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Datasets from Instructions (DINO 🦕)

This repository contains the code for Generating Datasets with Pretrained Language Models. The paper introduces a method called Datasets from Instructions (DINO 🦕) that enables pretrained language models to generate entire datasets from scratch.

🔧 Setup

All requirements for DINO can be found in requirements.txt. You can install all required packages in a new environment with pip install -r requirements.txt.

💬 CLI Usage

Single Texts

To generate datasets for (single) text classification, you can use DINO as follows:

python3 dino.py \
 --output_dir <OUTPUT_DIR> \
 --task_file <TASK_FILE> \
 --num_entries_per_label <N> \
 --batch_size 1

where <OUTPUT_DIR> is a directory to which the generated dataset is written, <TASK_FILE> is a JSON file containing a task specification (see Task Specs), and <N> is the number of examples to generate per label. To get an overview of additional parameters, run python3 dino.py --help.

Text Pairs

To generate datasets for text pair classification, you first need a dataset of raw input texts (which you can also generate using DINO). You can then run

python3 dino.py \
 --output_dir <OUTPUT_DIR> \
 --task_file <TASK_FILE> \
 --input_file <INPUT_FILE> \
 --input_file_type <INPUT_FILE_TYPE> \
 --num_entries_per_input_and_label <N>

with <OUTPUT_DIR> and <TASK_FILE> as before. <INPUT_FILE> refers to the file containing raw input texts, <INPUT_FILE_TYPE> specifies its type, which should be one of

  • plain: for a plain text file with one input text per line
  • jsonl: for a dataset file generated by DINO in a previous step

and <N> is the number of examples to generate per label and input text.

📋 Task Specs

🚨 Before you write custom task specifications, please note that this is still a very early release and we have not tested DINO on other tasks than semantic textual similarity yet. Please let us know if you see something strange. 🚨

To generate a dataset for a task, you need to provide a file containing a task specification, containing (among other things) the instructions given to the pretrained language model. A task specification is a single JSON object that looks like this:

{
  "task_name": "<TASK_NAME>",
  "labels": {
    "<LABEL_1>": {
      "instruction": "<INSTRUCTION_1>",
      "counter_labels": [<COUNTER_LABELS_1>]
    },

    ...,

    "<LABEL_n>": {
      "instruction": "<INSTRUCTION_n>",
      "counter_labels": [<COUNTER_LABELS_n>]
    }
  }
}

Here, <TASK_NAME> is the name for the task and <LABEL_1>, ..., <LABEL_n> are the task's labels. For each label <LABEL_i>, <INSTRUCTION_i> is the instruction provided to the language model for generating examples with label <LABEL_i> (see Writing Instructions). You can additionally specify a list of counter labels <COUNTER_LABELS_n> for each label. This tells the model to generate outputs that are not only likely given the current label, but also unlikely given all counter labels (see the paper for details).

Examples

You can find two examples of task specifications in /task_specs:

  • sts.json is a task specification for generating a semantic textual similarity dataset if a set of raw input texts is already given.
  • sts-x1.json is a task specification for generating a set of raw input texts. This set can then be used in a subsequent step to generate a full STS dataset using sts.json.

Writing Instructions

When writing instructions for a new task, you should consider the following things:

  • Always end your instructions with an (opening) quotation mark ("). This is required because it allows us to interpret the next quotation mark generated by the language model as a signal that it is done generating an example.
  • For good results, keep the instructions as short and simple as possible as this makes it easier for a pretrained language model to understand them.
  • If you are writing instructions for a text pair classification task, make sure that each instruction contains the placeholder <X1> exactly once. At this position, the provided raw input sentences are inserted during generation.

An example for an instruction that prompts the model to generate a positive review for a restaurant would be:

Task: Write a review for a really great restaurant.
Review: "

An example for an instruction that prompts the model to generate a sentence that has the same meaning as another given sentence would be:

Task: Write two sentences that mean the same thing.
Sentence 1: "<X1>"
Sentence 2: "

🦕 Generated DINOs

This section lists datasets that we have generated using DINO.

Dataset Description Link
STS‑🦕‑x2 (pp) A postprocessed version of STS-🦕-x2. Postprocessing includes label smoothing, data augmentation and selecting at most two x2's for each (x1, y) and is performed using this script. 📥 Download
STS‑🦕‑x1x2 (pp) A postprocessed version of STS-🦕-x1x2. Postprocessing includes label smoothing, data augmentation and selecting at most two x2's for each (x1, y) and is performed using this script. 📥 Download
STS‑🦕‑x2 (raw) A semantic textual similarity dataset generated with DINO, where the first text for each pair (x1, x2) is from the STS benchmark. For almost all use cases, you probably want to use the postprocessed (pp) version of this dataset. 📥 Download
STS‑🦕‑x1x2 (raw) A semantic textual similarity dataset generated with DINO, where each pair (x1, x2) is generated from scratch. For almost all use cases, you probably want to use the postprocessed (pp) version of this dataset. 📥 Download

📕 Citation

If you make use of the code in this repository or of any DINO-based dataset, please cite the following paper:

@article{schick2020generating,
  title={Generating Datasets with Pretrained Language Models},
  author={Timo Schick and Hinrich Schütze},
  journal={Computing Research Repository},
  volume={arXiv:2104.07540},
  url={https://arxiv.org/abs/2104.07540},
  year={2021}
}