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代码仓库
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Arthurizijar/Bert_Airport
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Nstats/bert_MRC
tf
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qbxlvnf11/BERT
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xiuyu0000/tutorials/tree/main/bert
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google-research/bigbird
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KnightZhang625/BERT_TF
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MS-P3/code5/tree/main/bert
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hate-alert/indicabusive
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tobyatgithub/bert_tutorial
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flyliu2017/bert
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StoneGH/bert
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longbowking/bert
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qbxlvnf11/contextualized-embedding-BERT
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cmunnis/BERT_vs_Transformer-XL
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SAP-samples/acl2019-commonsense
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tensorflow/models/tree/master/official/nlp
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ambityga/memsem
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BirgerMoell/bertcastle
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Colanim/BERT_STS-B
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facebookresearch/anli
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dreamgonfly/BERT-pytorch
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Eeun-ju/NLP-study
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anton-bushuiev/ppiformer
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TSLNIHAOGIT/bert_run
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bhavitvyamalik/bert
tf
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idiotprofessorchen/bert.github.io
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brightmart/bert_original
tf
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rohanshad/cmr_transformer
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venkyr91193/Sentiment-Analysis-with-BERT
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theneuralbeing/bert-finetuning-webinar
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g-eklund/bert_tutorial
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sherinaseri/bert
tf
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lyccol/CodeBERT-based-webshell-detection
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dmlc/gluon-nlp
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kriesbeck/masked-language-prediction
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noise-learning/selfmix
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om00839/machine-suneung
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Caesarzhang/bert-zh
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lonePatient/Bert-Multi-Label-Text-Classification
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xiangyue9607/CliniRC
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cuber2460/bert
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epcpu/deep-nlp-links
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knuddy/op_text
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luckynozomi/PPI_Bert
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wzzzd/LM_NER
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re-search/DocProduct
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Priyanshiguptaaa/CoLA_Sentence_Classification_BERT
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faizansuhail89/bert
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GitHub 中提及
Katsumata420/bert_mlm
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MichaelZhouwang/LMlexsub
tf
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manideep2510/siamese-BERT-fake-news-detection-LIAR
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Microsoft/AzureML-BERT
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deping-1/2019-false-news-detection-challenge
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TannerFry/CS522-Final-Project
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thethirdwheel/malumagraph
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zaaztt/bert_chinese_example
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dispink/xpt
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chen-xiong-yi/OwnBERT
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halo090770/bert
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karenacorn99/explore-bert
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aaronHuang0121/AI-CUP-2020
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ltgoslo/assessing_and_probing_sentiment
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broccolik/BERT-NER
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zhengcj1/ChID-Dataset
tf
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smarton-empower/smarton-ai
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DeligientSloth/bert-tensorflow
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lvyufeng/mindspore-nlp-tutorial
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luozhilin99/bert
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icewing1996/bert_dep
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h4ste/oscar
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derronxu/sparsebert
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piegu/language-models
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thecodemasterk/BERT
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writerai/fitbert
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abhineet/sentence_classification_pubmed_scibert
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llx666/git_demo
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ufal/wembedding_service
tf
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Doffery/BERT-Sentiment-Analysis-Amazon-Review
tf
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kpot/keras-transformer
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linlei1214/SITS-BERT
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lonePatient/bert-sentence-similarity-pytorch
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meizi1114/bert
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lennonzurich/lalala
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bino282/bert4news
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sajjjadayobi/ParsBigBird
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athms/learning-from-brains
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helboukkouri/character-bert
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jageshmaharjan/BERT_Service
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saravanakumarjsk/BERT-collection
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HuihuiChyan/BJTUNLP_Practice2021
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somiltg/bert
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Yipeng91/text_classifier_pub
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flxst/nerblackbox
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deepmipt/bert/tree/feat/multi_gpu
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graykode/nlp-tutorial
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surancy/nlp-classification-with-BERT
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GitHub 中提及
FaskyCC/TextClassification
tf
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Misoknisky/Bert-MultiGpu
tf
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AtmaHou/MetaDialog
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owainwest/uniprot_bert
tf
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yangyucheng000/bert_Ascend
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chujiezheng/ChID-Dataset
