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wangnan39@huawei.com
7188a14215
|
4 years ago | |
|---|---|---|
| .. | ||
| scripts | 4 years ago | |
| src | 4 years ago | |
| README.md | 4 years ago | |
| README_CN.md | 4 years ago | |
| export.py | 4 years ago | |
| mindspore_hub_conf.py | 5 years ago | |
| pretrain_eval.py | 5 years ago | |
| run_classifier.py | 5 years ago | |
| run_ner.py | 4 years ago | |
| run_pretrain.py | 4 years ago | |
| run_squad.py | 5 years ago | |
README.md
Contents
- Contents
- BERT Description
- Model Architecture
- Dataset
- Environment Requirements
- Quick Start
- Script Description
- Description of Random Situation
- ModelZoo Homepage
BERT Description
The BERT network was proposed by Google in 2018. The network has made a breakthrough in the field of NLP. The network uses pre-training to achieve a large network structure without modifying, and only by adding an output layer to achieve multiple text-based tasks in fine-tuning. The backbone code of BERT adopts the Encoder structure of Transformer. The attention mechanism is introduced to enable the output layer to capture high-latitude global semantic information. The pre-training uses denoising and self-encoding tasks, namely MLM(Masked Language Model) and NSP(Next Sentence Prediction). No need to label data, pre-training can be performed on massive text data, and only a small amount of data to fine-tuning downstream tasks to obtain good results. The pre-training plus fune-tuning mode created by BERT is widely adopted by subsequent NLP networks.
Paper: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Paper: Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen, Qun Liu. NEZHA: Neural Contextualized Representation for Chinese Language Understanding. arXiv preprint arXiv:1909.00204.
Model Architecture
The backbone structure of BERT is transformer. For BERT_base, the transformer contains 12 encoder modules, each module contains one self-attention module and each self-attention module contains one attention module. For BERT_NEZHA, the transformer contains 24 encoder modules, each module contains one self-attention module and each self-attention module contains one attention module. The difference between BERT_base and BERT_NEZHA is that BERT_base uses absolute position encoding to produce position embedding vector and BERT_NEZHA uses relative position encoding.
Dataset
- Download the zhwiki or enwiki dataset for pre-training. Extract and refine texts in the dataset with WikiExtractor. Convert the dataset to TFRecord format. Please refer to create_pretraining_data.py file in BERT repository.
- Download dataset for fine-tuning and evaluation such as CLUENER, TNEWS, SQuAD v1.1, etc. Convert dataset files from JSON format to TFRECORD format, please refer to run_classifier.py file in BERT repository.
Environment Requirements
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend/GPU processor. If you want to try Ascend, please send the application form to ascend@huawei.com. Once approved, you can get access to the resources.
- Framework
- For more information, please check the resources below：
Quick Start
After installing MindSpore via the official website, you can start pre-training, fine-tuning and evaluation as follows:
- Running on Ascend
# run standalone pre-training example
bash scripts/run_standalone_pretrain_ascend.sh 0 1 /path/cn-wiki-128
# run distributed pre-training example
bash scripts/run_distributed_pretrain_ascend.sh /path/cn-wiki-128 /path/hccl.json
# run fine-tuning and evaluation example
- If you are going to run a fine-tuning task, please prepare a checkpoint generated from pre-training.
- Set bert network config and optimizer hyperparameters in `finetune_eval_config.py`.
- Classification task: Set task related hyperparameters in scripts/run_classifier.sh.
- Run `bash scripts/run_classifier.py` for fine-tuning of BERT-base and BERT-NEZHA model.
bash scripts/run_classifier.sh
- NER task: Set task related hyperparameters in scripts/run_ner.sh.
- Run `bash scripts/run_ner.py` for fine-tuning of BERT-base and BERT-NEZHA model.
bash scripts/run_ner.sh
- SQuAD task: Set task related hyperparameters in scripts/run_squad.sh.
- Run `bash scripts/run_squad.py` for fine-tuning of BERT-base and BERT-NEZHA model.
bash scripts/run_squad.sh
- Running on GPU
# run standalone pre-training example
bash run_standalone_pretrain_for_gpu.sh 0 1 /path/cn-wiki-128
# run distributed pre-training example
bash scripts/run_distributed_pretrain_for_gpu.sh 8 40 /path/cn-wiki-128
# run fine-tuning and evaluation example
- If you are going to run a fine-tuning task, please prepare a checkpoint generated from pre-training.
