Update README.md

README.md

@@ -7,41 +7,15 @@ widget:
 
 
 ---
-# Chinese Whole Word Masking RoBERTa Miniatures
+# Chinese Xlarge Whole Word Masking RoBERTa Model
 
 ## Model description
 
-This is the set of 6 Chinese Whole Word Masking RoBERTa models pre-trained by [UER-py](https://github.com/dbiir/UER-py/), which is introduced in [this paper](https://arxiv.org/abs/1909.05658). Besides, the models could also be pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits UER-py to support models with parameters above one billion, and extends it to a multimodal pre-training framework.
+This is an xlarge Chinese Whole Word Masking RoBERTa model pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) introduced in [this paper](https://arxiv.org/abs/2212.06385), which inherits [UER-py](https://github.com/dbiir/UER-py/) to support models with parameters above one billion, and extends it to a multimodal pre-training framework.
 
-[Turc et al.](https://arxiv.org/abs/1908.08962) have shown that the standard BERT recipe is effective on a wide range of model sizes. Following their paper, we released the 6 Chinese Whole Word Masking RoBERTa models. In order to facilitate users in reproducing the results, we used a publicly available corpus and word segmentation tool, and provided all training details.
+[Turc et al.](https://arxiv.org/abs/1908.08962) have shown that the standard BERT recipe is effective on a wide range of model sizes. Following their paper, we released the xlarge Chinese Whole Word Masking RoBERTa model. In order to facilitate users in reproducing the results, we used a publicly available corpus and word segmentation tool, and provided all training details.
 
-You can download the 6 Chinese RoBERTa miniatures either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the links below:
-
-|          |           Link           |
-| -------- | :-----------------------: |
-| **Tiny**  | [**2/128 (Tiny)**][2_128] |
-| **Mini**  | [**4/256 (Mini)**][4_256] |
-| **Small**  | [**4/512 (Small)**][4_512] |
-| **Medium**  | [**8/512 (Medium)**][8_512] |
-| **Base** | [**12/768 (Base)**][12_768] |
-| **Large** | [**24/1024 (Large)**][24_1024] |
-
-Here are scores on the devlopment set of six Chinese tasks:
-
-| Model              | Score | book_review | chnsenticorp | lcqmc | tnews(CLUE) | iflytek(CLUE) | ocnli(CLUE) |
-| ------------------ | :---: | :----: | :----------: | :---: | :---------: | :-----------: | :---------: |
-| RoBERTa-Tiny-WWM   | 72.2  |  83.7  |     91.8     | 81.8  |    62.1     |     55.4      |    58.6     |
-| RoBERTa-Mini-WWM   | 76.3  |  86.4  |     93.0     | 86.8  |    64.4     |     58.7      |    68.8     |
-| RoBERTa-Small-WWM  | 77.6  |  88.1  |     93.8     | 87.2  |    65.2     |     59.6      |    71.4     |
-| RoBERTa-Medium-WWM | 78.6  |  89.3  |     94.4     | 88.8  |    66.0     |     59.9      |    73.2     |
-| RoBERTa-Base-WWM   | 80.2  |  90.6  |     95.8     | 89.4  |    67.5     |     61.8      |    76.2     |
-| RoBERTa-Large-WWM  | 81.1  |  91.1  |     95.8     | 90.0  |    68.5     |     62.1      |    79.1     |
-
-For each task, we selected the best fine-tuning hyperparameters from the lists below, and trained with the sequence length of 128:
-
-- epochs: 3, 5, 8
-- batch sizes: 32, 64
-- learning rates: 3e-5, 1e-4, 3e-4
+You can download the model either from the [UER-py Modelzoo page](https://github.com/dbiir/UER-py/wiki/Modelzoo), or via HuggingFace from the link [roberta-xlarge-wwm-chinese-cluecorpussmall](https://huggingface.co/uer/roberta-xlarge-wwm-chinese-cluecorpussmall):
 
