README.md

---
license: mit
tags:
- generated_from_trainer
datasets:
- pszemraj/fleece2instructions
metrics:
- rouge
model-index:
- name: bart_lfqa-fleece2instructions-r1
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: pszemraj/fleece2instructions
      type: pszemraj/fleece2instructions
      split: None
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.0334
widget:
- text: >-
    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: >-
    Start by
    selecting 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 like 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 like 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: Miniature diorama creation
- text: >-
    Start by selecting
    clothing that is futuristic and edgy, such as leather jackets, neon-colored
    accessories, and tech-inspired patterns. Add accessories like goggles,
    cybernetic implants, and LED lights to enhance the cyberpunk vibe. Use
    makeup and body paint to create a futuristic look, such as metallic skin or
    neon makeup. Consider adding functional elements to your costume, such as a
    built-in backpack or hidden pockets for your tech gadgets. Finally, practice
    your confident walk and embrace your inner cyberpunk for a memorable and
    immersive costume experience.
  example_title: Cyberpunk costume design
- text: >-
    Start by creating a base
    terrain with mountains, valleys, and other natural features. Use fractal
    noise and displacement mapping to add texture and detail to the terrain, and
    experiment with different materials like rock, grass, and water. Add surreal
    elements like floating islands, giant mushrooms, or impossible geometry to
    create a dreamlike atmosphere. Use lighting and color grading to enhance the
    mood and tone of the scene, and render the final image at a high resolution
    for maximum impact. Share your surreal landscape with the world and inspire
    others to explore the possibilities of 3D art.
  example_title: Surreal 3D landscape creation
- text: >-
    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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bart_lfqa-fleece2instructions-r1

This model is a fine-tuned version of [vblagoje/bart_lfqa](https://huggingface.co/vblagoje/bart_lfqa) on the pszemraj/fleece2instructions dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1890
- Rouge1: 0.0334
- Rouge2: 0.0299
- Rougel: 0.0334
- Rougelsum: 0.0334
- Gen Len: 255.9156

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- 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: 0.03
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.9558        | 1.0   | 362  | 1.2120          | 0.0    | 0.0    | 0.0    | 0.0       | 256.0   |
| 0.757         | 2.0   | 724  | 1.1890          | 0.0    | 0.0    | 0.0    | 0.0       | 256.0   |


### Framework versions

- Transformers 4.25.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2