- Set bert network config and optimizer hyperparameters in `finetune_eval_config.py`.
- Classification task: Set task related hyperparameters in scripts/run_classifier.sh.
- Run `bash scripts/run_classifier.py` for fine-tuning of BERT-base and BERT-NEZHA model.
bash scripts/run_classifier.sh
- NER task: Set task related hyperparameters in scripts/run_ner.sh.
- Run `bash scripts/run_ner.py` for fine-tuning of BERT-base and BERT-NEZHA model.
bash scripts/run_ner.sh
- SQuAD task: Set task related hyperparameters in scripts/run_squad.sh.
- Run `bash scripts/run_squad.py` for fine-tuning of BERT-base and BERT-NEZHA model.
bash scripts/run_squad.sh
For distributed training on Ascend, an hccl configuration file with JSON format needs to be created in advance.
For distributed training on single machine, here is an example hccl.json.
For distributed training among multiple machines, training command should be executed on each machine in a small time interval. Thus, an hccl.json is needed on each machine. here is an example of hccl.json for multi-machine case.
Please follow the instructions in the link below to create an hccl.json file in need:
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
For dataset, if you want to set the format and parameters, a schema configuration file with JSON format needs to be created, please refer to tfrecord format.
For pretraining, schema file contains ["input_ids", "input_mask", "segment_ids", "next_sentence_labels", "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"].
For ner or classification task, schema file contains ["input_ids", "input_mask", "segment_ids", "label_ids"].
For squad task, training: schema file contains ["start_positions", "end_positions", "input_ids", "input_mask", "segment_ids"], evaluation: schema file contains ["input_ids", "input_mask", "segment_ids"].
`numRows` is the only option which could be set by user, other values must be set according to the dataset.
For example, the schema file of cn-wiki-128 dataset for pretraining shows as follows:
{
"datasetType": "TF",
"numRows": 7680,
"columns": {
"input_ids": {
"type": "int64",
"rank": 1,
"shape": [128]
},
"input_mask": {
"type": "int64",
"rank": 1,
"shape": [128]
},
"segment_ids": {
"type": "int64",
"rank": 1,
"shape": [128]
},
"next_sentence_labels": {
"type": "int64",
"rank": 1,
"shape": [1]
},
"masked_lm_positions": {
"type": "int64",
"rank": 1,
"shape": [20]
},
"masked_lm_ids": {
"type": "int64",
"rank": 1,
"shape": [20]
},
"masked_lm_weights": {
"type": "float32",
"rank": 1,
"shape": [20]
}
}
}
Script Description
Script and Sample Code
.
└─bert
├─README.md
├─scripts
├─ascend_distributed_launcher
├─__init__.py
├─hyper_parameter_config.ini # hyper parameter for distributed pretraining
├─get_distribute_pretrain_cmd.py # script for distributed pretraining
├─README.md
├─run_classifier.sh # shell script for standalone classifier task on ascend or gpu
├─run_ner.sh # shell script for standalone NER task on ascend or gpu
├─run_squad.sh # shell script for standalone SQUAD task on ascend or gpu
├─run_standalone_pretrain_ascend.sh # shell script for standalone pretrain on ascend
├─run_distributed_pretrain_ascend.sh # shell script for distributed pretrain on ascend
├─run_distributed_pretrain_gpu.sh # shell script for distributed pretrain on gpu
└─run_standaloned_pretrain_gpu.sh # shell script for distributed pretrain on gpu
├─src
├─__init__.py
├─assessment_method.py # assessment method for evaluation
├─bert_for_finetune.py # backbone code of network
├─bert_for_pre_training.py # backbone code of network
├─bert_model.py # backbone code of network
├─finetune_data_preprocess.py # data preprocessing
├─cluner_evaluation.py # evaluation for cluner
├─config.py # parameter configuration for pretraining
├─CRF.py # assessment method for clue dataset
├─dataset.py # data preprocessing
├─finetune_eval_config.py # parameter configuration for finetuning
├─finetune_eval_model.py # backbone code of network
├─sample_process.py # sample processing
├─utils.py # util function
├─pretrain_eval.py # train and eval net