 ## How to use
 
@@ -49,41 +23,40 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```python
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='uer/roberta-tiny-wwm-chinese-cluecorpussmall')
+>>> unmasker = pipeline('fill-mask', model='uer/roberta-xlarge-wwm-chinese-cluecorpussmall')
 >>> unmasker("北京是[MASK]国的首都。")
 [
-    {'score': 0.294228732585907, 
-     'token': 704, 
-     'token_str': '中', 
-     'sequence': '北 京 是 中 国 的 首 都 。'},
-    {'score': 0.19691626727581024, 
-     'token': 1266, 
-     'token_str': '北', 
-     'sequence': '北 京 是 北 国 的 首 都 。'},
-    {'score': 0.1070084273815155, 
-     'token': 7506, 
-     'token_str': '韩', 
-     'sequence': '北 京 是 韩 国 的 首 都 。'},
-    {'score': 0.031527262181043625, 
-     'token': 2769, 
-     'token_str': '我', 
-     'sequence': '北 京 是 我 国 的 首 都 。'},
-    {'score': 0.023054633289575577, 
-     'token': 1298, 
-     'token_str': '南', 
-     'sequence': '北 京 是 南 国 的 首 都 。'}
+  {'score': 0.9298505783081055,
+   'token': 704,
+   'token_str': '中',
+   'sequence': '北 京 是 中 国 的 首 都 。'},
+  {'score': 0.05041525512933731,
+   'token': 2769,
+   'token_str': '我',
+   'sequence': '北 京 是 我 国 的 首 都 。'},
+  {'score': 0.004921116400510073,
+   'token': 4862,
+   'token_str': '祖',
+   'sequence': '北 京 是 祖 国 的 首 都 。'},
+  {'score': 0.0020684923510998487,
+   'token': 3696,
+   'token_str': '民',
+   'sequence': '北 京 是 民 国 的 首 都 。'},
+  {'score': 0.0018144999630749226,
+   'token': 3926,
+   'token_str': '清',
+   'sequence': '北 京 是 清 国 的 首 都 。'}
 ]
     
 
-
 ```
 
 Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
 from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('uer/roberta-base-wwm-chinese-cluecorpussmall')
-model = BertModel.from_pretrained("uer/roberta-base-wwm-chinese-cluecorpussmall")
+tokenizer = BertTokenizer.from_pretrained('uer/roberta-xlarge-wwm-chinese-cluecorpussmall')
+model = BertModel.from_pretrained("uer/roberta-xlarge-wwm-chinese-cluecorpussmall")
 text = "用你喜欢的任何文本替换我。"
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -93,8 +66,8 @@ and in TensorFlow:
 
 ```python
 from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('uer/roberta-base-wwm-chinese-cluecorpussmall')
-model = TFBertModel.from_pretrained("uer/roberta-base-wwm-chinese-cluecorpussmall")
+tokenizer = BertTokenizer.from_pretrained('uer/roberta-xlarge-wwm-chinese-cluecorpussmall')
+model = TFBertModel.from_pretrained("uer/roberta-xlarge-wwm-chinese-cluecorpussmall")
 text = "用你喜欢的任何文本替换我。"
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)
@@ -106,12 +79,10 @@ output = model(encoded_input)
 
 ## Training procedure
 
-Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. We use the same hyper-parameters on different model sizes.
+Models are pre-trained by [TencentPretrain](https://github.com/Tencent/TencentPretrain) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 500,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. 
 
 [jieba](https://github.com/fxsjy/jieba) is used as word segmentation tool.
 
