readme: ---
  language: zh
  datasets: couplet
  inference:
    parameters:
      max_length: 68
      num_return_sequences: 1
      do_sample: True
  widget: 
  - text: "燕子归来，问昔日雕梁何处。 -"
    example_title: "对联1"
  - text: "笑取琴书温旧梦。 -"
    example_title: "对联2"
  - text: "煦煦春风，吹暖五湖四海。 -"
    example_title: "对联3"
  ---
  
  
  # 对联
  
  ## Model description
  
    对联AI生成，给出上联，生成下联。
  
  ## How to use
  使用 pipeline 调用模型:
  
  ```python
  >>> # 调用微调后的模型
  >>> senc="燕子归来，问昔日雕梁何处。 -"
  >>> model_id="couplet-gpt2-finetuning"
  >>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
  
  >>> tokenizer = BertTokenizer.from_pretrained(model_id)
  >>> model = GPT2LMHeadModel.from_pretrained(model_id)
  >>> text_generator = TextGenerationPipeline(model, tokenizer)   
  >>> text_generator.model.config.pad_token_id = text_generator.model.config.eos_token_id
  >>> text_generator( senc,max_length=25, do_sample=True)
  [{'generated_text': '燕子归来，问昔日雕梁何处。 - 风 儿 吹 醒 ， 叹 今 朝 烟 雨 无'}]
  ```
  Here is how to use this model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import AutoTokenizer, AutoModelForCausalLM
  tokenizer = AutoTokenizer.from_pretrained("supermy/couplet")
  model = AutoModelForCausalLM.from_pretrained("supermy/couplet")
  ```
  
  
  
  ## Training data
  
  此数据集基于couplet-dataset的70w条数据集，在此基础上利用敏感词词库对数据进行了过滤，删除了低俗或敏感的内容，删除后剩余约74w条对联数据。
  
  ## 统计信息
  
  ```
  
  ```
  
  ## Training procedure
  
  模型：[GPT2](https://huggingface.co/gpt2) 
  训练环境：英伟达16G显卡
  
  bpe分词："vocab_size"=50000
  ```
  [INFO|trainer.py:1608] 2022-12-07 02:32:58,307 >> ***** Running training *****
  [INFO|trainer.py:1609] 2022-12-07 02:32:58,307 >>   Num examples = 260926
  [INFO|trainer.py:1610] 2022-12-07 02:32:58,307 >>   Num Epochs = 160
  [INFO|trainer.py:1611] 2022-12-07 02:32:58,307 >>   Instantaneous batch size per device = 96
  [INFO|trainer.py:1612] 2022-12-07 02:32:58,307 >>   Total train batch size (w. parallel, distributed & accumulation) = 96
  [INFO|trainer.py:1613] 2022-12-07 02:32:58,307 >>   Gradient Accumulation steps = 1
  [INFO|trainer.py:1614] 2022-12-07 02:32:58,307 >>   Total optimization steps = 434880
  [INFO|trainer.py:1616] 2022-12-07 02:32:58,308 >>   Number of trainable parameters = 124439808
  [INFO|trainer.py:1637] 2022-12-07 02:32:58,309 >>   Continuing training from checkpoint, will skip to saved global_step
  [INFO|trainer.py:1638] 2022-12-07 02:32:58,310 >>   Continuing training from epoch 93
  [INFO|trainer.py:1639] 2022-12-07 02:32:58,310 >>   Continuing training from global step 253500
  
  
  [INFO|trainer.py:1608] 2022-11-30 12:51:36,357 >> ***** Running training *****
  [INFO|trainer.py:1609] 2022-11-30 12:51:36,357 >>   Num examples = 260926
  [INFO|trainer.py:1610] 2022-11-30 12:51:36,357 >>   Num Epochs = 81
  [INFO|trainer.py:1611] 2022-11-30 12:51:36,357 >>   Instantaneous batch size per device = 96
  [INFO|trainer.py:1612] 2022-11-30 12:51:36,357 >>   Total train batch size (w. parallel, distributed & accumulation) = 96
  [INFO|trainer.py:1613] 2022-11-30 12:51:36,357 >>   Gradient Accumulation steps = 1
  [INFO|trainer.py:1614] 2022-11-30 12:51:36,357 >>   Total optimization steps = 220158
  [INFO|trainer.py:1616] 2022-11-30 12:51:36,358 >>   Number of trainable parameters = 124439808
  
  {'loss': 6.1104, 'learning_rate': 4.9888034956712906e-05, 'epoch': 0.18}
  {'loss': 5.5855, 'learning_rate': 4.977448014607691e-05, 'epoch': 0.37}
  {'loss': 5.3264, 'learning_rate': 4.966092533544091e-05, 'epoch': 0.55}
  ......
  ......
  ......
  {'loss': 2.8539, 'learning_rate': 5.677740531799889e-08, 'epoch': 80.94}
  {'train_runtime': 146835.0563, 'train_samples_per_second': 143.937, 'train_steps_per_second': 1.499, 'train_loss': 3.1762605669072217, 'epoch': 81.0}
  ***** train metrics *****
    epoch                    =               81.0
    train_loss               =             3.1763
    train_runtime            = 1 day, 16:47:15.05
    train_samples            =             260926
    train_samples_per_second =            143.937
    train_steps_per_second   =              1.499
  12/02/2022 05:38:54 - INFO - __main__ - *** Evaluate ***
  [INFO|trainer.py:2929] 2022-12-02 05:38:54,688 >> ***** Running Evaluation *****
  [INFO|trainer.py:2931] 2022-12-02 05:38:54,688 >>   Num examples = 1350
  [INFO|trainer.py:2934] 2022-12-02 05:38:54,688 >>   Batch size = 96
  100%|██████████| 15/15 [00:03<00:00,  4.20it/s]
  [INFO|modelcard.py:449] 2022-12-02 05:38:59,875 >> Dropping the following result as it does not have all the necessary fields:
  {'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.4447501469723692}]}
  ***** eval metrics *****
    epoch                   =       81.0
    eval_accuracy           =     0.4448
    eval_loss               =     3.2813
    eval_runtime            = 0:00:03.86
    eval_samples            =       1350
    eval_samples_per_second =    349.505
    eval_steps_per_second   =      3.883
    perplexity              =    26.6108
  
  
  {'loss': 3.0967, 'learning_rate': 1.8027961736571009e-07, 'epoch': 159.49}
  {'loss': 3.0922, 'learning_rate': 1.227924944812362e-07, 'ep