├─run_classifier.py # finetune and eval net for classifier task
├─run_ner.py # finetune and eval net for ner task
├─run_pretrain.py # train net for pretraining phase
└─run_squad.py # finetune and eval net for squad task
Script Parameters
Pre-Training
usage: run_pretrain.py [--distribute DISTRIBUTE] [--epoch_size N] [----device_num N] [--device_id N]
[--enable_save_ckpt ENABLE_SAVE_CKPT] [--device_target DEVICE_TARGET]
[--enable_lossscale ENABLE_LOSSSCALE] [--do_shuffle DO_SHUFFLE]
[--enable_data_sink ENABLE_DATA_SINK] [--data_sink_steps N]
[--accumulation_steps N]
[--allreduce_post_accumulation ALLREDUCE_POST_ACCUMULATION]
[--save_checkpoint_path SAVE_CHECKPOINT_PATH]
[--load_checkpoint_path LOAD_CHECKPOINT_PATH]
[--save_checkpoint_steps N] [--save_checkpoint_num N]
[--data_dir DATA_DIR] [--schema_dir SCHEMA_DIR] [train_steps N]
options:
--device_target device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
--distribute pre_training by several devices: "true"(training by more than 1 device) | "false", default is "false"
--epoch_size epoch size: N, default is 1
--device_num number of used devices: N, default is 1
--device_id device id: N, default is 0
--enable_save_ckpt enable save checkpoint: "true" | "false", default is "true"
--enable_lossscale enable lossscale: "true" | "false", default is "true"
--do_shuffle enable shuffle: "true" | "false", default is "true"
--enable_data_sink enable data sink: "true" | "false", default is "true"
--data_sink_steps set data sink steps: N, default is 1
--accumulation_steps accumulate gradients N times before weight update: N, default is 1
--allreduce_post_accumulation allreduce after accumulation of N steps or after each step: "true" | "false", default is "true"
--save_checkpoint_path path to save checkpoint files: PATH, default is ""
--load_checkpoint_path path to load checkpoint files: PATH, default is ""
--save_checkpoint_steps steps for saving checkpoint files: N, default is 1000
--save_checkpoint_num number for saving checkpoint files: N, default is 1
--train_steps Training Steps: N, default is -1
--data_dir path to dataset directory: PATH, default is ""
--schema_dir path to schema.json file, PATH, default is ""
Fine-Tuning and Evaluation
usage: run_ner.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
[--assessment_method ASSESSMENT_METHOD] [--use_crf USE_CRF]
[--device_id N] [--epoch_num N] [--vocab_file_path VOCAB_FILE_PATH]
[--label2id_file_path LABEL2ID_FILE_PATH]
[--train_data_shuffle TRAIN_DATA_SHUFFLE]
[--eval_data_shuffle EVAL_DATA_SHUFFLE]
[--save_finetune_checkpoint_path SAVE_FINETUNE_CHECKPOINT_PATH]
[--load_pretrain_checkpoint_path LOAD_PRETRAIN_CHECKPOINT_PATH]
[--train_data_file_path TRAIN_DATA_FILE_PATH]
[--eval_data_file_path EVAL_DATA_FILE_PATH]
[--schema_file_path SCHEMA_FILE_PATH]
options:
--device_target device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
--do_train whether to run training on training set: true | false
--do_eval whether to run eval on dev set: true | false
--assessment_method assessment method to do evaluation: f1 | clue_benchmark
--use_crf whether to use crf to calculate loss: true | false
--device_id device id to run task
--epoch_num total number of training epochs to perform
--train_data_shuffle Enable train data shuffle, default is true
--eval_data_shuffle Enable eval data shuffle, default is true
--vocab_file_path the vocabulary file that the BERT model was trained on
--label2id_file_path label to id file, each label name must be consistent with the type name labeled in the original dataset file
--save_finetune_checkpoint_path path to save generated finetuning checkpoint
--load_pretrain_checkpoint_path initial checkpoint (usually from a pre-trained BERT model)
--load_finetune_checkpoint_path give a finetuning checkpoint path if only do eval
--train_data_file_path ner tfrecord for training. E.g., train.tfrecord
--eval_data_file_path ner tfrecord for predictions if f1 is used to evaluate result, ner json for predictions if clue_benchmark is used to evaluate result
--dataset_format dataset format, support mindrecord or tfrecord
