Update README.md

README.md

@@ -17,9 +17,9 @@ tags:
 
 ## 简介 Brief Introduction
 
-将自然语言理解任务转化为multiple choice任务，并且使用 48 个 NLU 任务进行预训练
+UniMC 核心思想是将自然语言理解任务转化为 multiple choice 任务，并且使用多个 NLU 任务来进行预训练。我们在英文数据集实验结果表明仅含有 2.35 亿参数的 [ALBERT模型](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English)的zero-shot性能可以超越众多千亿的模型。并在中文测评基准 FewCLUE 和 ZeroCLUE 两个榜单中，13亿的[二郎神](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese)获得了第一的成绩。
 
-Convert natural language understanding tasks into multiple choice tasks, and use 48 NLU task for pre-training
+The core idea of UniMC is to convert natural language understanding tasks into multiple choice tasks and use multiple NLU tasks for pre-training. Our experimental results on the English dataset show that the zero-shot performance of a [ALBERT](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English) model with only 235 million parameters can surpass that of many hundreds of billions of models. And in the Chinese evaluation benchmarks FewCLUE and ZeroCLUE two lists, 1.3 billion [Erlangshen](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) won the first result.
 
 ## 模型分类 Model Taxonomy
 
@@ -33,19 +33,19 @@ Convert natural language understanding tasks into multiple choice tasks, and use
 
 We propose an new paradigm for zero-shot learners that is input-agnostic, in the sense that  it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense  reasoning, coreference resolution, sentiment analysis.
 Our approach converts zero-shot learning into multiple choice tasks, 
-avoiding problems in commonly used large generative models such as FLAN. It not only adds generalization ability to the models, but also reduces the needs of parameters significantly. We demonstrate that this approach leads to state-of-the-art performance on common language benchmarks, and produces satisfactory results on tasks such as natural language inference and text classification.  For more details, please refer to our [paper](https://arxiv.org/abs/2210.08590) or [github](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/)
-
+avoiding problems in commonly used large generative models such as FLAN. It not only adds generalization ability to the models, but also reduces the needs of parameters significantly. We demonstrate that this approach leads to state-of-the-art performance on common language benchmarks, and produces satisfactory results on tasks such as natural language inference and text classification. For more details, please refer to our [paper](https://arxiv.org/abs/2210.08590) or [github](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/)
 
 ### 下游效果 Performance
 
+
 **Few-shot**
 | Model      | eprstmt    | csldcp   | tnews     | iflytek  | ocnli     | bustm     | chid      | csl      | wsc       | Avg       |
 |------------|------------|----------|-----------|----------|-----------|-----------|-----------|----------|-----------|-----------|
-| Finetuning | 65.4       | 35.5     | 49        | 32.8     | 33        | 60.7      | 14.9      | 50       | 55.6      | 44.1      |
-| PET        | 86.7       | 51.7     | 54.5      | 46       | 44        | 56        | 61.2      | 59.4     | 57.5      | 57.44     |
-| LM-BFF     | 85.6       | 54.4     | 53        | 47.1     | 41.6      | 57.6      | 61.2      | 51.7     | 54.7      | 56.32     |
-| P-tuning   | 88.3       | 56       | 54.2      | **57.6** | 41.9      | 60.9      | 59.3      | **62.9** | 58.1      | 59.91     |
-| EFL        | 84.9       | 45       | 52.1      | 42.7     | 66.2      | 71.8      | 30.9      | 56.6     | 53        | 55.91     |
+| [FineTuning](https://arxiv.org/pdf/2107.07498.pdf)-RoBERTa-110M  | 65.4       | 35.5     | 49        | 32.8     | 33        | 60.7      | 14.9      | 50       | 55.6      | 44.1      |
+| [FineTuning](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M | 66.5 | 57   | 516  | 42.1 | 32   | 60.4 | 15    | 60.1 | 50.3 | 48.34 |
+| [PET](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M        | 84   | 59.9 | 56.4 | 50.3 | 38.1 | 58.4 | 40.6  | 61.1 | 58.7 | 56.39 |
+| [P-tuning](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M   | 80.6 | 56.6 | 55.9 | 52.6 | 35.7 | 60.8 | 39.61 | 51.8 | 55.7 | 54.37 |
+| [EFL](https://arxiv.org/pdf/2107.07498.pdf)-ERNIE1.0-110M        | 76.7 | 47.9 | 56.3 | 52.1 | 48.7 | 54.6 | 30.3  | 52.8 | 52.3 | 52.7  |
 | [UniMC-RoBERTa-110M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese) | 88.64      | 54.08    | 54.32     | 48.6     | 66.55     | 73.76     | 67.71     | 52.54    | 59.92     | 62.86     |
 | [UniMC-RoBERTa-330M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese) | 89.53      | 57.3     | 54.25     | 50       | 70.59     | 77.49     | 78.09     | 55.73    | 65.16     | 66.46     |
 | [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese) | **89.278** | **60.9** | **57.46** | 52.89    | **76.33** | **80.37** | **90.33** | 61.73    | **79.15** | **72.05** |
@@ -54,18 +54,17 @@ avoiding problems in commonly used large generative models such as FLAN. It not
 
