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--- |
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language: zh |
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datasets: couplet |
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inference: |
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parameters: |
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max_length: 68 |
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num_return_sequences: 1 |
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do_sample: True |
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widget: |
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- text: "燕子归来，问昔日雕梁何处。 -" |
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example_title: "对联1" |
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- text: "笑取琴书温旧梦。 -" |
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example_title: "对联2" |
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- text: "煦煦春风，吹暖五湖四海。 -" |
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example_title: "对联3" |
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--- |
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# 对联 |
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## Model description |
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对联AI生成，给出上联，生成下联。 |
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## How to use |
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使用 pipeline 调用模型: |
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```python |
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>>> # 调用微调后的模型 |
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>>> senc="燕子归来，问昔日雕梁何处。 -" |
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>>> model_id="couplet-gpt2-finetuning" |
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>>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline |
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>>> tokenizer = BertTokenizer.from_pretrained(model_id) |
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>>> model = GPT2LMHeadModel.from_pretrained(model_id) |
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>>> text_generator = TextGenerationPipeline(model, tokenizer) |
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>>> text_generator.model.config.pad_token_id = text_generator.model.config.eos_token_id |
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>>> text_generator( senc,max_length=25, do_sample=True) |
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[{'generated_text': '燕子归来，问昔日雕梁何处。 - 风 儿 吹 醒 ， 叹 今 朝 烟 雨 无'}] |
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``` |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("supermy/couplet") |
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model = AutoModelForCausalLM.from_pretrained("supermy/couplet") |
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``` |
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## Training data |
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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。 |
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## 统计信息 |
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``` |
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``` |
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## Training procedure |
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模型：[Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) |
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训练环境：英伟达16G显卡 |
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mt5分词："vocab_size"=50000 |
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``` |
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[INFO|trainer.py:1634] 2022-12-13 06:27:25,113 >> ***** Running training ***** |
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[INFO|trainer.py:1635] 2022-12-13 06:27:25,113 >> Num examples = 741096 |
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[INFO|trainer.py:1636] 2022-12-13 06:27:25,113 >> Num Epochs = 36 |
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[INFO|trainer.py:1637] 2022-12-13 06:27:25,113 >> Instantaneous batch size per device = 256 |
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[INFO|trainer.py:1638] 2022-12-13 06:27:25,113 >> Total train batch size (w. parallel, distributed & accumulation) = 256 |
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[INFO|trainer.py:1639] 2022-12-13 06:27:25,114 >> Gradient Accumulation steps = 1 |
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[INFO|trainer.py:1640] 2022-12-13 06:27:25,114 >> Total optimization steps = 104220 |
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[INFO|trainer.py:1642] 2022-12-13 06:27:25,114 >> Number of trainable parameters = 77419008 |
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[INFO|trainer.py:1663] 2022-12-13 06:27:25,115 >> Continuing training from checkpoint, will skip to saved global_step |
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[INFO|trainer.py:1664] 2022-12-13 06:27:25,115 >> Continuing training from epoch 2 |
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[INFO|trainer.py:1665] 2022-12-13 06:27:25,115 >> Continuing training from global step 7500 |
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{'loss': 5.5206, 'learning_rate': 4.616340433697947e-05, 'epoch': 2.76} |
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{'loss': 5.4737, 'learning_rate': 4.5924006908462866e-05, 'epoch': 2.94} |
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{'loss': 5.382, 'learning_rate': 4.5684609479946274e-05, 'epoch': 3.11} |
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{'loss': 5.34, 'learning_rate': 4.544473229706391e-05, 'epoch': 3.28} |
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{'loss': 5.3154, 'learning_rate': 4.520485511418154e-05, 'epoch': 3.45} |
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...... |
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...... |
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...... |
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{'loss': 3.3099, 'learning_rate': 3.650930723469584e-07, 'epoch': 35.75} |
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{'loss': 3.3077, 'learning_rate': 1.2521588946459413e-07, 'epoch': 35.92} |
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{'train_runtime': 41498.9079, 'train_samples_per_second': 642.895, 'train_steps_per_second': 2.511, 'train_loss': 3.675059686432734, 'epoch': 36.0} |
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***** train metrics ***** |
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epoch = 36.0 |
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train_loss = 3.6751 |
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train_runtime = 11:31:38.90 |
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train_samples = 741096 |
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train_samples_per_second = 642.895 |
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train_steps_per_second = 2.511 |
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12/13/2022 17:59:05 - INFO - __main__ - *** Evaluate *** |
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[INFO|trainer.py:2944] 2022-12-13 17:59:05,707 >> ***** Running Evaluation ***** |
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[INFO|trainer.py:2946] 2022-12-13 17:59:05,708 >> Num examples = 3834 |
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[INFO|trainer.py:2949] 2022-12-13 17:59:05,708 >> Batch size = 256 |
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100%|██████████| 15/15 [03:25<00:00, 13.69s/it] |
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[INFO|modelcard.py:449] 2022-12-13 18:02:46,984 >> Dropping the following result as it does not have all the necessary fields: |
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{'task': {'name': 'Translation', 'type': 'translation'}, 'metrics': [{'name': 'Bleu', 'type': 'bleu', 'value': 3.7831}]} |
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***** eval metrics ***** |
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epoch = 36.0 |
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eval_bleu = 3.7831 |
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eval_gen_len = 63.0 |
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eval_loss = 4.5035 |
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eval_runtime = 0:03:40.09 |
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eval_samples = 3834 |
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eval_samples_per_second = 17.419 |
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eval_steps_per_second = 0.068 |
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``` |
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