Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
art
License:
File size: 3,410 Bytes
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---
license: unlicense
task_categories:
- text-generation
language:
- en
tags:
- art
pretty_name: Drama Llama dataset
size_categories:
- 10K<n<100K
---
# DramaLlama dataset
This is the dataset repository of DramaLlama. This repository contains scripts designed to gather and prepare the dataset.
Note: This repository builds upon the findings of https://github.com/molbal/llm-text-completion-finetune
## Step 1: Getting novels
We will use The Gutenberg project again to gather novels. Let's get some drama categories. I will aim for a larger dataset size this time.
I'm running the following scripts:
```bash
pip install requests
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "detective fiction" --num_records 10000
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "crime nonfiction" --num_records 10000
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "mystery fiction" --num_records 10000
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "detective fiction" --num_records 10000
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "gothic fiction" --num_records 10000
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "horror" --num_records 10000
python .\j\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "romantic fiction" --num_records 10000
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "short stories" --num_records 10000
python .\pipeline\step1-acquire.py --output_dir "./training-data/0_raw/" --topic "western" --num_records 10000
```
## Step 2: Preprocessing
### Step 2/a: Stripping header and footer
Now we need to strip the headers and footers of the files. I noticed how some files failed to download, and those ones do not have a file extension. This might be caused by a bug in the downloader script, but it was ~200 errors for me out of ~4000 downloads so
```bash
python .\pipeline\step2a-strip.py --input_dir "./training-data/0_raw/" --output_dir "./training-data/2a_stripped/"
```
### Step 2/b: Stripping
We do a bit more cleaning. We have two files, a blacklist and a junklist. Blacklist contains expressions that we do not want included in the trainig data, I filled it with common ChatGPT output. (We do not need to worry, as our training data comes well **before** ChatGPT, but still) Junklist's contents are simply removed from it. These are distribution notes here.
Here we chunk to small pieces, (~250) and if a chunk contains a blacklisted sentence, it is sent to our local LLM to rephrase it.
_Note: We need Ollama for this installed on the local environment_
```bash
ollama pull mistral
pip install nltk ollama
python .\pipeline\step2b-clean.py --input_dir "./training-data/2a_stripped/" --output_dir "./training-data/2b_cleaned/" --llm "mistral"
```
After this, it puts the files back together in the output directory.
## Step 3: Chunking
We chunk the dataset now and save it into a parquet file.
```bash
pip install pandas pyarrow
python .\pipeline\step3-chunking.py --source_dir "./training-data/2b_cleaned/" --output_file "./training-data/data.parquet"
```
## Step 4: 🤗 dataset upload
We upload the dataset to Hugging Face:
https://huggingface.co/datasets/molbal/dramallama-novels
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