Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
art
License:
| 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 | |