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EN
Jinyong Gpt2
J
Jinyong Gpt2
Developed by supermy
A text generation model fine-tuned on GPT-2 for Jin Yong martial arts novel style
Downloads 71
Release Time : 12/2/2022
Model Overview
This model is used to generate continuations in Jin Yong's style, automatically extending text based on a given beginning, mimicking the language style and narrative characteristics of Jin Yong's martial arts novels.
Model Features
Jin Yong Style Imitation
Capable of generating text with the distinctive features of Jin Yong's martial arts novels, including language style, character dialogue, and plot development.
Long Text Generation
Supports generating coherent text up to 108 tokens long, maintaining contextual consistency.
Random Sampling
Enables random sampling functionality, making the generated text more diverse and creative.
Model Capabilities
Martial arts novel continuation
Style imitation
Creative writing assistance
Use Cases
Literary creation
Martial arts novel continuation
Generates follow-up content consistent with Jin Yong's style based on provided excerpts
Produces coherent text that aligns with Jin Yong's style
Creative writing assistance
Provides inspiration and material for writers working on martial arts novels
Diverse plot developments and character dialogue suggestions
Educational research
Literary style research
Used for analyzing linguistic features of Jin Yong's literary style
Generates research samples for style analysis
🚀 Flying Snow, Shooting White Deer: AI for Jin Yong's Novels
This AI model is designed to continue Jin Yong's novels based on given openings, offering a unique way to experience the charm of martial - arts literature.
🚀 Quick Start
✨ Features
- Generate continuations for Jin Yong's novels with a given text as the starting point.
📦 Installation
No specific installation steps are provided in the original document, so this section is skipped.
💻 Usage Examples
Basic Usage
# Invoking the fine - tuned model
senc="These snowflakes are falling, so white and beautiful. In a few days, when the sun comes out, each snowflake will disappear without a trace. By next winter, there will be many more snowflakes, but they won't be these ones from this year."
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': 'These snowflakes are falling, so white and beautiful. In a few days, when the sun comes out, each snowflake will disappear without a trace. By next winter, there will be many more snowflakes, but they won\'t be these ones from this year. Anyway, God has eyes. I don\'t know what kind of risks there are?” Just as he was speaking, he suddenly heard Xie Xun\'s howl getting closer. He couldn\'t help opening his mouth and exclaiming. Everyone rushed towards him. They only heard Xie Xun let out a roar, and then he slapped out with his left hand, using his palm force to disperse. Everyone was startled and jumped out of the sea at the same time, both taking a step back. Zhang Cuishan and Yin Susu looked at each other. They both thought about how they could resist with the strength of these two great masters and how they could attack the enemy with today\'s strength.'}]
Advanced Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("supermy/jinyong - gpt2")
model = AutoModelForCausalLM.from_pretrained("supermy/jinyong - gpt2")
📚 Documentation
Model Description
The model is used for AI - generated continuations of Jin Yong's novels. Given an opening text, it can generate subsequent content.
Training Data
This dataset is trained on Jin Yong's novel collection "Flying Snow, Shooting White Deer, Laughing Book, Divine Hero, Leaning on Green M鸳".
Training Procedure
- Base Model: GPT2
- Training Environment: NVIDIA 16G GPU
- BPE Tokenization: "vocab_size" = 30000
The following are the training statistics:
[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
📄 License
No license information is provided in the original document, so this section is skipped.
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