English | 简体中文
- Pre-training Tasks
- ERNIE 1.0: Enhanced Representation through kNowledge IntEgration
- Compare the ERNIE 1.0 and ERNIE 2.0
- Results
- Communication
- Usage
ERNIE 2.0 is a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through multi-task learning. In this framework, different customized tasks can be incrementally introduced at any time. For example, the tasks including named entity prediction, discourse relation recognition, sentence order prediction are leveraged in order to enable the models to learn language representations.
We compare the performance of ERNIE 2.0 model with the existing SOTA pre-training models on the authoritative English dataset GLUE and 9 popular Chinese datasets separately. And the results show that ERNIE 2.0 model outperforms BERT and XLNet on 7 GLUE tasks and outperforms BERT on all of the 9 Chinese NLP tasks. Specifically, according to the experimental results on GLUE datasets, we observe that ERNIE 2.0 model almost comprehensively outperforms BERT and XLNet on English tasks, whether the base model or the large model. And according to the experimental results on all Chinese datasets, ERNIE 2.0 model comprehensively outperforms BERT on all of the 9 Chinese datasets. Furthermore, ERNIE 2.0 large model achieves the best performance and creates new state-of-the-art results on these Chinese NLP task.
We construct several tasks to capture different aspects of information in the training corpora:
- Word-aware Tasks: to handle the lexical information
- Structure-aware Tasks: to capture the syntactic information
- Semantic-aware Tasks: in charge of semantic signals
At the same time, ERINE 2.0 feeds task embedding to model the characteristic of different tasks. We represent different tasks with an ID ranging from 0 to N. Each task ID is assigned to one unique task embedding.
- ERNIE 1.0 introduced phrase and named entity masking strategies to help the model learn the dependency information in both local contexts and global contexts.
- Capitalized words usually have certain specific semantic value compared to other words in sentences. we add a task to predict whether the word is capitalized or not.
- A task to predict whether the token in a segment appears in other segments of the original document.
- This task try to learn the relationships among sentences by randomly spliting a given paragraph into 1 to m segments and reorganizing these permuted segments as a standard classification task.
- This task handles the distance between sentences as a 3-class classification problem.
- A task try to predict the semantic or rhetorical relation between two sentences.
- A 3-class classification task which predicts the relationship between a query and a title.
ERNIE 1.0 is a new unsupervised language representation learning method enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Inspired by the masking strategy of BERT (Devlin et al., 2018), ERNIE introduced phrase masking and named entity masking and predicts the whole masked phrases or named entities. Phrase-level strategy masks the whole phrase which is a group of words that functions as a conceptual unit. Entity-level strategy masks named entities including persons, locations, organizations, products, etc., which can be denoted with proper names.
Example:
Harry Potter is a series of fantasy novel written by J. K. Rowling
- Learned by BERT ：[mask] Potter is a series [mask] fantasy novel [mask] by J. [mask] Rowling
- Learned by ERNIE：Harry Potter is a series of [mask] [mask] written by [mask] [mask] [mask]
In the example sentence above, BERT can identify the “K.” through the local co-occurring words J., K., and Rowling, but the model fails to learn any knowledge related to the word "J. K. Rowling". ERNIE however can extrapolate the relationship between Harry Potter and J. K. Rowling by analyzing implicit knowledge of words and entities, and infer that Harry Potter is a novel written by J. K. Rowling.
Integrating both phrase information and named entity information enables the model to obtain better language representation compare to BERT. ERNIE is trained on multi-source data and knowledge collected from encyclopedia articles, news, and forum dialogues, which improves its performance in context-based knowledge reasoning.
| Tasks | ERNIE model 1.0 | ERNIE model 2.0 (en) | ERNIE model 2.0 (zh) |
|---|---|---|---|
| Word-aware | ✅ Knowledge Masking | ✅ Knowledge Masking ✅ Capitalization Prediction ✅ Token-Document Relation Prediction |
✅ Knowledge Masking |
| Structure-aware | ✅ Sentence Reordering | ✅ Sentence Reordering ✅ Sentence Distance |
|
| Semantic-aware | ✅ Next Sentence Prediction | ✅ Discourse Relation | ✅ Discourse Relation ✅ IR Relevance |
The English version ERNIE 2.0 is evaluated on GLUE benchmark including 10 datasets and 11 test sets, which cover tasks about Natural Language Inference, e.g., MNLI, Sentiment Analysis, e.g., SST-2, Coreference Resolution, e.g., WNLI and so on. We compare single model ERNIE 2.0 with XLNet and BERT on GLUE dev set according to the result in the paper XLNet (Z. Yang. etc) and compare with BERT on GLUE test set according to the open leaderboard.
