README.md

---
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：[Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) 
训练环境：英伟达16G显卡

mt5分词："vocab_size"=50000
```
[INFO|trainer.py:1634] 2022-12-13 06:27:25,113 >> ***** Running training *****
[INFO|trainer.py:1635] 2022-12-13 06:27:25,113 >>   Num examples = 741096
[INFO|trainer.py:1636] 2022-12-13 06:27:25,113 >>   Num Epochs = 36
[INFO|trainer.py:1637] 2022-12-13 06:27:25,113 >>   Instantaneous batch size per device = 256
[INFO|trainer.py:1638] 2022-12-13 06:27:25,113 >>   Total train batch size (w. parallel, distributed & accumulation) = 256
[INFO|trainer.py:1639] 2022-12-13 06:27:25,114 >>   Gradient Accumulation steps = 1
[INFO|trainer.py:1640] 2022-12-13 06:27:25,114 >>   Total optimization steps = 104220
[INFO|trainer.py:1642] 2022-12-13 06:27:25,114 >>   Number of trainable parameters = 77419008
[INFO|trainer.py:1663] 2022-12-13 06:27:25,115 >>   Continuing training from checkpoint, will skip to saved global_step
[INFO|trainer.py:1664] 2022-12-13 06:27:25,115 >>   Continuing training from epoch 2
[INFO|trainer.py:1665] 2022-12-13 06:27:25,115 >>   Continuing training from global step 7500

{'loss': 5.5206, 'learning_rate': 4.616340433697947e-05, 'epoch': 2.76}
{'loss': 5.4737, 'learning_rate': 4.5924006908462866e-05, 'epoch': 2.94}
{'loss': 5.382, 'learning_rate': 4.5684609479946274e-05, 'epoch': 3.11}
{'loss': 5.34, 'learning_rate': 4.544473229706391e-05, 'epoch': 3.28}
{'loss': 5.3154, 'learning_rate': 4.520485511418154e-05, 'epoch': 3.45}
......
......
......
{'loss': 3.3099, 'learning_rate': 3.650930723469584e-07, 'epoch': 35.75}
{'loss': 3.3077, 'learning_rate': 1.2521588946459413e-07, 'epoch': 35.92}
{'train_runtime': 41498.9079, 'train_samples_per_second': 642.895, 'train_steps_per_second': 2.511, 'train_loss': 3.675059686432734, 'epoch': 36.0}
***** train metrics *****
  epoch                    =        36.0
  train_loss               =      3.6751
  train_runtime            = 11:31:38.90
  train_samples            =      741096
  train_samples_per_second =     642.895
  train_steps_per_second   =       2.511
12/13/2022 17:59:05 - INFO - __main__ - *** Evaluate ***
[INFO|trainer.py:2944] 2022-12-13 17:59:05,707 >> ***** Running Evaluation *****
[INFO|trainer.py:2946] 2022-12-13 17:59:05,708 >>   Num examples = 3834
[INFO|trainer.py:2949] 2022-12-13 17:59:05,708 >>   Batch size = 256
100%|██████████| 15/15 [03:25<00:00, 13.69s/it]
[INFO|modelcard.py:449] 2022-12-13 18:02:46,984 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Translation', 'type': 'translation'}, 'metrics': [{'name': 'Bleu', 'type': 'bleu', 'value': 3.7831}]}
***** eval metrics *****
  epoch                   =       36.0
  eval_bleu               =     3.7831
  eval_gen_len            =       63.0
  eval_loss               =     4.5035
  eval_runtime            = 0:03:40.09
  eval_samples            =       3834
  eval_samples_per_second =     17.419
  eval_steps_per_second   =      0.068

```