文件 · repro · PaddlePaddle / ERNIE · GitCode

XNLI is a natural language inference dataset in 15 languages. It was jointly built by Facebook and New York University. We use Chinese data of XNLI to evaluate language understanding ability of our model. [url: https://github.com/facebookresearch/XNLI]
DuReader is a new large-scale, open-domain Chinese machine reading comprehension (MRC) dataset, which is designed to address real-world MRC. This dataset was released in ACL2018 (He et al., 2018) by Baidu. In this dataset, questions and documents are based on Baidu Search and Baidu Zhidao, answers are manually generated.
Our experiment was carried out on an extractive single-document subset of DuReader. The training set contained 15,763 documents and questions, and the validation set contained 1628 documents and questions. The goal was to extract continuous fragments from documents as answers. [url: https://arxiv.org/pdf/1711.05073.pdf]
CMRC2018 is a evaluation of Chinese extractive reading comprehension hosted by Chinese Information Processing Society of China (CIPS-CL). [url: https://github.com/ymcui/cmrc2018]
DRCD is an open domain Traditional Chinese machine reading comprehension (MRC) dataset released by Delta Research Center. We translate this dataset to Simplified Chinese for our experiment. [url: https://github.com/DRCKnowledgeTeam/DRCD]
MSRA-NER (SIGHAN2006) dataset is released by MSRA for recognizing the names of people, locations and organizations in text.
ChnSentiCorp is a sentiment analysis dataset consisting of reviews on online shopping of hotels, notebooks and books.
NLPCC2016-DBQA is a sub-task of NLPCC-ICCPOL 2016 Shared Task which is hosted by NLPCC(Natural Language Processing and Chinese Computing), this task targets on selecting documents from the candidates to answer the questions. [url: http://tcci.ccf.org.cn/conference/2016/dldoc/evagline2.pdf]
LCQMC is a Chinese question semantic matching corpus published in COLING2018. [url: http://aclweb.org/anthology/C18-1166]
BQ Corpus (Bank Question corpus) is a Chinese corpus for sentence semantic equivalence identification. This dataset was published in EMNLP 2018. [url: https://www.aclweb.org/anthology/D18-1536]
pip install -r requirements.txt
label  text_a
...
0   hide new secretions from the parental units
0   contains no wit , only labored gags
1   that loves its characters and communicates something rather beautiful about human nature
0   remains utterly satisfied to remain the same throughout
0   on the worst revenge-of-the-nerds clichés the filmmakers could dredge up
0   that 's far too tragic to merit such superficial treatment
1   demonstrates that the director of such hollywood blockbusters as patriot games can still turn out a small , personal film with an emotional wallop .
1   of saucy
...
export TASK_DATA_PATH=(the value of ${TASK_DATA_PATH} mentioned above)
export MODEL_PATH=(the value of ${MODEL_PATH} mentioned above)
epoch: 3, progress: 22456/67349, step: 3500, ave loss: 0.015862, ave acc: 0.984375, speed: 1.328810 steps/s
[dev evaluation] ave loss: 0.174793, acc:0.957569, data_num: 872, elapsed time: 15.314256 s file: ./data/dev.tsv, epoch: 3, steps: 3500
testing ./data/test.tsv, save to output/test_out.tsv
[dev evaluation] ave loss: 0.303819, acc:0.943333, data_num: 1200, elapsed time: 16.280898 s, file: ./task_data/chnsenticorp/dev.tsv, epoch: 9, steps: 4001
[dev evaluation] ave loss: 0.228482, acc:0.958333, data_num: 1200, elapsed time: 16.023091 s, file: ./task_data/chnsenticorp/test.tsv, epoch: 9, steps: 4001
text_a  text_b  label
Oil prices fall back as Yukos oil threat lifted Oil prices rise.    0
No Weapons of Mass Destruction Found in Iraq Yet.   Weapons of Mass Destruction Found in Iraq.  0
Iran is said to give up al Qaeda members.   Iran hands over al Qaeda members.   1
Sani-Seat can offset the rising cost of paper products  The cost of paper is rising.    1
export TASK_DATA_PATH=(the value of ${TASK_DATA_PATH} mentioned above)
export MODEL_PATH=(the value of ${MODEL_PATH} mentioned above)
epoch: 4, progress: 2489/2490, step: 760, ave loss: 0.000729, ave acc: 1.000000, speed: 1.221889 steps/s