| Dataset | CoLA | SST-2 | MRPC | STS-B | QQP | MNLI-m | QNLI | RTE |
|---|---|---|---|---|---|---|---|---|
| metric | matthews corr. | acc | acc | pearson corr. | acc | acc | acc | acc |
| BERT Large | 60.6 | 93.2 | 88.0 | 90.0 | 91.3 | 86.6 | 92.3 | 70.4 |
| XLNet Large | 63.6 | 95.6 | 89.2 | 91.8 | 91.8 | 89.8 | 93.9 | 83.8 |
| ERNIE 2.0 Large | 65.4 (+4.8,+1.8) |
96.0 (+2.8,+0.4) |
89.7 (+1.7,+0.5) |
92.3 (+2.3,+0.5) |
92.5 (+1.2,+0.7) |
89.1 (+2.5,-0.7) |
94.3 (+2.0,+0.4) |
85.2 (+14.8,+1.4) |
We use single-task dev results in the table.
| Dataset | - | CoLA | SST-2 | MRPC | STS-B | QQP | MNLI-m | MNLI-mm | QNLI | RTE | WNLI | AX |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Metric | score | matthews corr. | acc | f1-score/acc | spearman/pearson corr. | f1-score/acc | acc | acc | acc | acc | acc | matthews corr. |
| BERT Base | 78.3 | 52.1 | 93.5 | 88.9/84.8 | 85.8/87.1 | 71.2/89.2 | 84.6 | 83.4 | 90.5 | 66.4 | 65.1 | 34.2 |
| ERNIE 2.0 Base | 80.6 (+2.3) |
55.2 (+3.1) |
95.0 (+1.5) |
89.9/86.1 (+1.0/+1.3) |
86.5/87.6 (+0.7/+0.5) |
73.2/89.8 (+2.0/+0.6) |
86.1 (+1.5) |
85.5 (+2.1) |
92.9 (+2.4) |
74.8 (+8.4) |
65.1 | 37.4 (+3.2) |
| BERT Large | 80.5 | 60.5 | 94.9 | 89.3/85.4 | 86.5/87.6 | 72.1/89.3 | 86.7 | 85.9 | 92.7 | 70.1 | 65.1 | 39.6 |
| ERNIE 2.0 Large | 83.6 (+3.1) |
63.5 (+3.0) |
95.6 (+0.7) |
90.2/87.4 (+0.9/+2.0) |
90.6/91.2 (+4.1/+3.6) |
73.8/90.1 (+1.7/+0.8) |
88.7 (+2.0) |
88.8 (+2.9) |
94.6 (+1.9) |
80.2 (+10.1) |
67.8 (+2.7) |
48.0 (+8.4) |
Because XLNet have not published single model test result on GLUE, so we only compare ERNIE 2.0 with BERT here.
| Dataset
|
XNLI | |
|---|---|---|
|
Metric
|
acc
|
|
|
dev
|
test
|
|
|
BERT Base
|
78.1 | 77.2 |
|
ERNIE 1.0 Base
|
79.9 (+1.8) | 78.4 (+1.2) |
|
ERNIE 2.0 Base
|
81.2 (+3.1) | 79.7 (+2.5) |
|
ERNIE 2.0 Large
|
82.6 (+4.5) | 81.0 (+3.8) |
- XNLI
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]
| Dataset
|
DuReader | CMRC2018 | DRCD | |||||
|---|---|---|---|---|---|---|---|---|
|
Metric
|
em
|
f1-score
|
em
|
f1-score
|
em
|
f1-score
|
||
|
dev
|
dev
|
dev
|
test
|
dev
|
test
|
|||
| BERT Base | 59.5 | 73.1 | 66.3 | 85.9 | 85.7 | 84.9 | 91.6 | 90.9 |
| ERNIE 1.0 Base | 57.9 (-1.6) | 72.1 (-1.0) | 65.1 (-1.2) | 85.1 (-0.8) | 84.6 (-1.1) | 84.0 (-0.9) | 90.9 (-0.7) | 90.5 (-0.4) |
| ERNIE 2.0 Base | 61.3 (+1.8) | 74.9 (+1.8) | 69.1 (+2.8) | 88.6 (+2.7) | 88.5 (+2.8) | 88.0 (+3.1) | 93.8 (+2.2) | 93.4 (+2.5) |
| ERNIE 2.0 Large | 64.2 (+4.7) | 77.3 (+4.2) | 71.5 (+5.2) | 89.9 (+4.0) | 89.7 (+4.0) | 89.0 (+4.1) | 94.7 (+3.1) | 94.2 (+3.3) |
*The extractive single-document subset of DuReader dataset is an internal data set
*The DRCD dataset is converted from Traditional Chinese to Simplified Chinese based on tool: https://github.com/skydark/nstools/tree/master/zhtools
* The pre-training data of ERNIE 1.0 BASE does not contain instances whose length exceeds 128, but other models is pre-trained with the instances whose length are 512. It causes poorer performance of ERNIE 1.0 BASE on long-text tasks. So We have released ERNIE 1.0 Base (max-len-512) on July 29th, 2019
- DuReader
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
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
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]
| Dataset
|
MSRA-NER (SIGHAN2006) | |
|---|---|---|
|
Metric
|
f1-score
|
|
|
dev
|
test
|
|
| BERT Base | 94.0 | 92.6 |
| ERNIE 1.0 Base | 95.0 (+1.0) | 93.8 (+1.2) |
| ERNIE 2.0 Base | 95.2 (+1.2) | 93.8 (+1.2) |
| ERNIE 2.0 Large | 96.3 (+2.3) | 95.0 (+2.4) |
- MSRA-NER (SIGHAN2006)
MSRA-NER (SIGHAN2006) dataset is released by MSRA for recognizing the names of people, locations and organizations in text.
