README.md

---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- pszemraj/fleece2instructions
metrics:
- rouge
model-index:
- name: bart-base-instructiongen
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: pszemraj/fleece2instructions
      type: pszemraj/fleece2instructions
      split: validation
    metrics:
    - name: Rouge1
      type: rouge
      value: 61.7209
widget:
- text: "To plan a successful surprise birthday party, you'll need to start by choosing the right venue. Consider the type of atmosphere and the size of the area that will be suitable for the number of guests you plan to invite. Choose the right decorations based on your brother's interests, such as balloons in his favorite colors, banners, and streamers. Next, decide on the food and drinks, making sure they are tasty and appropriate for the occasion. Then decide on the other games, music, and entertainment that will make the party memorable. Finally, involve your brother's friends and family to help create the perfect surprise."
  example_title: "birthday party"
- text: "1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo"
  example_title: "ice cream"
- Text: "To create a miniature diorama of a post-apocalyptic cityscape, start by choosing a scale model of a building that fits the theme. Use a hobby knife and glue to cut and assemble the model into a ruined or abandoned version of itself, adding details such as broken windows and graffiti. Create a base for the diorama using foam, plaster, or other materials, and paint it to resemble a ruined street or sidewalk. Add miniature vehicles, debris, and figures to complete the scene, and use weathering techniques such as dry brushing and rust washes to add realism. Display the diorama in a shadow box or other protective case to showcase your work."
  example_title: "Creating a Miniature Diorama"
- text: "To train a pet rat to perform tricks, start by teaching it basic commands like 'come' and 'stay' using positive reinforcement such as treats or praise. Once your rat has mastered these commands, move on to more complex tricks like 'spin' or 'fetch'. Use a clicker and treats to mark and reward desired behaviors, and break the trick down into smaller steps to make it easier for your rat to learn. Consider using a target stick or other training aids to help your rat understand what you're asking him to do. With patience and consistency, you can train your pet rat to perform a variety of impressive and entertaining tricks."
  example_title: "Rat Training"
- text: "To train for a marathon, start by setting a realistic goal and creating a training plan. Build up your mileage gradually over time, and incorporate cross-training and strength exercises to prevent injury and improve endurance. Be sure to stay hydrated and properly fuel your body with nutritious foods. Listen to your body and adjust your training as needed to avoid overexertion or burnout. Finally, taper your training in the weeks leading up to the race to give your body time to rest and recover before the big day."
  example_title: "Marathon training"
- text: "To create a personalized fragrance blend, start by selecting base notes that complement your skin chemistry, such as sandalwood, vanilla, or musk. Add middle notes that provide depth and complexity, such as jasmine, lavender, or rose. Finally, add top notes that provide freshness and vibrancy, such as citrus, mint, or green tea. Experiment with different combinations and ratios of scents until you find a blend that suits your style and personality. Consider using essential oils or natural ingredients for a more eco-friendly and sustainable fragrance option. Enjoy your unique and personalized scent and let it inspire confidence and creativity in everything you do."
  example_title: "Surreal 3D landscape creation"

---


# bart-base-instructiongen

Instead of generating questions from text, generate instructions for LLMs!

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the pszemraj/fleece2instructions dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0034
- Rouge1: 61.7209
- Rouge2: 45.0116
- Rougel: 59.8188
- Rougelsum: 59.8931
- Gen Len: 14.3179

## Intended uses & limitations

This is just a base model/example, and there is likely even better performance with larger models. 

Additionally, this was trained on a dataset of **only** instructions+outputs with the `inputs` filtered out/dropped. This means that text of  *1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo* will **not** get you *"Rank the following icecream flavors: oreo, mint chip, chocolate chip, cookies and cream"*

## Training and evaluation data

See the linked dataset `pszemraj/fleece2instructions` - it is a filtered/formatted version of `tatsu-lab/alpaca` to generate instructions for arbitrary text.

- some of the API examples are purposefully weird to demonstrate generalizability of the model

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.2723        | 1.0   | 362  | 1.0325          | 61.6206 | 45.1199 | 59.6467 | 59.7534   | 14.0443 |
| 1.0157        | 2.0   | 724  | 1.0034          | 62.4433 | 46.0114 | 60.5355 | 60.6392   | 14.1807 |