--schema_file_path path to datafile schema file
usage: run_squad.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
[--device_id N] [--epoch_num N] [--num_class N]
[--vocab_file_path VOCAB_FILE_PATH]
[--eval_json_path EVAL_JSON_PATH]
[--train_data_shuffle TRAIN_DATA_SHUFFLE]
[--eval_data_shuffle EVAL_DATA_SHUFFLE]
[--save_finetune_checkpoint_path SAVE_FINETUNE_CHECKPOINT_PATH]
[--load_pretrain_checkpoint_path LOAD_PRETRAIN_CHECKPOINT_PATH]
[--load_finetune_checkpoint_path LOAD_FINETUNE_CHECKPOINT_PATH]
[--train_data_file_path TRAIN_DATA_FILE_PATH]
[--eval_data_file_path EVAL_DATA_FILE_PATH]
[--schema_file_path SCHEMA_FILE_PATH]
options:
--device_target device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
--do_train whether to run training on training set: true | false
--do_eval whether to run eval on dev set: true | false
--device_id device id to run task
--epoch_num total number of training epochs to perform
--num_class number of classes to classify, usually 2 for squad task
--train_data_shuffle Enable train data shuffle, default is true
--eval_data_shuffle Enable eval data shuffle, default is true
--vocab_file_path the vocabulary file that the BERT model was trained on
--eval_json_path path to squad dev json file
--save_finetune_checkpoint_path path to save generated finetuning checkpoint
--load_pretrain_checkpoint_path initial checkpoint (usually from a pre-trained BERT model)
--load_finetune_checkpoint_path give a finetuning checkpoint path if only do eval
--train_data_file_path squad tfrecord for training. E.g., train1.1.tfrecord
--eval_data_file_path squad tfrecord for predictions. E.g., dev1.1.tfrecord
--schema_file_path path to datafile schema file
usage: run_classifier.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
[--assessment_method ASSESSMENT_METHOD] [--device_id N] [--epoch_num N] [--num_class N]
[--save_finetune_checkpoint_path SAVE_FINETUNE_CHECKPOINT_PATH]
[--load_pretrain_checkpoint_path LOAD_PRETRAIN_CHECKPOINT_PATH]
[--load_finetune_checkpoint_path LOAD_FINETUNE_CHECKPOINT_PATH]
[--train_data_shuffle TRAIN_DATA_SHUFFLE]
[--eval_data_shuffle EVAL_DATA_SHUFFLE]
[--train_data_file_path TRAIN_DATA_FILE_PATH]
[--eval_data_file_path EVAL_DATA_FILE_PATH]
[--schema_file_path SCHEMA_FILE_PATH]
options:
--device_target targeted device to run task: Ascend | GPU
--do_train whether to run training on training set: true | false
--do_eval whether to run eval on dev set: true | false
--assessment_method assessment method to do evaluation: accuracy | f1 | mcc | spearman_correlation
--device_id device id to run task
--epoch_num total number of training epochs to perform
--num_class number of classes to do labeling
--train_data_shuffle Enable train data shuffle, default is true
--eval_data_shuffle Enable eval data shuffle, default is true
--save_finetune_checkpoint_path path to save generated finetuning checkpoint
--load_pretrain_checkpoint_path initial checkpoint (usually from a pre-trained BERT model)
--load_finetune_checkpoint_path give a finetuning checkpoint path if only do eval
--train_data_file_path tfrecord for training. E.g., train.tfrecord
--eval_data_file_path tfrecord for predictions. E.g., dev.tfrecord
--schema_file_path path to datafile schema file
Options and Parameters
Parameters for training and evaluation can be set in file config.py and finetune_eval_config.py respectively.
Options
config for lossscale and etc.
bert_network version of BERT model: base | nezha, default is base
batch_size batch size of input dataset: N, default is 16
loss_scale_value initial value of loss scale: N, default is 2^32
scale_factor factor used to update loss scale: N, default is 2
scale_window steps for once updatation of loss scale: N, default is 1000
optimizer optimizer used in the network: AdamWerigtDecayDynamicLR | Lamb | Momentum, default is "Lamb"
Parameters
Parameters for dataset and network (Pre-Training/Fine-Tuning/Evaluation):
seq_length length of input sequence: N, default is 128
vocab_size size of each embedding vector: N, must be consistent with the dataset you use. Default is 21128.
