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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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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-small-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: 52.201 |
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widget: |
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- text: >- |
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To plan a successful surprise birthday party, 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 |
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invite. Choose the right decorations based on your brother's interests, |
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such as balloons in his favorite colors, banners, and streamers. Next, |
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decide on the food and drinks, making sure they are tasty and appropriate |
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for the occasion. Then decide on the other games, music, and entertainment |
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that will make the party memorable. Finally, involve your brother's |
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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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To create a miniature diorama of a post-apocalyptic cityscape, start by |
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selecting a scale model of a building that fits the theme. Use a hobby |
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knife and glue to cut and assemble the model into a ruined or abandoned |
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version of itself, adding details like broken windows and graffiti. Create |
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a base for the diorama using foam, plaster, or other materials, and paint |
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it to resemble a ruined street or sidewalk. Add miniature vehicles, |
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debris, and figures to complete the scene, and use weathering techniques |
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like dry brushing and rust washes to add realism. Display the diorama in a |
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shadow box 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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To create a costume inspired by the world of cyberpunk, start by selecting |
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clothing that is futuristic and edgy, such as leather jackets, |
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neon-colored accessories, and tech-inspired patterns. Add accessories like |
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goggles, cybernetic implants, and LED lights to enhance the cyberpunk |
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vibe. Use makeup and body paint to create a futuristic look, such as |
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metallic skin or neon makeup. Consider adding functional elements to your |
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costume, such as a built-in backpack or hidden pockets for your tech |
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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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To create a surreal landscape using 3D software, 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, |
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and experiment with different materials like rock, grass, and water. Add |
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surreal elements like floating islands, giant mushrooms, or impossible |
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geometry to create a dreamlike atmosphere. Use lighting and color grading |
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to enhance the mood and tone of the scene, and render the final image at a |
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high resolution for maximum impact. Share your surreal landscape with the |
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world and inspire 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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To train for a marathon, 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 |
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to avoid overexertion or burnout. Finally, taper your training in the |
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weeks leading up to the race to give your body time to rest and recover |
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before the big day. |
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example_title: Marathon training |
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--- |
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# flan-t5-small-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-small](https://huggingface.co/google/flan-t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3401 |
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- Rouge1: 52.201 |
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- Rouge2: 35.6154 |
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- Rougel: 50.2334 |
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- Rougelsum: 50.338 |
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- Gen Len: 14.0450 |
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## Intended uses & limitations |
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This is just a **small** model/example. There is likely to be even better performance with larger models (ex [pszemraj/bart-base-instructiongen)](https://huggingface.co/pszemraj/bart-base-instructiongen) generalizes better) |
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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: 16 |
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- total_train_batch_size: 128 |
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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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| 1.6161 | 1.0 | 181 | 1.3714 | 51.1003 | 34.5701 | 49.1277 | 49.2466 | 13.8357 | |
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| 1.539 | 2.0 | 362 | 1.3401 | 52.201 | 35.6154 | 50.2334 | 50.338 | 14.0450 | |
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