 | Model         | eprstmt   | csldcp    | tnews     | iflytek   | ocnli     | bustm    | chid     | csl      | wsc       | Avg       |
 |---------------|-----------|-----------|-----------|-----------|-----------|----------|----------|----------|-----------|-----------|
-| GPT-zero      | 57.5      | 26.2      | 37        | 19        | 34.4      | 50       | 65.6     | 50.1     | 50.3      | 43.4      |
-| PET-zero      | 85.2      | 12.6      | 26.1      | 26.6      | 40.3      | 50.6     | 57.6     | 52.2     | 54.7      | 45.1      |
-| NSP-BERT      | 86.9      | 47.6      | 51        | 41.6      | 37.4      | 63.4     | 52       | **64.4** | 59.4      | 55.96     |
-| ZeroPrompt    | -         | -         | -         | 16.14     | 46.16     | -        | -        | -        | 47.98     | -         |
-|  Yuan1.0-13B  | 88.13     | 38.99     | 57.47     | 38.82     | 48.13     | 59.38    | 86.14    | 50       | 38.99     | 56.22     |
-| ERNIE3.0-240B | 88.75     | **50.97** | **57.83** | **40.42** | 53.57     | 64.38    | 87.13    | 56.25    | 53.46     | 61.41     |
+| [GPT](https://arxiv.org/pdf/2107.07498.pdf)-110M      | 57.5      | 26.2      | 37        | 19        | 34.4      | 50       | 65.6     | 50.1     | 50.3      | 43.4      |
+| [PET](https://arxiv.org/pdf/2107.07498.pdf)-RoBERTa-110M      | 85.2      | 12.6      | 26.1      | 26.6      | 40.3      | 50.6     | 57.6     | 52.2     | 54.7      | 45.1      |
+| [NSP-BERT](https://arxiv.org/abs/2109.03564)-110M      | 86.9      | 47.6      | 51        | 41.6      | 37.4      | 63.4     | 52       | **64.4** | 59.4      | 55.96     |
+| [ZeroPrompt](https://arxiv.org/abs/2201.06910)-T5-1.5B    | -         | -         | -         | 16.14     | 46.16     | -        | -        | -        | 47.98     | -         |
+|  [Yuan1.0-13B](https://arxiv.org/abs/2110.04725)  | 88.13     | 38.99     | 57.47     | 38.82     | 48.13     | 59.38    | 86.14    | 50       | 38.99     | 56.22     |
+| [ERNIE3.0-240B](https://arxiv.org/abs/2107.02137) | 88.75     | **50.97** | **57.83** | **40.42** | 53.57     | 64.38    | 87.13    | 56.25    | 53.46     | 61.41     |
 | [UniMC-RoBERTa-110M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-110M-Chinese)    | 86.16     | 31.26     | 46.61     | 26.54     | 66.91     | 73.34    | 66.68    | 50.09    | 53.66     | 55.7      |
 | [UniMC-RoBERTa-330M](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese)     | 87.5      | 30.4      | 47.6      | 31.5      | 69.9      | 75.9     | 78.17    | 49.5     | 60.55     | 59.01     |
 | [UniMC-MegatronBERT-1.3B](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-MegatronBERT-1.3B-Chinese)     | **88.79** | 42.06     | 55.21     | 33.93     | **75.57** | **79.5** | **89.4** | 50.25    | **66.67** | **64.53** |
 
 
-
 ## 使用 Usage
 ```shell
 git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
@@ -76,11 +75,11 @@ pip install --editable .
 
 ```python3
 import argparse
-from fengshen.pipelines.multiplechoice import UniMCPiplines
+from fengshen.pipelines.multiplechoice import UniMCPipelines
 
 
 total_parser = argparse.ArgumentParser("TASK NAME")
-total_parser = UniMCPiplines.piplines_args(total_parser)
+total_parser = UniMCPipelines.piplines_args(total_parser)
 args = total_parser.parse_args()
 args.pretrained_model_path = 'IDEA-CCNL/Erlangshen-UniMC-RoBERTa-330M-Chinese'
 args.learning_rate=2e-5
@@ -88,7 +87,7 @@ args.max_length=512
 args.max_epochs=3
 args.batchsize=8
 args.default_root_dir='./'
-model = UniMCPiplines(args)
+model = UniMCPipelines(args)
 
 train_data = []
 dev_data = []