metadata
language: zh
datasets: couplet
inference:
parameters:
max_length: 68
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:
>>> task_prefix = ""
>>> sentence = task_prefix+"国色天香，姹紫嫣红，碧水青云欣共赏"
>>> model_output_dir='couplet-hel-mt5-finetuning/'
>>> from transformers import pipeline
>>> translation = pipeline("translation", model=model_output_dir)
>>> print(translation(sentence,max_length=28))
[{'translation_text': '月圆花好,良辰美景,良辰美景喜相逢'}]
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("supermy/couplet-helsinki")
model = AutoModel.from_pretrained("supermy/couplet-helsinki")
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 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