Create README.md

README.md

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+---
+language: zh
+datasets: couplet
+inference:
+  parameters:
+    max_length: 108
+    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] 2022-11-29 16:00:16,391 >> ***** Running training *****
+[INFO|trainer.py:1609] 2022-11-29 16:00:16,391 >>   Num examples = 249327
+[INFO|trainer.py:1610] 2022-11-29 16:00:16,391 >>   Num Epochs = 38
+[INFO|trainer.py:1611] 2022-11-29 16:00:16,391 >>   Instantaneous batch size per device = 96
+[INFO|trainer.py:1612] 2022-11-29 16:00:16,391 >>   Total train batch size (w. parallel, distributed & accumulation) = 96
+[INFO|trainer.py:1613] 2022-11-29 16:00:16,391 >>   Gradient Accumulation steps = 1
+[INFO|trainer.py:1614] 2022-11-29 16:00:16,391 >>   Total optimization steps = 98724
+[INFO|trainer.py:1616] 2022-11-29 16:00:16,392 >>   Number of trainable parameters = 124439808
+
+{'loss': 6.4109, 'learning_rate': 4.975031400672582e-05, 'epoch': 0.19}
+{'loss': 5.8476, 'learning_rate': 4.9497082776224627e-05, 'epoch': 0.38}
+......
+......
+......
+{'loss': 3.4331, 'learning_rate': 1.3573193954864066e-07, 'epoch': 37.91}
+{'train_runtime': 65776.233, 'train_samples_per_second': 144.04, 'train_steps_per_second': 1.501, 'train_loss': 3.74187503763847, 'epoch': 38.0}
+***** train metrics *****
+  epoch                    =        38.0
+  train_loss               =      3.7419
+  train_runtime            = 18:16:16.23
+  train_samples            =      249327
+  train_samples_per_second =      144.04
+  train_steps_per_second   =       1.501
+11/30/2022 10:16:35 - INFO - __main__ - *** Evaluate ***
+[INFO|trainer.py:2929] 2022-11-30 10:16:35,902 >> ***** Running Evaluation *****
+[INFO|trainer.py:2931] 2022-11-30 10:16:35,902 >>   Num examples = 1290
+[INFO|trainer.py:2934] 2022-11-30 10:16:35,902 >>   Batch size = 96
+100%|██████████| 14/14 [00:03<00:00,  4.13it/s]
+[INFO|modelcard.py:449] 2022-11-30 10:16:40,821 >> 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': 0.39426602682416634}]}
+***** eval metrics *****
+  epoch                   =       38.0
+  eval_accuracy           =     0.3943
+  eval_loss               =      3.546
+  eval_runtime            = 0:00:03.67
+  eval_samples            =       1290
+  eval_samples_per_second =    351.199
+  eval_steps_per_second   =      3.811
+  perplexity              =    34.6733
+
+```