metadata
language: zh
datasets: jinyong
inference:
parameters:
max_length: 108
num_return_sequences: 1
do_sample: true
widget:
- text: 杨过朗声说道：今番良晤，豪兴不浅，他日江湖相逢，再当杯酒言欢。咱们就此别过。 -
example_title: 神雕侠侣
- text: 乱世之际，人不如狗。 -
example_title: 射雕英雄传
飞雪连天射白鹿，笑书神侠倚碧鸳
Model description
AI生成金庸小说，给出开头续写。
How to use
使用 pipeline 调用模型:
>>> # 调用微调后的模型
>>> senc="这些雪花落下来,多么白,多么好看.过几天太阳出来,每一片 雪花都变得无影无踪.到得明年冬天,又有许很多多雪花,只不过已不是 今年这些雪花罢了。"
>>> model_id="jinyong-gpt2-finetuning"
>>> from transformers import AutoTokenizer, GPT2LMHeadModel, TextGenerationPipeline
>>> tokenizer = AutoTokenizer.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=108, do_sample=True)
[{'generated_text': '这些雪花落下来,多么白,多么好看.过几天太阳出来,每一片 雪花都变得无影无踪.到得明年冬天,又有许很多多雪花,只不过已不是 今年这些雪花罢了。 反正 老天爷 有眼 ， 不知 哪里 是甚么 风 险 ？” 正 说到此处 ， 突然 听得 谢逊 啸声 渐近 ， 忍不住 张口 惊呼 ， 一齐 向他 扑去 ， 只听 谢逊 一声 怒吼 ， 跟着 左手 用力 拍 出一掌 ， 以 掌力 化开 。 众人 吃了一惊 ， 同时 从 海 道 中 跃出 ， 双双 倒退 。 张翠山和殷素素 对望一眼 ， 均想 以 这两 大高手 之力 如何 抵挡 ， 以 今日 之力 如何 攻敌 之'}]
>>>
Here is how to use this model to get the features of a given text in PyTorch:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("supermy/jinyong-gpt2")
model = AutoModelForCausalLM.from_pretrained("supermy/jinyong-gpt2")
Training data
此数据集基于金庸的【飞雪连天射白鹿，笑书神侠倚碧鸳】小说集训练。
统计信息
Training procedure
基于模型：GPT2 训练环境：英伟达16G显卡
bpe分词："vocab_size"=30000
[INFO|trainer.py:1608] 2022-12-02 19:52:59,024 >> ***** Running training *****
[INFO|trainer.py:1609] 2022-12-02 19:52:59,024 >> Num examples = 9443
[INFO|trainer.py:1610] 2022-12-02 19:52:59,024 >> Num Epochs = 108
[INFO|trainer.py:1611] 2022-12-02 19:52:59,024 >> Instantaneous batch size per device = 12
[INFO|trainer.py:1612] 2022-12-02 19:52:59,024 >> Total train batch size (w. parallel, distributed & accumulation) = 12
[INFO|trainer.py:1613] 2022-12-02 19:52:59,024 >> Gradient Accumulation steps = 1
[INFO|trainer.py:1614] 2022-12-02 19:52:59,024 >> Total optimization steps = 84996
[INFO|trainer.py:1616] 2022-12-02 19:52:59,025 >> Number of trainable parameters = 124439808
[INFO|trainer.py:1608] 2022-12-03 21:44:00,182 >> ***** Running training *****
[INFO|trainer.py:1609] 2022-12-03 21:44:00,182 >> Num examples = 9443
[INFO|trainer.py:1610] 2022-12-03 21:44:00,182 >> Num Epochs = 216
[INFO|trainer.py:1611] 2022-12-03 21:44:00,182 >> Instantaneous batch size per device = 12
[INFO|trainer.py:1612] 2022-12-03 21:44:00,182 >> Total train batch size (w. parallel, distributed & accumulation) = 12
[INFO|trainer.py:1613] 2022-12-03 21:44:00,182 >> Gradient Accumulation steps = 1
[INFO|trainer.py:1614] 2022-12-03 21:44:00,182 >> Total optimization steps = 169992
[INFO|trainer.py:1616] 2022-12-03 21:44:00,183 >> Number of trainable parameters = 124439808
[INFO|trainer.py:1637] 2022-12-03 21:44:00,184 >> Continuing training from checkpoint, will skip to saved global_step
[INFO|trainer.py:1638] 2022-12-03 21:44:00,184 >> Continuing training from epoch 107
