TriadParty
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README.md
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license: apache-2.0
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---
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---
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license: apache-2.0
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datasets:
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- TriadParty/deepsword
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language:
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- zh
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- en
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---
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## **Deepsword-34B-Base**
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Introducing **wrath** in the Seven Deadly Sins series of models.
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![](https://media.discordapp.net/attachments/1088992345824972840/1187269297811247195/dickboy._Chinese_Fangtian_Painting_Halberd_Manufactured_by_Mech_532eefe6-7d75-473c-b5ef-13e1f46bb09e.png?ex=659645b2&is=6583d0b2&hm=51125137c9b25e1f7447c35ea07e891393b374c8072e023b04c0f231a1533cd8 =200x200)
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- Continuous pre-training of qlora on Yi-34b
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- High-quality martial arts novels
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- Thoughtful cleaning process
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This model is designed to serve as the base model in the agent model of the script-killing game process. For this purpose, I've collected approximately 10G of martial arts novels, sourced from various novel websites and PT sites. However, this dataset includes a significant amount of duplicate and low-quality content. To address these issues, I've undertaken the following steps:
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### 1. Define Data Quality Dimensions
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For martial arts novels, high-quality works are typically represented by authors like Jin Yong, Gu Long, and Liang Yusheng. In these novels, the complexity of the plot is a critical factor and is the focal point for script quality.
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### 2. Quantify Data Quality Dimensions
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Given the emphasis on plot complexity, we approached this in several stages:
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Chapter Summarization:
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English: Utilize [Hugging Face's LED-Large-Book-Summary model](https://huggingface.co/pszemraj/led-large-book-summary).
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Chinese: Use the [Randeng-Pegasus-523M-Summary-Chinese](https://huggingface.co/IDEA-CCNL/Randeng-Pegasus-523M-Summary-Chinese) model.
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Vectorization and Complexity Analysis:
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Convert plot summaries into vectors using a BERT-based model.
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Measure transitions between chapters through cosine similarity or Euclidean distance.
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Develop a complexity algorithm focused on standard deviation and peak analysis.
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Metric Quantification:
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Apply subjective weighting to the complexity metrics derived from chapter transitions.
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### 3. Outcome
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By employing these methods, we can effectively filter out novels of higher quality. This refined [dataset](https://huggingface.co/datasets/TriadParty/deepsword) has been shared for further use. The next step is to continue pretraining, for which specific parameters can be referred to in my previous model descriptions.
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