| Dataset
|
ChnSentiCorp | |
|---|---|---|
|
Metric
|
acc
|
|
|
dev
|
test
|
|
| BERT Base | 94.6 | 94.3 |
| ERNIE 1.0 Base | 95.2 (+0.6) | 95.4 (+1.1) |
| ERNIE 2.0 Base | 95.7 (+1.1) | 95.5 (+1.2) |
| ERNIE 2.0 Large | 96.1 (+1.5) | 95.8 (+1.5) |
- ChnSentiCorp
ChnSentiCorp is a sentiment analysis dataset consisting of reviews on online shopping of hotels, notebooks and books.
| Datset
|
NLPCC2016-DBQA | |||
|---|---|---|---|---|
|
Metric
|
mrr
|
f1-score
|
||
|
dev
|
test
|
dev
|
test
|
|
| BERT Base | 94.7 | 94.6 | 80.7 | 80.8 |
| ERNIE 1.0 Base | 95.0 (+0.3) | 95.1 (+0.5) | 82.3 (+1.6) | 82.7 (+1.9) |
| ERNIE 2.0 Base | 95.7 (+1.0) | 95.7 (+1.1) | 84.7 (+4.0) | 85.3 (+4.5) |
| ERNIE 2.0 Large | 95.9 (+1.2) | 95.8 (+1.2) | 85.3 (+4.6) | 85.8 (+5.0) |
- NLPCC2016-DBQA
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]
| Dataset
|
LCQMC | BQ Corpus | ||
|---|---|---|---|---|
|
Metric
|
acc | acc | ||
|
dev
|
test
|
dev
|
test
|
|
| BERT Base | 88.8 | 87.0 | 85.9 | 84.8 |
| ERNIE 1.0 Base | 89.7 (+0.9) | 87.4 (+0.4) | 86.1 (+0.2) | 84.8 |
| ERNIE 2.0 Base | 90.9 (+2.1) | 87.9 (+0.9) | 86.4 (+0.5) | 85.0 (+0.2) |
| ERNIE 2.0 Large | 90.9 (+2.1) | 87.9 (+0.9) | 86.5 (+0.6) | 85.2 (+0.4) |
* You can apply to the dataset owners for LCQMC、BQ Corpus. For the LCQMC: http://icrc.hitsz.edu.cn/info/1037/1146.htm, For BQ Corpus: http://icrc.hitsz.edu.cn/Article/show/175.html
- LCQMC
LCQMC is a Chinese question semantic matching corpus published in COLING2018. [url: http://aclweb.org/anthology/C18-1166]
- BQ Corpus
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]
- Github Issues: bug reports, feature requests, install issues, usage issues, etc.
- QQ discussion group: 760439550 (ERNIE discussion group).
- Forums: discuss implementations, research, etc.
- Install PaddlePaddle
- Pre-trained Models & Datasets
- Fine-tuning
- Pre-training with ERNIE 1.0
- Distillation
- FAQ
- FAQ1: How to get sentence/tokens embedding of ERNIE?
- FAQ2: How to predict on new data with Fine-tuning model?
- FAQ3: Is the argument batch_size for one GPU card or for all GPU cards?
- FAQ4: Can not find library: libcudnn.so. Please try to add the lib path to LD_LIBRARY_PATH.