[INFO|trainer.py:1639] 2022-12-03 21:44:00,184 >> Continuing training from global step 84500
[INFO|trainer.py:1608] 2022-12-05 07:36:13,626 >> ***** Running training *****
[INFO|trainer.py:1609] 2022-12-05 07:36:13,626 >> Num examples = 9443
[INFO|trainer.py:1610] 2022-12-05 07:36:13,626 >> Num Epochs = 368
[INFO|trainer.py:1611] 2022-12-05 07:36:13,626 >> Instantaneous batch size per device = 12
[INFO|trainer.py:1612] 2022-12-05 07:36:13,626 >> Total train batch size (w. parallel, distributed & accumulation) = 12
[INFO|trainer.py:1613] 2022-12-05 07:36:13,626 >> Gradient Accumulation steps = 1
[INFO|trainer.py:1614] 2022-12-05 07:36:13,626 >> Total optimization steps = 289616
[INFO|trainer.py:1616] 2022-12-05 07:36:13,627 >> Number of trainable parameters = 124439808
[INFO|trainer.py:1637] 2022-12-05 07:36:13,628 >> Continuing training from checkpoint, will skip to saved global_step
[INFO|trainer.py:1638] 2022-12-05 07:36:13,628 >> Continuing training from epoch 255
[INFO|trainer.py:1639] 2022-12-05 07:36:13,628 >> Continuing training from global step 201000
{'loss': 8.0431, 'learning_rate': 4.970998635229893e-05, 'epoch': 0.64}
{'loss': 7.4867, 'learning_rate': 4.94158548637583e-05, 'epoch': 1.27}
{'loss': 7.322, 'learning_rate': 4.912172337521766e-05, 'epoch': 1.91}
......
{'loss': 3.901, 'learning_rate': 2.5010882865076008e-05, 'epoch': 108.01}
{'loss': 3.8959, 'learning_rate': 2.4863817120805686e-05, 'epoch': 108.64}
......
{'loss': 3.1625, 'learning_rate': 4.6090404254317857e-07, 'epoch': 214.1}
{'loss': 3.1592, 'learning_rate': 3.1413242976140055e-07, 'epoch': 214.74}
{'loss': 3.1625, 'learning_rate': 1.6706668549108195e-07, 'epoch': 215.37}
{'train_runtime': 72271.9602, 'train_samples_per_second': 28.222, 'train_steps_per_second': 2.352, 'train_loss': 1.7180436183842016, 'epoch': 216.0}
{'loss': 2.7087, 'learning_rate': 4.2642671675598036e-08, 'epoch': 367.85}
{'train_runtime': 74859.0808, 'train_samples_per_second': 46.421, 'train_steps_per_second': 3.869, 'train_loss': 0.8725239146935282, 'epoch': 368.0}
***** train metrics *****
epoch = 368.0
train_loss = 0.8725
train_runtime = 20:47:39.08
train_samples = 9443
train_samples_per_second = 46.421
train_steps_per_second = 3.869
12/06/2022 04:23:55 - INFO - __main__ - *** Evaluate ***
[INFO|trainer.py:2929] 2022-12-06 04:23:55,953 >> ***** Running Evaluation *****
[INFO|trainer.py:2931] 2022-12-06 04:23:55,953 >> Num examples = 283
[INFO|trainer.py:2934] 2022-12-06 04:23:55,954 >> Batch size = 12
100%|██████████| 24/24 [00:07<00:00, 3.20it/s]
[INFO|modelcard.py:449] 2022-12-06 04:24:04,760 >> 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': 0.19599206157122803}]}
***** eval metrics *****
epoch = 368.0
eval_accuracy = 0.196
eval_loss = 7.9524
eval_runtime = 0:00:07.87
eval_samples = 283
eval_samples_per_second = 35.94
eval_steps_per_second = 3.048
perplexity = 2842.2766