Update README.md

README.md

@@ -9,4 +9,70 @@ tags:
 pretty_name: Drama Llama dataset
 size_categories:
 - 10K<n<100K
----
+---
+
+
+# DramaLlama dataset
+
+![title.png](title.png)
+
+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 
+