---
language: zh
datasets: couplet
inference:
  parameters:
    max_length: 30
    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] 2022-11-30 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

```