HateXplain:我们可以使用解释来改善仇恨言论模型吗？ 论文被AAAI 2021接受
:magnifying_glass_tilted_right: HateXplain：可解释的仇恨语音检测的基准数据集[AAAI 2021接受]
:party_popper: :party_popper: BERT检测仇恨言论从我们的数据与训练的理由可以。 一定要检查一下 :party_popper: :party_popper: 。
:party_popper: :party_popper: 现在，您可以从Huggingface数据集加载我们的数据集。 一定要检查一下 :party_popper: :party_popper: 。
:party_popper: :party_popper: 看看我们的预印本以获得更详细的信息 :party_popper: :party_popper: 。
抽象的
仇恨言论是困扰在线社交媒体的一个具有挑战性的问题。 尽管不断开发出更好的仇恨语音检测模型，但对仇恨语音的偏见和可解释性方面的研究很少。 在这项工作中，我们介绍了HateXplain，这是涵盖该问题多个方面的第一个基准讨厌语音数据集。 我们数据集中的每个帖子都从三个不同的角度
文件下载
资源详情
[{"title":"（ 87 个子文件 2.37MB ） HateXplain:我们可以使用解释来改善仇恨言论模型吗？ 论文被AAAI 2021接受","children":[{"title":"HateXplain-master","children":[{"title":"test_parallel.sh <span style='color:#111;'> 1.14KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 2.21KB </span>","children":null,"spread":false},{"title":"eraserbenchmark","children":[{"title":".gitignore <span style='color:#111;'> 1.29KB </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 194B </span>","children":null,"spread":false},{"title":"REPRODUCTION.txt <span style='color:#111;'> 3.62KB </span>","children":null,"spread":false},{"title":"params","children":[{"title":"multirc_baas.json <span style='color:#111;'> 610B </span>","children":null,"spread":false},{"title":"fever_soft.json <span style='color:#111;'> 492B </span>","children":null,"spread":false},{"title":"multirc_bert.json <span style='color:#111;'> 798B </span>","children":null,"spread":false},{"title":"boolq_soft.json <span style='color:#111;'> 485B </span>","children":null,"spread":false},{"title":"boolq.json <span style='color:#111;'> 621B </span>","children":null,"spread":false},{"title":"boolq_bert.json <span style='color:#111;'> 798B </span>","children":null,"spread":false},{"title":"esnli_bert.json <span style='color:#111;'> 772B </span>","children":null,"spread":false},{"title":"boolq_baas.json <span style='color:#111;'> 610B </span>","children":null,"spread":false},{"title":"evidence_inference_bert.json <span style='color:#111;'> 906B </span>","children":null,"spread":false},{"title":"movies_soft.json <span style='color:#111;'> 482B </span>","children":null,"spread":false},{"title":"fever_baas.json <span style='color:#111;'> 615B </span>","children":null,"spread":false},{"title":"multirc_soft.json <span style='color:#111;'> 485B </span>","children":null,"spread":false},{"title":"fever.json <span style='color:#111;'> 629B </span>","children":null,"spread":false},{"title":"movies_bert.json <span style='color:#111;'> 795B </span>","children":null,"spread":false},{"title":"movies.json <span style='color:#111;'> 620B </span>","children":null,"spread":false},{"title":"movies_baas.json <span style='color:#111;'> 607B </span>","children":null,"spread":false},{"title":"multirc.json <span style='color:#111;'> 623B </span>","children":null,"spread":false},{"title":"cose_bert.json <span style='color:#111;'> 808B </span>","children":null,"spread":false},{"title":"evidence_inference.json <span style='color:#111;'> 686B </span>","children":null,"spread":false},{"title":"evidence_inference_soft.json <span style='color:#111;'> 582B </span>","children":null,"spread":false},{"title":"fever_bert.json <span style='color:#111;'> 810B </span>","children":null,"spread":false}],"spread":false},{"title":"LICENSE <span style='color:#111;'> 11.09KB </span>","children":null,"spread":false},{"title":"data_exploration.ipynb <span style='color:#111;'> 11.91KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 2.73KB </span>","children":null,"spread":false},{"title":"rationale_benchmark","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"models","children":[{"title":"mlp.py <span style='color:#111;'> 13.43KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"sequence_taggers.py <span style='color:#111;'> 2.68KB </span>","children":null,"spread":false},{"title":"encode_attend.py <span style='color:#111;'> 26.87KB </span>","children":null,"spread":false},{"title":"model_utils.py <span style='color:#111;'> 6.58KB </span>","children":null,"spread":false},{"title":"pipeline","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"bert_pipeline.py <span style='color:#111;'> 18.47KB </span>","children":null,"spread":false},{"title":"evidence_token_identifier.py <span style='color:#111;'> 13.28KB </span>","children":null,"spread":false},{"title":"pipeline_utils.py <span style='color:#111;'> 45.34KB </span>","children":null,"spread":false},{"title":"evidence_identifier.py <span style='color:#111;'> 13.27KB </span>","children":null,"spread":false},{"title":"evidence_classifier.py <span style='color:#111;'> 9.89KB </span>","children":null,"spread":false},{"title":"pipeline_train.py <span style='color:#111;'> 10.06KB </span>","children":null,"spread":false}],"spread":false}],"spread":true},{"title":"utils.py <span style='color:#111;'> 7.97KB </span>","children":null,"spread":false},{"title":"metrics.py <span style='color:#111;'> 36.57KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Parameters_description.md <span style='color:#111;'> 4.44KB </span>","children":null,"spread":false},{"title":"convert_to_word2vec.py <span style='color:#111;'> 587B </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 301B </span>","children":null,"spread":false},{"title":"Explainability_Calculation_NB.ipynb <span style='color:#111;'> 17.82KB </span>","children":null,"spread":false},{"title":"TensorDataset","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"dataLoader.py <span style='color:#111;'> 2.08KB </span>","children":null,"spread":false},{"title":"datsetSplitter.py <span style='color:#111;'> 5.07KB </span>","children":null,"spread":false}],"spread":true},{"title":"testing_with_lime.py <span style='color:#111;'> 14.76KB </span>","children":null,"spread":false},{"title":"best_model_json","children":[{"title":"bestModel_bert_base_uncased_Attn_train_FALSE.json <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"bestModel_birnnscrat.json <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"bestModel_cnn_gru.json <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"bestModel_birnnatt.json <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false},{"title":"bestModel_bert_base_uncased_Attn_train_TRUE.json <span style='color:#111;'> 1.22KB </span>","children":null,"spread":false},{"title":"bestModel_birnn.json <span style='color:#111;'> 1.21KB </span>","children":null,"spread":false}],"spread":true},{"title":"Bias_Calculation_NB.ipynb <span style='color:#111;'> 70.66KB </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false},{"title":"best_runs.sh <span style='color:#111;'> 1.64KB </span>","children":null,"spread":false},{"title":"manual_training_inference.py <span style='color:#111;'> 20.97KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 6.72KB </span>","children":null,"spread":false},{"title":"Example_HateExplain.ipynb <span style='color:#111;'> 207.78KB </span>","children":null,"spread":false},{"title":"testing_for_bias.py <span style='color:#111;'> 10.78KB </span>","children":null,"spread":false},{"title":"Data","children":[{"title":"post_id_divisions.json <span style='color:#111;'> 578.05KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 2.98KB </span>","children":null,"spread":false},{"title":"dataset.json <span style='color:#111;'> 11.69MB </span>","children":null,"spread":false},{"title":"classes_two.npy <span style='color:#111;'> 352B </span>","children":null,"spread":false},{"title":"classes.npy <span style='color:#111;'> 248B </span>","children":null,"spread":false}],"spread":false},{"title":"Preprocess","children":[{"title":"dataCollect.py <span style='color:#111;'> 13.74KB </span>","children":null,"spread":false},{"title":"spanMatcher.py <span style='color:#111;'> 12.63KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 484B </span>","children":null,"spread":false},{"title":"preProcess.py <span style='color:#111;'> 7.65KB </span>","children":null,"spread":false},{"title":"attentionCal.py <span style='color:#111;'> 4.94KB </span>","children":null,"spread":false}],"spread":false},{"title":"Figures","children":[{"title":"dataset_example.png <span style='color:#111;'> 78.66KB </span>","children":null,"spread":false},{"title":"bias-subgroup.pdf <span style='color:#111;'> 18.22KB </span>","children":null,"spread":false},{"title":"bias-bnsp.pdf <span style='color:#111;'> 18.25KB </span>","children":null,"spread":false},{"title":"bias-bpsn.pdf <span style='color:#111;'> 18.25KB </span>","children":null,"spread":false}],"spread":false},{"title":"testing_with_rational.py <span style='color:#111;'> 13.04KB </span>","children":null,"spread":false},{"title":"parameters_selection.py <span style='color:#111;'> 4.00KB </span>","children":null,"spread":false},{"title":"Models","children":[{"title":"otherModels.py <span style='color:#111;'> 7.65KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 8.65KB </span>","children":null,"spread":false},{"title":"bertModels.py <span style='color:#111;'> 2.19KB </span>","children":null,"spread":false},{"title":"attentionLayer.py <span style='color:#111;'> 5.20KB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"spread":true}]
评论信息
其他资源
- 全国增值税发票查验平台验证码识别
- CRC校验的MATLAB程序
- c#接收邮件 c#接收邮件
- Android电子书阅读器完整源码
- 基于Hbuilder X 开发的酒店预约系统
- R语言实现多元线性回归
- GIS 百度地图
- Python-深度学习与自然语言处理知识图谱对话系统
- PyQt5系列教程(二)利用QtDesigner设计UI界面源码包
- OpenCV算法精解——源码和测试图片
- 2019年小猪o2o破解版源码v11.03生活通系统swoole-loader解密组件分享.zip
- RabbitMQ原理及应用.pdf
- wa2200_fat.bin
- 基于GemFire的海量数据计算性能实验分析
- 北方交通大学材料力学2006年硕士研究生入学考试试题.zip
- ILI6123CPT_SPEC_V004_20091116.pdf
- d3_折线_柱状_地图.zip
- 局域网音视频广播软件
- Xilinx《Parallel Progeamming for FPGAs ——The HLS Book》中文版
- VC笔记-如何从txt文本中读写结构体数据
- VC画波形图，二进制读写文件
- c语言编程中常见的陷阱和漏洞
- bool商城代码+笔记+流程图
- 基于GIS城市地理信息系统源代码
免责申明
【只为小站】的资源来自网友分享，仅供学习研究，请务必在下载后24小时内给予删除，不得用于其他任何用途，否则后果自负。基于互联网的特殊性，【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查；无论 【只为小站】 经营者是否已进行审查，用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明