人练习生蔡徐坤",
+    "真麻烦你了。希望你们好好的跳无",
+    "他法语说的很好，的语也不错",
+    "遇到一位很棒的奴生跟我疗天",
+    "我们为这个目标努力不解",
+]
+len_mid = min(max_len, max([len(t)+2 for t in texts])) * 2
+
+with torch.no_grad():
+    texts_relm = [list(t) + [tokenizer.sep_token] + [tokenizer.mask_token for _ in t] for t in texts]
+    texts_relm = ["".join(t) for t in texts_relm]
+    outputs = model(**tokenizer(texts_relm, padding=True, max_length=len_mid,
+                                return_tensors="pt").to(device))
+
+def get_errors(source, target):
+    """   极简方法获取 errors   """
+    len_min = min(len(source), len(target))
+    errors = []
+    for idx in range(len_min):
+        if source[idx] != target[idx]:
+            errors.append([source[idx], target[idx], idx])
+    return errors
+
+result = []
+for probs, source in zip(outputs.logits, texts):
+    ids = torch.argmax(probs, dim=-1)
+    tokens_space = tokenizer.decode(ids, skip_special_tokens=False)
+    text_new = tokens_space.split(" ")
+    # print(text_new)
+    target = text_new[len(source)+2:len(source)*2+2]
+    errors = get_errors(list(source), target)
+    target = "".join(target)
+    print(source, " => ", target, errors)
+    result.append([target, errors])
+print(result)
+"""
+机七学习是人工智能领遇最能体现智能的一个分知  =>  机器学习是人工智能领域最能体现智能的一个分知 [['七', '器', 1], ['遇', '域', 10]]
+我是练习时长两念半的鸽仁练习生蔡徐坤  =>  我是练习时长两年半的鸽人练习生蔡徐坤 [['念', '年', 7], ['仁', '人', 11]]
+我是练习时长两年半的鸽人练习生蔡徐坤  =>  我是练习时长两年半的个人练习生蔡徐坤 [['鸽', '个', 10]]
+真麻烦你了。希望你们好好的跳无  =>  真麻烦你了。希望你们好好地跳舞 [['的', '地', 12], ['无', '舞', 14]]
+他法语说的很好，的语也不错  =>  他法语说得很好，德语也不错 [['的', '得', 4], ['的', '德', 8]]
+遇到一位很棒的奴生跟我疗天  =>  遇到一位很棒的女生跟我聊天 [['奴', '女', 7], ['疗', '聊', 11]]
+我们为这个目标努力不解  =>  我们为这个目标努力不懈 [['解', '懈', 10]]
+"""
+```
+
+## 四、论文(Paper)
+ - 2024-Refining: [Refining Corpora from a Model Calibration Perspective for Chinese](https://arxiv.org/abs/2407.15498)
+ - 2024-ReLM: [Chinese Spelling Correction as Rephrasing Language Model](https://arxiv.org/abs/2308.08796)
+ - 2024-DICS: [DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check](https://arxiv.org/abs/2412.12863)
+
+ - 2023-Bi-DCSpell: [A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Check]()
+ - 2023-BERT-MFT: [Rethinking Masked Language Modeling for Chinese Spelling Correction](https://arxiv.org/abs/2305.17721)
+ - 2023-PTCSpell: [PTCSpell: Pre-trained Corrector Based on Character Shape and Pinyin for Chinese Spelling Correction](https://arxiv.org/abs/2212.04068)
+ - 2023-DR-CSC: [A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese](https://aclanthology.org/2023.findings-emnlp.771)
+ - 2023-DROM: [Disentangled Phonetic Representation for Chinese Spelling Correction](https://arxiv.org/abs/2305.14783)
+ - 2023-EGCM: [An Error-Guided Correction Model for Chinese Spelling Error Correction](https://arxiv.org/abs/2301.06323)
+ - 2023-IGPI: [Investigating Glyph-Phonetic Information for Chinese Spell Checking: What Works and What’s Next?](https://arxiv.org/abs/2212.04068)
+ - 2023-CL: [Contextual Similarity is More Valuable than Character Similarity-An Empirical Study for Chinese Spell Checking]()
+
+ - 2022-CRASpell: [CRASpell: A Contextual Typo Robust Approach to Improve Chinese Spelling Correction](https://aclanthology.org/2022.findings-acl.237)
+ - 2022-MDCSpell: [MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction](https://aclanthology.org/2022.findings-acl.98)
+ - 2022-SCOPE: [Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity](https://arxiv.org/abs/2210.10996)
+ - 2022-ECOPO: [The Past Mistake is the Future Wisdom: Error-driven Contrastive Probability Optimization for Chinese Spell Checking](https://arxiv.org/abs/2203.00991)
+
+ - 2021-MLMPhonetics: [Correcting Chinese Spelling Errors with Phonetic Pre-training](https://aclanthology.org/2021.findings-acl.198)
+ - 2021-ChineseBERT: [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://aclanthology.org/2021.acl-long.161/)
+ - 2021-BERTCrsGad: [Global Attention Decoder for Chinese Spelling Error Correction](https://aclanthology.org/2021.findings-acl.122)
+ - 2021-ThinkTwice: [Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction](https://www.mdpi.com/2076-3417/11/13/5832)
+ - 2021-PHMOSpell: [PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Chec](https://aclanthology.org/2021.acl-long.464)
+ - 2021-SpellBERT: [SpellBERT: A Lightweight Pretrained Model for Chinese Spelling Check](https://aclanthology.org/2021.emnlp-main.287)
+ - 2021-TwoWays: [Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models](https://aclanthology.org/2021.acl-short.56)
+ - 2021-ReaLiSe: [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://arxiv.org/abs/2105.12306)
+ - 2021-DCSpell: [DCSpell: A Detector-Corrector Framework for Chinese Spelling Error Correction](https://dl.acm.org/doi/10.1145/3404835.3463050)
+ - 2021-PLOME: [PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction](https://aclanthology.org/2021.acl-long.233)
+ - 2021-DCN: [Dynamic Connected Networks for Chinese Spelling Check](https://aclanthology.org/2021.findings-acl.216/)
+
+ - 2020-SoftMaskBERT: [Spelling Error Correction with Soft-Masked BERT](https://arxiv.org/abs/2005.07421)
+ - 2020-SpellGCN: [SpellGCN：Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check](https://arxiv.org/abs/2004.14166)
+ - 2020-ChunkCSC: [Chunk-based Chinese Spelling Check with Global Optimization](https://aclanthology.org/2020.findings-emnlp.184)
+ - 2020-MacBERT: [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
+
+ - 2019-FASPell: [FASPell: A Fast, Adaptable, Simple, Powerful Chinese Spell Checker Based On DAE-Decoder Paradigm](https://aclanthology.org/D19-5522)
+ - 2018-Hybrid: [A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Checking](https://aclanthology.org/D18-1273)
+
+ - 2015-Sighan15: [Introduction to SIGHAN 2015 Bake-off for Chinese Spelling Check](https://aclanthology.org/W15-3106/)
+ - 2014-Sighan14: [Overview of SIGHAN 2014 Bake-off for Chinese Spelling Check](https://aclanthology.org/W14-6820/)
+ - 2013-Sighan13: [Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013](https://aclanthology.org/W13-4406/)
+
+## 五、参考(Refer)
+ - [nghuyong/Chinese-text-correction-papers](https://github.com/nghuyong/Chinese-text-correction-papers)
+ - [destwang/CTCResources](https://github.com/destwang/CTCResources)
+ - [wangwang110/CSC](https://github.com/wangwang110/CSC)
+ - [chinese-poetry/chinese-poetry](https://github.com/chinese-poetry/chinese-poetry)
+ - [chinese-poetry/huajianji](https://github.com/chinese-poetry/huajianji)
+ - [garychowcmu/daizhigev20](https://github.com/garychowcmu/daizhigev20)
+ - [yangjianxin1/Firefly](https://github.com/yangjianxin1/Firefly)
+ - [Macropodus/xuexiqiangguo_428w](https://huggingface.co/datasets/Macropodus/xuexiqiangguo_428w)
+ - [Macropodus/csc_clean_wang271k](https://huggingface.co/datasets/Macropodus/csc_clean_wang271k)
+ - [Macropodus/csc_eval_public](https://huggingface.co/datasets//Macropodus/csc_eval_public)
+ - [shibing624/pycorrector](https://github.com/shibing624/pycorrector)
+ - [iioSnail/MDCSpell_pytorch](https://github.com/iioSnail/MDCSpell_pytorch)
+ - [gingasan/lemon](https://github.com/gingasan/lemon)
+ - [Claude-Liu/ReLM](https://github.com/Claude-Liu/ReLM)
+
+
+## 六、引用(Cite)
+For citing this work, you can refer to the present GitHub project. For example, with BibTeX:
+```
+@software{macro-correct,
+    url = {https://github.com/yongzhuo/macro-correct},
+    author = {Yongzhuo Mo},
+    title = {macro-correct},
+    year = {2025}
+```
+