Usually, we use 21128 for CN vocabs and 30522 for EN vocabs according to the origin paper.
hidden_size size of bert encoder layers: N, default is 768
num_hidden_layers number of hidden layers: N, default is 12
num_attention_heads number of attention heads: N, default is 12
intermediate_size size of intermediate layer: N, default is 3072
hidden_act activation function used: ACTIVATION, default is "gelu"
hidden_dropout_prob dropout probability for BertOutput: Q, default is 0.1
attention_probs_dropout_prob dropout probability for BertAttention: Q, default is 0.1
max_position_embeddings maximum length of sequences: N, default is 512
type_vocab_size size of token type vocab: N, default is 16
initializer_range initialization value of TruncatedNormal: Q, default is 0.02
use_relative_positions use relative positions or not: True | False, default is False
dtype data type of input: mstype.float16 | mstype.float32, default is mstype.float32
compute_type compute type in BertTransformer: mstype.float16 | mstype.float32, default is mstype.float16
Parameters for optimizer:
AdamWeightDecay:
decay_steps steps of the learning rate decay: N
learning_rate value of learning rate: Q
end_learning_rate value of end learning rate: Q, must be positive
power power: Q
warmup_steps steps of the learning rate warm up: N
weight_decay weight decay: Q
eps term added to the denominator to improve numerical stability: Q
Lamb:
decay_steps steps of the learning rate decay: N
learning_rate value of learning rate: Q
end_learning_rate value of end learning rate: Q
power power: Q
warmup_steps steps of the learning rate warm up: N
weight_decay weight decay: Q
Momentum:
learning_rate value of learning rate: Q
momentum momentum for the moving average: Q
Training Process
Training
Running on Ascend
bash scripts/run_standalone_pretrain_ascend.sh 0 1 /path/cn-wiki-128
The command above will run in the background, you can view training logs in pretraining_log.txt. After training finished, you will get some checkpoint files under the script folder by default. The loss values will be displayed as follows:
# grep "epoch" pretraining_log.txt
epoch: 0.0, current epoch percent: 0.000, step: 1, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0856101e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.000, step: 2, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0821701e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
running on GPU
bash scripts/run_standalone_pretrain_for_gpu.sh 0 1 /path/cn-wiki-128
The command above will run in the background, you can view the results the file pretraining_log.txt. After training, you will get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
# grep "epoch" pretraining_log.txt
epoch: 0.0, current epoch percent: 0.000, step: 1, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0856101e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.000, step: 2, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0821701e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
Attention If you are running with a huge dataset on Ascend, it's better to add an external environ variable to make sure the hccl won't timeout.
export HCCL_CONNECT_TIMEOUT=600This will extend the timeout limits of hccl from the default 120 seconds to 600 seconds.
Attention If you are running with a big bert model, some error of protobuf may occurs while saving checkpoints, try with the following environ set.export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
Distributed Training
Running on Ascend
bash scripts/run_distributed_pretrain_ascend.sh /path/cn-wiki-128 /path/hccl.json
The command above will run in the background, you can view training logs in pretraining_log.txt. After training finished, you will get some checkpoint files under the LOG* folder by default. The loss value will be displayed as follows:
# grep "epoch" LOG*/pretraining_log.txt
epoch: 0.0, current epoch percent: 0.001, step: 100, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.08209e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.07566e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
epoch: 0.0, current epoch percent: 0.001, step: 100, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.08218e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.07770e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
running on GPU
bash scripts/run_distributed_pretrain_for_gpu.sh /path/cn-wiki-128
The command above will run in the background, you can view the results the file pretraining_log.txt. After training, you will get some checkpoint files under the LOG* folder by default. The loss value will be achieved as follows:
# grep "epoch" LOG*/pretraining_log.txt
epoch: 0.0, current epoch percent: 0.001, step: 100, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.08209e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.07566e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
epoch: 0.0, current epoch percent: 0.001, step: 100, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.08218e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.07770e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
Attention This will bind the processor cores according to the
device_numand total processor numbers. If you don't expect to run pretraining with binding processor cores, remove the operations abouttasksetinscripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py
Evaluation Process
Evaluation
evaluation on cola dataset when running on Ascend
Before running the command below, please check the load pretrain checkpoint path has been set. Please set the checkpoint path to be the absolute full path, e.g:"/username/pretrain/checkpoint_100_300.ckpt".
bash scripts/run_classifier.sh
The command above will run in the background, you can view training logs in classfier_log.txt.
