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README.md

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+---
+license: mit
+---
+
+# T5-XXL Embeddings for Stable Diffusion Prompts
+
+This dataset contains 4096-dimensional text embeddings for prompts from the [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset.
+
+## Model Used
+
+The embeddings were generated using a T5-XXL encoder model. Specifically, the weights from `t5xxl_fp8_e4m3fn_scaled.safetensors` were used.
+
+**Important Note:** While the source model weights were in a FP8 format, the embeddings in this dataset have been calculated and stored in full **`float32`** precision to ensure numerical stability and maximum quality.
+
+## Data Fields
+
+- `Prompt`: A `string` containing the original text prompt.
+- `vector`: A `list` of `float32` numbers, representing the 4096-dimensional embedding of the prompt.
+
+## Data Splits
+
+- `train`: Contains 73,718 prompts and their corresponding vectors.
+- `test`: Contains 8192 prompts and their corresponding vectors, sampled from the end of the original train set.
+
+## Usage
+
+You can load the dataset easily using the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("VotreUsername/le-nom-de-votre-dataset")
+
+# Access the splits
+train_data = dataset['train']
+test_data = dataset['test']
+
+# Example
+print(train_data[0]['Prompt'])
+print(len(train_data[0]['vector']))
+# Output: 4096
+
+Please credit the original prompt dataset: Gustavosta/Stable-Diffusion-Prompts.
+The model architecture is based on Google's T5.

test.parquet

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train.parquet

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