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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- alpaca |
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- self-instruct |
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- instruct |
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- instruction generation |
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datasets: |
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- pszemraj/fleece2instructions |
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metrics: |
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- rouge |
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model-index: |
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- name: bart-base-instructiongen |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: pszemraj/fleece2instructions |
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type: pszemraj/fleece2instructions |
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split: validation |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 61.7209 |
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widget: |
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- text: >- |
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You'll need to start by |
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choosing the right venue. Consider the type of atmosphere and the size of |
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the area that will be suitable for the number of guests you plan to invite. |
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Choose the right decorations based on your brother's interests, such as |
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balloons in his favorite colors, banners, and streamers. Next, decide on the |
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food and drinks, making sure they are tasty and appropriate for the |
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occasion. Then decide on the other games, music, and entertainment that will |
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make the party memorable. Finally, involve your brother's friends and family |
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to help create the perfect surprise. |
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example_title: birthday party |
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- text: 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo |
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example_title: ice cream |
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- text: >- |
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Start by |
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selecting a scale model of a building that fits the theme. Use a hobby knife |
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and glue to cut and assemble the model into a ruined or abandoned version of |
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itself, adding details like broken windows and graffiti. Create a base for |
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the diorama using foam, plaster, or other materials, and paint it to |
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resemble a ruined street or sidewalk. Add miniature vehicles, debris, and |
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figures to complete the scene, and use weathering techniques like dry |
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brushing and rust washes to add realism. Display the diorama in a shadow box |
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or other protective case to showcase your work. |
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example_title: Miniature diorama creation |
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- text: >- |
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Start by selecting |
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clothing that is futuristic and edgy, such as leather jackets, neon-colored |
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accessories, and tech-inspired patterns. Add accessories like goggles, |
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cybernetic implants, and LED lights to enhance the cyberpunk vibe. Use |
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makeup and body paint to create a futuristic look, such as metallic skin or |
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neon makeup. Consider adding functional elements to your costume, such as a |
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built-in backpack or hidden pockets for your tech gadgets. Finally, practice |
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your confident walk and embrace your inner cyberpunk for a memorable and |
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immersive costume experience. |
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example_title: Cyberpunk costume design |
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- text: >- |
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Start by creating a base |
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terrain with mountains, valleys, and other natural features. Use fractal |
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noise and displacement mapping to add texture and detail to the terrain, and |
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experiment with different materials like rock, grass, and water. Add surreal |
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elements like floating islands, giant mushrooms, or impossible geometry to |
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create a dreamlike atmosphere. Use lighting and color grading to enhance the |
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mood and tone of the scene, and render the final image at a high resolution |
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for maximum impact. Share your surreal landscape with the world and inspire |
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others to explore the possibilities of 3D art. |
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example_title: Surreal 3D landscape creation |
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- text: >- |
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Start by setting a realistic goal and creating a |
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training plan. Build up your mileage gradually over time, and incorporate |
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cross-training and strength exercises to prevent injury and improve |
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endurance. Be sure to stay hydrated and properly fuel your body with |
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nutritious foods. Listen to your body and adjust your training as needed to |
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avoid overexertion or burnout. Finally, taper your training in the weeks |
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leading up to the race to give your body time to rest and recover before the |
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big day. |
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example_title: Marathon training |
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inference: |
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parameters: |
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max_length: 96 |
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num_beams: 4 |
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--- |
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# bart-base-instructiongen |
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Instead of generating questions from text, generate instructions for LLMs! Demo on Spaces [is here](https://huggingface.co/spaces/pszemraj/generate-instructions). |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the pszemraj/fleece2instructions dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0034 |
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- Rouge1: 61.7209 |
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- Rouge2: 45.0116 |
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- Rougel: 59.8188 |
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- Rougelsum: 59.8931 |
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- Gen Len: 14.3179 |
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## Intended uses & limitations |
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This is just a base model/example. There is likely to be even better performance with larger models. |
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Additionally, this was trained on a dataset of **only** instructions+outputs, with the `inputs` filtered out. This means that text of *1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo* will **not** get you *"Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream"*. |
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## Training and evaluation data |
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See the linked dataset `pszemraj/fleece2instructions` - it is a filtered/formatted version of `tatsu-lab/alpaca` to generate instructions for arbitrary text. |
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- Some of the API examples are intentionally weird to demonstrate the generalizability of the model. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.02 |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.2723 | 1.0 | 362 | 1.0325 | 61.6206 | 45.1199 | 59.6467 | 59.7534 | 14.0443 | |
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| 1.0157 | 2.0 | 724 | 1.0034 | 62.4433 | 46.0114 | 60.5355 | 60.6392 | 14.1807 | |