- FAQ5: Can not find library: libnccl.so. Please try to add the lib path to LD_LIBRARY_PATH.
- FQA6: Runtime error:
ModuleNotFoundError No module named propeller
This code base has been tested with Paddle Fluid 1.5.1 under Python2.
*Important* When finished installing Paddle Fluid, remember to update LD_LIBRARY_PATH about CUDA, cuDNN, NCCL2, for more information on paddlepaddle setup, you can click here and here. Also, you can read FAQ at the end of this document when you encounter errors.
For beginners of PaddlePaddle, the following documentation will tutor you about installing PaddlePaddle:
- Installation Manuals ：Installation on Ubuntu/CentOS/Windows/MacOS is supported.
If you have been armed with certain level of deep learning knowledge, and it happens to be the first time to try PaddlePaddle, the following cases of model building will expedite your learning process:
- Programming with Fluid ： Core concepts and basic usage of Fluid
- Deep Learning Basics： This section encompasses various fields of fundamental deep learning knowledge, such as image classification, customized recommendation, machine translation, and examples implemented by Fluid are provided.
For more information about paddlepadde, Please refer to PaddlePaddle Github or Official Website for details.
Other dependency of ERNIE is listed in requirements.txt, you can install it by
pip install -r requirements.txt
| Model | Description |
|---|---|
| ERNIE 1.0 Base for Chinese | with params |
| ERNIE 1.0 Base for Chinese | with params, config and vocabs |
| ERNIE 1.0 Base for Chinese(max-len-512) | with params, config and vocabs |
| ERNIE 2.0 Base for English | with params, config and vocabs |
| ERNIE 2.0 Large for English | with params, config and vocabs |
Download the GLUE data by running this script and unpack it to some directory ${TASK_DATA_PATH}
After the dataset is downloaded, you should run sh ./script/en_glue/preprocess/cvt.sh $TASK_DATA_PATH to convert the data format for training. If everything goes well, there will be a folder named glue_data_processed created with all the converted datas in it.
You can download Chinese Datasets from here
In our experiments, we found that the batch size is important for different tasks. For users can more easily reproducing results, we list the batch size and gpu cards here:
| Dataset | Batch Size | GPU |
|---|---|---|
| CoLA | 32 / 64 (base) | 1 |
| SST-2 | 64 / 256 (base) | 8 |
| STS-B | 128 | 8 |
| QQP | 256 | 8 |
| MNLI | 256 / 512 (base) | 8 |
| QNLI | 256 | 8 |
| RTE | 16 / 4 (base) | 1 |
| MRPC | 16 / 32 (base) | 2 |
| WNLI | 8 | 1 |
| XNLI | 65536 (tokens) | 8 |
| CMRC2018 | 64 | 8 (large) / 4(base) |
| DRCD | 64 | 8 (large) / 4(base) |
| MSRA-NER(SIGHAN2006) | 16 | 1 |
| ChnSentiCorp | 24 | 1 |
| LCQMC | 32 | 1 |
| BQ Corpus | 64 | 1 |
| NLPCC2016-DBQA | 64 | 8 |
* For MNLI, QNLI，we used 32GB V100, for other tasks we used 22GB P40
multiprocessing finetuning can be simply enabled with finetune_launch.py in your finetune script.
with multiprocessing finetune paddle can fully utilize your CPU/GPU capacity to accelerate finetuning.
finetune_launch.py should place in front of your finetune command. make sure to provide number of process and device id per node by specifiying --nproc_per_node and --selected_gpus. Number of device ids should match nproc_per_node and CUDA_VISIBLE_DEVICES, and the indexing should start from 0.
fp16 finetuning can be simply enable by specifing --use_fp16 true in your training script (make sure you use have a Tensor Core device). ERNIE will cast computation op to fp16 precision, while maintain storage in fp32 precision. approximately 60% speedup is seen on XNLI finetuning.
dynamic loss scale is used to avoid gradient vanish.
The code used to perform classification/regression finetuning is in run_classifier.py, we also provide the shell scripts for each task including best hyperpameters.
Take an English task SST-2 and a Chinese task ChnSentCorp for example,
Step1: Download and unarchive the model in path ${MODEL_PATH}, if everything goes well, there should be a folder named params in $MODEL_PATH;
Step2: Download and unarchive the data set in ${TASK_DATA_PATH}, for English tasks, there should be 9 folders named CoLA , MNLI, MRPC, QNLI , QQP, RTE , SST-2, STS-B , WNLI; for Chinese tasks, there should be 6 folders named cmrc2018 drc, xnli, msra-ner, chnsentcorp, nlpcc-dbqa in ${TASK_DATA_PATH};
Step3: Follow the instructions below based on your own task type for starting your programs.
