README.md

---
language:
- nl
license: eupl-1.1
size_categories:
- 10K<n<100K
tags:
- documents
- fine-tuning
dataset_info:
  features:
  - name: prompt_id
    dtype: int64
  - name: message
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  splits:
  - name: train
    num_bytes: 9011452
    num_examples: 9900
  - name: test
    num_bytes: 998068
    num_examples: 1100
  - name: val
    num_bytes: 1000675
    num_examples: 1100
  - name: discard
    num_bytes: 7897005
    num_examples: 8718
  download_size: 5846654
  dataset_size: 18907200
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: val
    path: data/val-*
  - split: discard
    path: data/discard-*
---

This dataset is used in the Woo-document classification project from the Municipality of Amsterdam. 
The goal of this dataset is to fine-tune LLMs. The documents are formatted into a zero-shot prompt and then turned into conversations, 
where the ideal response of the model is the prediction (class) formatted into JSON format. 


Specifics:
- Truncation: first 200 tokens of each document. Docs are tokenized using the Llama tokenizer.
- Data split:
  - test set: first 100 docs of each class (in total 1100 docs)
  - train set: remaining docs, with max of 1500 docs per class (11000 docs)
    - train set: 90% of train set is used for fine-tuning model (9900 docs)
    - val set: 10% of train segt is used for evaluating the loss during training (1100 docs)