Update README.md

README.md

@@ -1,3 +1,125 @@
 ---
 license: apache-2.0
+tags:
+- instruct
+- instructions
+- domain adapt
+- instructiongen
+metrics:
+- rouge
+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
+inference:
+  parameters:
+    max_length: 96
+    num_beams: 4
+datasets:
+- pszemraj/fleece2instructions-inputs-alpaca-cleaned
+language:
+- en
+pipeline_tag: text2text-generation
 ---
+
+
+# bart-base-instructiongen-w-inputs
+
+Use this text2text model to find out what LLM `instruction` (**and** `inputs` if relevant) might have generated `<arbitrary input text>`!
+
+This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the `pszemraj/fleece2instructions-inputs-alpaca-cleaned` dataset.
+
+It achieves the following results on the evaluation set:
+- Loss: 0.9579
+- Rouge1: 62.3604
+- Rouge2: 39.5109
+- Rougel: 58.8843
+- Rougelsum: 60.4494
+- Gen Len: 24.9917
+
+## Intended uses & limitations
+
+This model is intended to be used to generate instructions from arbitrary text. You can then use these instructions + your data to fine-tune an LLM on instructions w.r.t. a specific domain. This model is primarily intended to enable **low-resource domain adaptation**, rather than "_I want to generate even better prompts for the FLAN-V2 dataset!_".
+
+The `fleece2instructions-inputs-alpaca-cleaned` dataset, obtained from the [alpaca-lora repo](https://github.com/tloen/alpaca-lora) under the ODC-BY license, has been converted to a text2text format for use with language models. In this dataset, the original 'inputs' and 'instructions' columns are combined into a single 'instructions_inputs' column. To clearly separate the two types of content, each piece of text is prefixed with either an `<instruction>` or `<inputs>` token. These tokens not only facilitate model comprehension, but also allow for easy regex separation of model outputs during inference. 
+
+As such, users can expect the output of this model to be similarly structured with `<instruction>` and `<inputs>` tokens.
+
+## Training and evaluation data
+
+Refer to the [fleece2instructions-inputs-alpaca-cleaned](https://huggingface.co/datasets/pszemraj/fleece2instructions-inputs-alpaca-cleaned) dataset
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 8e-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: 0.02
+- num_epochs: 2.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
+| 1.1147        | 1.0   | 680  | 0.9901          | 61.8451 | 38.8293 | 58.3372 | 59.8658   | 25.2401 |
+| 0.9565        | 2.0   | 1360 | 0.9579          | 62.3604 | 39.5109 | 58.8843 | 60.4494   | 24.9917 |
+