config.json

@@ -0,0 +1,30 @@
+{
+  "_name_or_path": "chinese-bert-base",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

csc.config

@@ -0,0 +1,47 @@
+{
+    "pretrained_model_name_or_path": "",
+    "path_relm": "relm-m0.3.bin",
+    "path_train": "csc_public.train.json",
+    "path_dev": "csc_public.dev.json",
+    "path_tet": "csc_public.tet.json",
+    "model_save_path": "../output/relm_v1",
+    "task_name": "relm_csc",
+    "do_lower_case": true,
+    "do_train": true,
+    "do_eval": true,
+    "do_test": true,
+    "gradient_accumulation_steps": 4,
+    "warmup_proportion": 0.1,
+    "num_warmup_steps": null,
+    "max_train_steps": null,
+    "num_train_epochs": 3,
+    "train_batch_size": 8,
+    "eval_batch_size": 8,
+    "learning_rate": 3e-05,
+    "max_seq_length": 256,
+    "max_grad_norm": 1.0,
+    "weight_decay": 0.0005,
+    "save_steps": 1000,
+    "anchor": null,
+    "seed": 42,
+    "lr_scheduler_type": "cosine",
+    "loss_type": "focal_loss",
+    "mask_mode": "noerror",
+    "loss_det_rate": 0.3,
+    "prompt_length": 0,
+    "mask_rate": 0.3,
+    "threshold": 0.5,
+    "flag_dynamic_encode": false,
+    "flag_loss_period": false,
+    "flag_cpo_loss": false,
+    "flag_fast_tokenizer": true,
+    "flag_pin_memory": true,
+    "flag_train": false,
+    "flag_fp16": false,
+    "flag_cuda": true,
+    "flag_skip": true,
+    "flag_mft": true,
+    "num_workers": 0,
+    "CUDA_VISIBLE_DEVICES": "0",
+    "USE_TORCH": "1"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee638ee68d9c61280fd196ea1cbf94999a4506ab68062ab8811320467693ce53
+size 409230197

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,13 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}