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README.md
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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model-index:
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- name: flan-t5-base-
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results:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# flan-t5-base-
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Rougelsum: 56.9171
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- Gen Len: 13.1493
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##
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More information needed
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## Training and evaluation data
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license: apache-2.0
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tags:
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- generated_from_trainer
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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: flan-t5-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: 58.9516
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widget:
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- text: >-
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You'll need to start by choosing the right venue. Consider the type of
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atmosphere and the size of the area that will be suitable for the number
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of guests you plan to invite. Choose the right decorations based on your
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brother's interests, such as balloons in his favorite colors, banners, and
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streamers. Next, decide on the food and drinks, making sure they are tasty
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and appropriate for the occasion. Then decide on the other games, music,
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and entertainment that will make the party memorable. Finally, involve
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your brother's friends and family 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 selecting a scale model of a building that fits the theme. Use a
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hobby knife and glue to cut and assemble the model into a ruined or
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abandoned version of itself, adding details like broken windows and
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graffiti. Create a base for the diorama using foam, plaster, or other
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materials, and paint it to resemble a ruined street or sidewalk. Add
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miniature vehicles, debris, and figures to complete the scene, and use
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weathering techniques like dry brushing and rust washes to add realism.
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Display the diorama in a shadow box or other protective case to showcase
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your work.
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example_title: Miniature diorama creation
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- text: >-
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Start by selecting clothing that is futuristic and edgy, such as leather
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jackets, neon-colored accessories, and tech-inspired patterns. Add
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accessories like goggles, cybernetic implants, and LED lights to enhance
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the cyberpunk vibe. Use makeup and body paint to create a futuristic look,
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such as metallic skin or neon makeup. Consider adding functional elements
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to your costume, such as a built-in backpack or hidden pockets for your
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tech gadgets. Finally, practice your confident walk and embrace your inner
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cyberpunk for a memorable and 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 terrain with mountains, valleys, and other
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natural features. Use fractal noise and displacement mapping to add
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texture and detail to the terrain, and experiment with different materials
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like rock, grass, and water. Add surreal elements like floating islands,
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giant mushrooms, or impossible geometry to create a dreamlike atmosphere.
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Use lighting and color grading to enhance the mood and tone of the scene,
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and render the final image at a high resolution for maximum impact. Share
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your surreal landscape with the world and inspire others to explore the
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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 training plan. Build up
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your mileage gradually over time, and incorporate cross-training and
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strength exercises to prevent injury and improve endurance. Be sure to
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stay hydrated and properly fuel your body with nutritious foods. Listen to
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your body and adjust your training as needed to avoid overexertion or
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burnout. Finally, taper your training in the weeks leading up to the race
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to give your body time to rest and recover before the big day.
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example_title: Marathon training
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---
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# flan-t5-base-instructiongen
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Instead of generating questions from text, generate instructions for LLMs!
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Rougelsum: 56.9171
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- Gen Len: 13.1493
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## Intended uses & limitations
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> Of the three models fine-tuned so far, `flan-t5-base` is in an awkward position where it has the largest model file size, but not the best performance. I'd recommend looking at the two linked below.
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This is just a `base` FLAN model, and is mostly uploaded for comparison with the [FLAN-small](https://huggingface.co/pszemraj/flan-t5-small-instructiongen) and [bart-base](https://huggingface.co/pszemraj/bart-base-instructiongen) variants.
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Additionally, it 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 and evaluation data
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