-Taking the case of Whole Word Masking RoBERTa-Medium
-
 Stage1:
 
 ```
@@ -123,17 +94,24 @@ python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
 ```
 
 ```
-python3 pretrain.py --dataset_path cluecorpussmall_word_seq128_dataset.pt \
+deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json --dataset_path cluecorpussmall_seq128_dataset.pt \
                     --vocab_path models/google_zh_vocab.txt \
-                    --config_path models/bert/medium_config.json \
-                    --output_model_path models/cluecorpussmall_wwm_roberta_medium_seq128_model.bin \
+                    --config_path models/bert/xlarge_config.json \
+                    --output_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq128_model \
                     --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
-                    --total_steps 1000000 --save_checkpoint_steps 100000 --report_steps 50000 \
-                    --learning_rate 1e-4 --batch_size 64 \
-                    --whole_word_masking \
+                    --total_steps 500000 --save_checkpoint_steps 50000 --report_steps 500 \
+                    --learning_rate 2e-5 --batch_size 128 --deep_init \
+                    --whole_word_masking  --deepspeed_checkpoint_activations \
                     --data_processor mlm --target mlm
 ```
 
+Before stage2, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
+
+```
+python3 models/cluecorpussmall_wwm_roberta_xlarge_seq128_model/zero_to_fp32.py models/cluecorpussmall_wwm_roberta_xlarge_seq128_model/ \
+                                                                        models/cluecorpussmall_wwm_roberta_xlarge_seq128_model.bin
+```
+
 Stage2:
 
 ```
@@ -145,24 +123,31 @@ python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
 ```
 
 ```
-python3 pretrain.py --dataset_path cluecorpussmall_seq512_dataset.pt \
+deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json --dataset_path cluecorpussmall_seq512_dataset.pt \
                     --vocab_path models/google_zh_vocab.txt \
-                    --pretrained_model_path models/cluecorpussmall_wwm_roberta_medium_seq128_model.bin-1000000 \
-                    --config_path models/bert/medium_config.json \
-                    --output_model_path models/cluecorpussmall_wwm_roberta_medium_seq512_model.bin \
+                    --config_path models/bert/xlarge_config.json \
+                    --pretrained_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq128_model.bin \
+                    --output_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq512_model \
                     --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \
-                    --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 10000 \
-                    --learning_rate 5e-5 --batch_size 16 \
-                    --whole_word_masking \
+                    --total_steps 250000 --save_checkpoint_steps 50000 --report_steps 500 \
+                    --learning_rate 5e-5 --batch_size 32 \
+                    --whole_word_masking --deepspeed_checkpoint_activations \
                     --data_processor mlm --target mlm
 ```
 
+Then, we extract fp32 consolidated weights from a zero 2 and 3 DeepSpeed checkpoints:
+
+```
+python3 models/cluecorpussmall_wwm_roberta_xlarge_seq512_model/zero_to_fp32.py models/cluecorpussmall_wwm_roberta_xlarge_seq512_model/ \
+                                                                        models/cluecorpussmall_wwm_roberta_xlarge_seq512_model.bin
+```
+
 Finally, we convert the pre-trained model into Huggingface's format:
 
 ```
-python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path models/cluecorpussmall_wwm_roberta_medium_seq512_model.bin \
+python3 scripts/convert_bert_from_tencentpretrain_to_huggingface.py --input_model_path models/cluecorpussmall_wwm_roberta_xlarge_seq512_model.bin \
                                                         --output_model_path pytorch_model.bin \
-                                                        --layers_num 8 --type mlm
+                                                        --layers_num 36 --type mlm
 ```
 
 ### BibTeX entry and citation info
@@ -182,11 +167,5 @@ python3 scripts/convert_bert_from_uer_to_huggingface.py --input_model_path model
   journal={ACL 2023},
   pages={217},
   year={2023}
+}
 ```
-
-[2_128]:https://huggingface.co/uer/roberta-tiny-wwm-chinese-cluecorpussmall
-[4_256]:https://huggingface.co/uer/roberta-mini-wwm-chinese-cluecorpussmall
-[4_512]:https://huggingface.co/uer/roberta-small-wwm-chinese-cluecorpussmall
-[8_512]:https://huggingface.co/uer/roberta-medium-wwm-chinese-cluecorpussmall
-[12_768]:https://huggingface.co/uer/roberta-base-wwm-chinese-cluecorpussmall
-[24_1024]:https://huggingface.co/uer/roberta-large-wwm-chinese-cluecorpussmall