diff --git a/model_cards/uer/gpt2-chinese-couplet/README.md b/model_cards/uer/gpt2-chinese-couplet/README.md new file mode 100644 index 00000000000000..891d9e3b2c15da --- /dev/null +++ b/model_cards/uer/gpt2-chinese-couplet/README.md @@ -0,0 +1,85 @@ +--- +language: zh +widget: +- text: "[CLS]国 色 天 香 ， 姹 紫 嫣 红 ， 碧 水 青 云 欣 共 赏 -" + + +--- + +# Chinese Couplet GPT2 Model + +## Model description + +The model is used to generate Chinese couplets. 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-couplet][couplet]. + +Since the parameter skip_special_tokens is used in the pipelines.py, special tokens such as [SEP], [UNK] will be deleted, and the output results may not be neat. + +## How to use + +You can use the model directly with a pipeline for text generation: + +When the parameter skip_special_tokens is True: + +```python +>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline +>>> from transformers import TextGenerationPipeline, +>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-couplet") +>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-couplet") +>>> text_generator = TextGenerationPipeline(model, tokenizer) +>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True) + [{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 从 天 外 来 阅 旗'}] +``` + +When the parameter skip_special_tokens is False: + +```python +>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline +>>> from transformers import TextGenerationPipeline, +>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem") +>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem") +>>> text_generator = TextGenerationPipeline(model, tokenizer) +>>> text_generator("[CLS]丹 枫 江 冷 人 初 去 -", max_length=25, do_sample=True) + [{'generated_text': '[CLS]丹 枫 江 冷 人 初 去 - 黄 叶 声 我 酒 不 辞 [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP] [SEP]'}] +``` + +## Training data + +Contains 700,000 Chinese couplets collected by [couplet-clean-dataset](https://github.com/v-zich/couplet-clean-dataset). + +## Training procedure + +Models are pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We pre-train 25,000 steps with a sequence length of 64. + +``` +python3 preprocess.py --corpus_path corpora/couplet.txt \ + --vocab_path models/google_zh_vocab.txt \ + --dataset_path couplet.pt --processes_num 16 \ + --seq_length 64 --target lm +``` + +``` +python3 pretrain.py --dataset_path couplet.pt \ + --vocab_path models/google_zh_vocab.txt \ + --output_model_path models/couplet_gpt_base_model.bin \ + --config_path models/bert_base_config.json --learning_rate 5e-4 \ + --tie_weight --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ + --batch_size 64 --report_steps 1000 \ + --save_checkpoint_steps 5000 --total_steps 25000 \ + --embedding gpt --encoder gpt2 --target lm + +``` + +### BibTeX entry and citation info + +``` +@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} +} +``` + +[couplet]: https://huggingface.co/uer/gpt2-chinese-couplet + diff --git a/model_cards/uer/gpt2-chinese-poem/README.md b/model_cards/uer/gpt2-chinese-poem/README.md new file mode 100644 index 00000000000000..bb068eac7ff306 --- /dev/null +++ b/model_cards/uer/gpt2-chinese-poem/README.md @@ -0,0 +1,85 @@ +--- +language: zh +widget: +- text: "[CLS] 万 叠 春 山 积 雨 晴 ，" +- text: "[CLS] 青 山 削 芙 蓉 ，" + + +--- + +# Chinese Poem GPT2 Model + +## Model description + +The model is used to generate Chinese ancient poems. 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-poem][poem]. + +Since the parameter skip_special_tokens is used in the pipelines.py, special tokens such as [SEP], [UNK] will be deleted, and the output results may not be neat. + +## How to use + +You can use the model directly with a pipeline for text generation: + +When the parameter skip_special_tokens is True: + +```python +>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline +>>> from transformers import TextGenerationPipeline, +>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem") +>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem") +>>> text_generator = TextGenerationPipeline(model, tokenizer) +>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True) + [{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 于 。 行 将 解 尘 缨 ， 于 焉 蹈 高 踵 。 我'}] +``` + +When the parameter skip_special_tokens is False: + +```python +>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline +>>> from transformers import TextGenerationPipeline, +>>> tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem") +>>> model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem") +>>> text_generator = TextGenerationPipeline(model, tokenizer) +>>> text_generator("[CLS]梅 山 如 积 翠 ，", max_length=50, do_sample=True) + [{'generated_text': '[CLS]梅 山 如 积 翠 ， 的 [UNK] 手 堪 捧 。 遥 遥 仙 人 尉 ， 盘 盘 故 时 陇 。 丹 泉 清 可 鉴 ， 石 乳 甘 可 捧 。 银 汉 迟 不 来 ， 槎 头 欲 谁 揽 。 何'}] +``` + +## Training data + +Contains 800,000 Chinese ancient poems collected by [chinese-poetry](https://github.com/chinese-poetry/chinese-poetry) and [Poetry](https://github.com/Werneror/Poetry) projects. + +## Training procedure + +The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We pre-train 200,000 steps with a sequence length of 128. + +``` +python3 preprocess.py --corpus_path corpora/poem.txt \ + --vocab_path models/google_zh_vocab.txt \ + --dataset_path poem.pt --processes_num 16 \ + --seq_length 128 --target lm +``` + +``` +python3 pretrain.py --dataset_path poem.pt \ + --vocab_path models/google_zh_vocab.txt \ + --output_model_path models/poem_gpt_base_model.bin \ + --config_path models/bert_base_config.json --learning_rate 5e-4 \ + --tie_weight --world_size 8 --gpu_ranks 0 1 2 3 4 5 6 7 \ + --batch_size 64 --report_steps 1000 \ + --save_checkpoint_steps 50000 --total_steps 200000 \ + --embedding gpt --encoder gpt2 --target lm + +``` + +### BibTeX entry and citation info + +``` +@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} +} +``` + +[poem]: https://huggingface.co/uer/gpt2-chinese-poem