If you choose accuracy as assessment method, the result will be as follows:
acc_num XXX, total_num XXX, accuracy 0.588986
evaluation on cluener dataset when running on Ascend
bash scripts/run_ner.sh
The command above will run in the background, you can view training logs in ner_log.txt.
If you choose F1 as assessment method, the result will be as follows:
Precision 0.920507
Recall 0.948683
F1 0.920507
evaluation on msra dataset when running on Ascend
For preprocess, you can first convert the original txt format of MSRA dataset into mindrecord by run the command as below (please keep in mind that the label names in label2id_file should be consistent with the type names labeled in the original msra_dataset.xml dataset file):
python src/finetune_data_preprocess.py --data_dir=/path/msra_dataset.xml --vocab_file=/path/vacab_file --save_path=/path/msra_dataset.mindrecord --label2id=/path/label2id_file --max_seq_len=seq_len --class_filter="NAMEX" --split_begin=0.0 --split_end=1.0
For finetune and evaluation, just do
bash scripts/run_ner.sh
The command above will run in the background, you can view training logs in ner_log.txt.
If you choose MF1(F1 score with multi-labels) as assessment method, the result will be as follows if evaluation is done after finetuning 10 epoches:
F1 0.931243
evaluation on squad v1.1 dataset when running on Ascend
bash scripts/squad.sh
The command above will run in the background, you can view training logs in squad_log.txt.
The result will be as follows:
{"exact_match": 80.3878923040233284, "f1": 87.6902384023850329}
Model Description
Performance
Pretraining Performance
| Parameters | Ascend | GPU |
|---|---|---|
| Model Version | BERT_base | BERT_base |
| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-16G, cpu: Intel(R) Xeon(R) Platinum 8160 CPU @2.10GHz, memory: 256G |
| uploaded Date | 08/22/2020 | 05/06/2020 |
| MindSpore Version | 1.0.0 | 1.0.0 |
| Dataset | cn-wiki-128(4000w) | cn-wiki-128(4000w) |
| Training Parameters | src/config.py | src/config.py |
| Optimizer | Lamb | AdamWeightDecay |
| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
| outputs | probability | probability |
| Epoch | 40 | 40 |
| Batch_size | 256*8 | 32*8 |
| Loss | 1.7 | 1.7 |
| Speed | 340ms/step | 290ms/step |
| Total time | 73h | 610H |
| Params (M) | 110M | 110M |
| Checkpoint for Fine tuning | 1.2G(.ckpt file) | 1.2G(.ckpt file) |
| Scripts | BERT_base | BERT_base |
| Parameters | Ascend |
|---|---|
| Model Version | BERT_NEZHA |
| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G |
| uploaded Date | 08/20/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | cn-wiki-128(4000w) |
| Training Parameters | src/config.py |
| Optimizer | Lamb |
| Loss Function | SoftmaxCrossEntropy |
| outputs | probability |
| Epoch | 40 |
| Batch_size | 96*8 |
| Loss | 1.7 |
| Speed | 360ms/step |
| Total time | 200h |
| Params (M) | 340M |
| Checkpoint for Fine tuning | 3.2G(.ckpt file) |
| Scripts | BERT_NEZHA |
Inference Performance
| Parameters | Ascend |
|---|---|
| Model Version | |
| Resource | Ascend 910 |
| uploaded Date | 08/22/2020 |
| MindSpore Version | 1.0.0 |
| Dataset | cola, 1.2W |
| batch_size | 32(1P) |
| Accuracy | 0.588986 |
| Speed | 59.25ms/step |
| Total time | 15min |
| Model for inference | 1.2G(.ckpt file) |
Description of Random Situation
In run_standalone_pretrain.sh and run_distributed_pretrain.sh, we set do_shuffle to True to shuffle the dataset by default.
In run_classifier.sh, run_ner.sh and run_squad.sh, we set train_data_shuffle and eval_data_shuffle to True to shuffle the dataset by default.
In config.py, we set the hidden_dropout_prob and attention_pros_dropout_prob to 0.1 to dropout some network node by default.
In run_pretrain.py, we set a random seed to make sure that each node has the same initial weight in distribute training.
ModelZoo Homepage
Please check the official homepage.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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Contributors (25+)
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chenzomi12@gmail.com
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zhaojichen1@huawei.com