Create README.md

README.md

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+---
+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
+
+```