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README.md

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+---
+language: zh
+datasets: CLUECorpusSmall
+widget: 
+- text: "这是很久之前的事情了"
+
+
+---
+
+
+# Chinese GPT2 Model
+
+## Model description
+
+The model is used to generate Chinese texts. You can download the model either from the [GPT2-Chinese Github page](https://github.com/Morizeyao/GPT2-Chinese), or via HuggingFace from the link [gpt2-chinese-cluecorpussmall](https://huggingface.co/uer/gpt2-chinese-cluecorpussmall).
+
+## How to use
+
+You can use the model directly with a pipeline for text generation:
+
+```python
+>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
+>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
+>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
+>>> text_generator = TextGenerationPipeline(model, tokenizer)   
+>>> text_generator("这是很久之前的事情了", max_length=100, do_sample=True)
+    [{'generated_text': '这是很久之前的事情了 ， 我 曾 经 把 这 个 当 做 一 种 思 想 的 传 承 ， 或 者 是 人 生 的 回 顾 ， 当 时 我 们 是 一 个 刚 刚 加 入 的 时 候 就 想 要 加 入 他 们 ， 于 是 我 们 每 天 看 到 他 们 ， 加 上 他 们 的 各 种 不 可 思 议 的 行 为 ， 直 到 现 在 ， 我 们 的 人 生 才 完 整 起 来 。'}]
+```
+
+## Training data
+
+[CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. 
+
+## Training procedure
+
+The model is 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 1024. 
+
+Stage1:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path cluecorpussmall_lm_seq128_dataset.pt \
+                      --seq_length 128 --processes_num 32 --data_processor lm 
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_lm_seq128_dataset.pt \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --config_path models/gpt2/config.json \
+                    --output_model_path models/cluecorpussmall_gpt2_seq128_model.bin \
+                    --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
+```
+
+Stage2:
+
+```
+python3 preprocess.py --corpus_path corpora/cluecorpussmall.txt \
+                      --vocab_path models/google_zh_vocab.txt \
+                      --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
+                      --seq_length 1024 --processes_num 32 --data_processor lm 
+```
+
+```
+python3 pretrain.py --dataset_path cluecorpussmall_lm_seq1024_dataset.pt \
+                    --vocab_path models/google_zh_vocab.txt \
+                    --pretrained_model_path models/cluecorpussmall_gpt2_seq128_model.bin-1000000 \
+                    --config_path models/gpt2/config.json \
+                    --output_model_path models/cluecorpussmall_gpt2_seq1024_model.bin \
+                    --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
+```
+
+Finally, we convert the pre-trained model into Huggingface's format:
+
+```
+python3 scripts/convert_gpt2_from_uer_to_huggingface.py --input_model_path cluecorpussmall_gpt2_seq1024_model.bin-250000 \
+                                                        --output_model_path pytorch_model.bin \
+                                                        --layers_num 12
+```
+
+### BibTeX entry and citation info
+
+```
+@article{radford2019language,
+  title={Language Models are Unsupervised Multitask Learners},
+  author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
+  year={2019}
+}
+
+@article{zhao2019uer,
+  title={UER: An Open-Source Toolkit for Pre-training Models},
+  author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong},
+  journal={EMNLP-IJCNLP 2019},
+  pages={241},
+  year={2019}
+}
+```

config.json

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+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "embd_pdrop": 0.1,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "output_past": true,
+  "resid_pdrop": 0.1,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 320
+    }
+  },
+  "tokenizer_class": "BertTokenizer",
+  "vocab_size": 21128
+}

flax_model.msgpack

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pytorch_model.bin

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special_tokens_map.json

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tf_model.h5

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tokenizer_config.json

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+{"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 1024}