train pyreader queue size: 9, learning rate: 0.000000
epoch: 4, progress: 2489/2490, step: 770, ave loss: 0.000833, ave acc: 1.000000, speed: 1.246080 steps/s
train pyreader queue size: 0, learning rate: 0.000000
epoch: 4, progress: 2489/2490, step: 780, ave loss: 0.000786, ave acc: 1.000000, speed: 1.265365 steps/s
validation result of dataset ./data/dev.tsv:
[dev evaluation] ave loss: 0.898279, acc:0.851986, data_num: 277, elapsed time: 6.425834 s file: ./data/dev.tsv, epoch: 4, steps: 781
testing ./data/test.tsv, save to output/test_out.5.2019-07-23-15-25-06.tsv.4.781
text_a  label
在 这 里 恕 弟 不 恭 之 罪 ， 敢 在 尊 前 一 诤 ： 前 人 论 书 ， 每 曰 “ 字 字 有 来 历 ， 笔 笔 有 出 处 ” ， 细 读 公 字 ， 何 尝 跳 出 前 人 藩 篱 ， 自 隶 变 而 后 ， 直 至 明 季 ， 兄 有 何 新 出 ？    O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
相 比 之 下 ， 青 岛 海 牛 队 和 广 州 松 日 队 的 雨 中 之 战 虽 然 也 是 0 ∶ 0 ， 但 乏 善 可 陈 。   O O O O O B-ORG I-ORG I-ORG I-ORG I-ORG O B-ORG I-ORG I-ORG I-ORG I-ORG O O O O O O O O O O O O O O O O O O O
理 由 多 多 ， 最 无 奈 的 却 是 ： 5 月 恰 逢 双 重 考 试 ， 她 攻 读 的 博 士 学 位 论 文 要 通 考 ； 她 任 教 的 两 所 学 校 ， 也 要 在 这 段 时 日 大 考 。    O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
[dev evaluation] f1: 0.951949, precision: 0.944636, recall: 0.959376, elapsed time: 19.156693 s
[test evaluation] f1: 0.937390, precision: 0.925988, recall: 0.949077, elapsed time: 36.565929 s
{
 "version": "1.3",
 "data": [
   {
     "paragraphs": [
       {
         "id": "1001-11",
         "context": "广州是京广铁路、广深铁路、广茂铁路、广梅汕铁路的终点站。2009年末，武广客运专线投入运营，多单元列车覆盖980公里的路程，最高时速可达350公里/小时。2011年1月7日，广珠城际铁路投入运营，平均时速可达200公里/小时。广州铁路、长途汽车和渡轮直达香港，广九直通车从广州东站开出，直达香港九龙红磡站，总长度约182公里，车程在两小时内。繁忙的长途汽车每年会从城市中的不同载客点把旅客接载至香港。在珠江靠市中心的北航道有渡轮线路，用于近江居民直接渡江而无需乘坐公交或步行过桥。南沙码头和莲花山码头间每天都有高速双体船往返，渡轮也开往香港中国客运码头和港澳码头。",
         "qas": [
           {
             "question": "广珠城际铁路平均每小时可以走多远？",
             "id": "1001-11-1",
             "answers": [
               {
                 "text": "200公里",
                 "answer_start": 104,
                 "id": "1"
               }
             ]
           }
         ]
       }
     ],
     "id": "1001",
     "title": "广州"
   }
 ]
}
[dev evaluation] em: 88.450624, f1: 93.749887, avg: 91.100255, question_num: 3524
[test evaluation] em: 88.061838, f1: 93.520152, avg: 90.790995, question_num: 3493
1 1048 492 1333 1361 1051 326 2508 5 1803 1827 98 164 133 2777 2696 983 121 4 19 9 634 551 844 85 14 2476 1895 33 13 983 121 23 7 1093 24 46 660 12043 2 1263 6 328 33 121 126 398 276 315 5 63 44 35 25 12043 2;0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1;0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55;-1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 -1 0 0 0 1 0 0 1 0 1 0 0 1 0 1 0 -1;0
current learning_rate:0.000001
epoch: 1, progress: 1/1, step: 30, loss: 10.540648, ppl: 19106.925781, next_sent_acc: 0.625000, speed: 0.849662 steps/s, file: ./data/demo_train_set.gz, mask_type: mask_word
feed_queue size 70
current learning_rate:0.000001
epoch: 1, progress: 1/1, step: 40, loss: 10.529287, ppl: 18056.654297, next_sent_acc: 0.531250, speed: 0.849549 steps/s, file: ./data/demo_train_set.gz, mask_type: mask_word
feed_queue size 70
current learning_rate:0.000001
epoch: 1, progress: 1/1, step: 50, loss: 10.360563, ppl: 16398.287109, next_sent_acc: 0.625000, speed: 0.843776 steps/s, file: ./data/demo_train_set.gz, mask_type: mask_word
export FLAGS_sync_nccl_allreduce=1
export CUDA_VISIBLE_DEVICES=0

python -u ernie_encoder.py \
                   --use_cuda true \
                   --batch_size 32 \
                   --output_dir "./test" \
                   --init_pretraining_params ${MODEL_PATH}/params \
                   --data_set ${TASK_DATA_PATH}/lcqmc/dev.tsv \
                   --vocab_path ${MODEL_PATH}/vocab.txt \
                   --max_seq_len 128 \
                   --ernie_config_path ${MODEL_PATH}/ernie_config.json
python -u infer_classifyer.py \
    --ernie_config_path ${MODEL_PATH}/ernie_config.json \
    --init_checkpoint "./checkpoints/step_100" \
    --save_inference_model_path ./saved_model \
    --predict_set  ${TASK_DATA_PATH}/xnli/test.tsv \
    --vocab_path ${MODEL_PATH}/vocab.txt \
    --num_labels 3