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kingcheng2000/bert
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fancyerii/bert
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uabinf/nlp-fall-2019-project-shuhui_shuqu
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rajlm10/Chandler
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shaikhzhas/bert
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kobowon/cs470_project_version2
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gggggeun/DeepLearning_Architecture
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scut-bds/example_repo_from_scutbds
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EthanWYB/bert-classification
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nalbarr/coursera-ai4med-course3
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chambliss/Multilingual_NER
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wenhuchen/HDSA-Dialog
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samwisegamjeee/pytorch-transformers
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cedrickchee/pytorch-pretrained-BERT
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JNUpython/bert
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zsweet/BERT_zsw
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saurabhnlp/bert
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reppy4620/Dialog
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wyf-python/Bert-Pytorch-TextClassification
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hoangtrungchinh/Bert-SQuAD-v2
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KomorebiLHX/Emotion-Recognition-in-Conversations
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FengJiaChunFromSYSU/Bert
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polzinben/TensorFlow_NLP
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eunseo1092/Graduation_project
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jiyuan/ainote
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svakulenk0/response_eval
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Sikun/dlam_project
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arkel23/PyTorch-Pretrained-ViT
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Chonwai/Learning_BERT
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jungomi/swiss-language-model
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ProsusAI/finBERT
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Coda-s/BJTU_NLP_Practice
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JA-Bar/nlp-depression
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junhahyung/bert_finetune
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pytorch/text
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thu-keg/omnievent
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Domminique/Deploy-BERT-for-Sentiment-Analysis-with-FastAPI-
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af-ai-center/nerblackbox
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abhiagg92/bert_sentiment
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xiaopp123/bert_explain
tf
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heraclex12/vietpunc
jax
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andi611/Mockingjay-Speech-Representation
pytorch
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CyberZHG/keras-bert
tf
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lonePatient/BERT-chinese-text-classification-pytorch
pytorch
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TYTYTYTYTYTYTYTYTY/558-project
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GitHub 中提及
PaddlePaddle/PaddleNLP
paddle
Qordobacode/fitbert
pytorch
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octanove/grammartagger
pytorch
GitHub 中提及
yydai/bert_test
tf
GitHub 中提及
coronazap/bert_client
tf
GitHub 中提及
HuihuiChyan/BJTUNLP_Practice2020
pytorch
GitHub 中提及
kamalkraj/BERT_NER
pytorch
AbhishekD15/Sentiment-Analysis_BERT
GitHub 中提及
soutsios/pos-tagger-bert-tensorflow
tf
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CVxTz/music_genre_classification
tf
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ArvinZhuang/BiTAG
pytorch
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evtaktasheva/dependency_extraction
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IMPLabUniPr/UniParma-at-semeval-2021-task-5
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tf
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BinWang28/SBERT-WK-Sentence-Embedding
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huanghonggit/Mask-Language-Model
pytorch
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epfml/collaborative-attention
pytorch
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Skumarr53/Attention-is-All-you-Need-PyTorch
pytorch
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ricardordb/bert
tf
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imgarylai/bert-embedding
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DimasDMM/nlp-completer
pytorch
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ShivamRajSharma/Transformer-Architectures-From-Scratch
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基准测试
| 基准 | 方法 | 指标 |
|---|---|---|
| common-sense-reasoning-on-record | BERT-Base (single model) | EM: 54.040 F1: 56.065 |
| common-sense-reasoning-on-swag | BERT-LARGE | Dev: 86.6 Test: 86.3 |
| coreference-resolution-on-winograd-schema | BERT-large 340M | Accuracy: 62.0 |
| cross-lingual-natural-language-inference-on-1 | BERT | Accuracy: 74.3% |
| cross-lingual-natural-language-inference-on-3 | BERT | Accuracy: 70.5% |
| emotion-recognition-in-conversation-on-cped | BERT_{utt} | Accuracy of Sentiment: 48.96 Macro-F1 of Sentiment: 45.18 |
| linguistic-acceptability-on-cola | BERT-LARGE | Accuracy: 60.5% |
| multimodal-intent-recognition-on-photochat | BERT | F1: 53.2 Precision: 56.1 Recall: 50.6 |
| natural-language-inference-on-multinli | BERT-LARGE | Matched: 86.7 Mismatched: 85.9 |
| natural-language-inference-on-qnli | BERT-LARGE | Accuracy: 92.7% |
| natural-language-inference-on-rte | BERT-large 340M | Accuracy: 70.1% |
| natural-language-inference-on-wnli | BERT-large 340M | Accuracy: 65.1 |
| natural-language-understanding-on-glue | BERT-LARGE | Average: 82.1 |
| natural-language-understanding-on-pdp60 | BERT-large 340M | Accuracy: 78.3 |
| paraphrase-identification-on-quora-question | BERT-LARGE | F1: 72.1 |
| question-answering-on-coqa | BERT Large Augmented (single model) | In-domain: 82.5 Out-of-domain: 77.6 Overall: 81.1 |
| question-answering-on-coqa | BERT-base finetune (single model) | In-domain: 79.8 Out-of-domain: 74.1 Overall: 78.1 |
| question-answering-on-mrqa-2019 | BERT (large) | Average F1: 78.5 |
| question-answering-on-multirc | BERT-large(single model) | EM: 24.1 F1: 70.0 |
| question-answering-on-multitq | BERT | Hits@1: 8.3 Hits@10: 48.2 |
| question-answering-on-piqa | BERT-Large 340M | Accuracy: 66.7 |
| question-answering-on-squad11 | BERT-LARGE (Ensemble+TriviaQA) | EM: 87.4 F1: 93.2 |
| question-answering-on-squad11 | BERT (ensemble) | EM: 87.433 F1: 93.160 |
| question-answering-on-squad11 | BERT (single model) | EM: 85.083 F1: 91.835 |
| question-answering-on-squad11 | BERT-LARGE (Single+TriviaQA) | F1: 91.8 |
| question-answering-on-squad11-dev | BERT-LARGE (Ensemble+TriviaQA) | EM: 86.2 F1: 92.2 |
| question-answering-on-squad11-dev | BERT-LARGE (Single+TriviaQA) | EM: 84.2 F1: 91.1 |
| semantic-textual-similarity-on-mrpc | BERT-LARGE | F1: 89.3 |
| semantic-textual-similarity-on-sts-benchmark | BERT-LARGE | Spearman Correlation: 0.865 |
| sentiment-analysis-on-sst-2-binary | BERT-LARGE | Accuracy: 94.9 |
| stock-market-prediction-on-astock | Bert Chinese | Accuray: 59.11 F1-score: 58.99 Precision: 59.07 Recall: 59.20 |
| text-classification-on-dbpedia | Bidirectional Encoder Representations from Transformers | Error: 0.64 |
| type-prediction-on-manytypes4typescript | BERT | Average Accuracy: 57.52 Average F1: 54.10 Average Precision: 54.18 Average Recall: 54.02 |