Take SST-2 as an example, the path of its training data set should be ${TASK_DATA_PATH}/SST-2/train.tsv, the data should have 2 fields with tsv format: text_a label, Here is some example datas:
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
...
Before runinng the scripts, we should set some environment variables
export TASK_DATA_PATH=(the value of ${TASK_DATA_PATH} mentioned above)
export MODEL_PATH=(the value of ${MODEL_PATH} mentioned above)
Run sh script/en_glue/ernie_large/SST-2/task.sh for finetuning，some logs will be shown below:
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
Similarly, for the Chinese task ChnSentCorp, after setting the environment variables, runsh script/zh_task/ernie_base/run_ChnSentiCorp.sh, some logs will be shown below:
[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
Take RTE as an example, the data should have 3 fields text_a text_b label with tsv format. Here is some example datas:
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
the path of its training data set should be ${TASK_DATA_PATH}/RTE/train.tsv
Before runinng the scripts, we should set some environment variables like before:
export TASK_DATA_PATH=(the value of ${TASK_DATA_PATH} mentioned above)
export MODEL_PATH=(the value of ${MODEL_PATH} mentioned above)
Run sh script/en_glue/ernie_large/RTE/task.sh for finetuning, some logs are shown below:
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
Take MSRA-NER(SIGHAN2006) as an example, the data should have 2 fields, text_a label, with tsv format. Here is some example datas :
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
Also, remember to set environmental variables like above, and run sh script/zh_task/ernie_base/run_msra_ner.sh for finetuning, some logs are shown below:
[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
Take DRCD as an example, convert the data into SQUAD format firstly:
{
"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"
}
]
}
Also, remember to set environmental variables like above, and run sh script/zh_task/ernie_base/run_drcd.sh for finetuning, some logs are shown below:
[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
We construct the training dataset based on Baidu Baike, Baidu Knows(Baidu Zhidao), Baidu Tieba for Chinese version ERNIE, and Wikipedia, Reddit, BookCorpus for English version ERNIE.
For the Chinese version dataset, we use a private version wordseg tool in Baidu to label those Chinese corpora in different granularities, such as character, word, entity, etc. Then using class CharTokenizer in tokenization.py for tokenization to get word boundaries. Finally, the words are mapped to ids according to the vocabulary config/vocab.txt . During training progress, we randomly mask words based on boundary information.
Here are some train instances after processing (which can be found in data/demo_train_set.gz and data/demo_valid_set.gz), each line corresponds to one training instance:
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
Each instance is composed of 5 fields, which are joined by ;in one line, represented token_ids; sentence_type_ids; position_ids; seg_labels; next_sentence_label respectively. Especially, in the fieldseg_labels, 0 means the begin of one word, 1 means non-begin of one word, -1 means placeholder, the other number means CLS or SEP.
The start entry for pretrain is script/zh_task/pretrain.sh. Before we run the train program, remember to set CUDA、cuDNN、NCCL2 etc. in the environment variable LD_LIBRARY_PATH.
Execute sh script/zh_task/pretrain.sh , the progress of pretrain will start with default parameters.
Here are some logs in the pretraining progress, including learning rate, epochs, steps, errors, training speed etc. The information will be printed according to the command parameter --validation_steps
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
ERNIE provide a toolkit for data distillation to further accelerate your ineference, see here for detail
Run ernie_encoder.py we can get the both sentence embedding and tokens embeddings. The input data format should be same as that mentioned in chapter Fine-tuning.
Here is an example to get sentence embedding and token embedding for LCQMC dev dataset:
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
when finished running this script, cls_emb.npy and top_layer_emb.npy will be generated for sentence embedding and token embedding respectively in folder test .
Take classification tasks for example, here is the script for batch prediction:
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 ${MODEL_PATH}/vocab.txt \
--num_labels 3
Argument init_checkpoint is the path of the model, predict_set is the path of test file, num_labels is the number of target labels.
Note: predict_set should be a tsv file with two fields named text_a、text_b(optional)
For one GPU card.
Export the path of cuda to LD_LIBRARY_PATH, e.g.: export LD_LIBRARY_PATH=/home/work/cudnn/cudnn_v[your cudnn version]/cuda/lib64
Download NCCL2, and export the library path to LD_LIBRARY_PATH, e.g.:export LD_LIBRARY_PATH=/home/work/nccl/lib
you can import propeller to your PYTHONPATH by export PYTHONPATH:./:$PYTHONPATH
`