Update README.md

README.md

@@ -7,28 +7,43 @@ datasets:
 model-index:
 - name: distilgpt2-multiprompt-v2-fp
   results: []
+widget:
+ - text: "morning sun over Jakarta"
+   example_title: "morning sun"
+ - text: "WARNING: pip is"
+   example_title: "pip"
+ - text: "sentient cheese"
+   example_title: "sentient cheese"
+ - text: "cheeps are"
+   example_title: "cheeps"
+ - text: "avocado armchair"
+   example_title: "creative prompt"
+ - text: "Landscape of"
+   example_title: "landscape"
+parameters:
+  min_length: 16
+  max_length: 96
+  no_repeat_ngram_size: 1
+  do_sample: True
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# distilgpt2-multiprompt
 
-# distilgpt2-multiprompt-v2-fp
+Generate/augment your prompt with a model trained on a large & diverse prompt dataset.
 
 This model is a fine-tuned version of [pszemraj/distilgpt2-multiprompt-v1](https://huggingface.co/pszemraj/distilgpt2-multiprompt-v1) on the pszemraj/text2image-prompts-multi dataset.
 It achieves the following results on the evaluation set:
 - Loss: 2.0213
+- perplexity              =     7.55
 
-## Model description
-
-More information needed
 
 ## Intended uses & limitations
 
-More information needed
+- The model will generate augmentations that are biased towards the training data, i.e. what people already asked for in the SD/midjourney discords, etc. Creating a larger dataset was an attempt at mitigating this through more data from different datasets.
 
 ## Training and evaluation data
 
-More information needed
+See the `pszemraj/text2image-prompts-multi` dataset card for details. The dataset is a compilation of several text-to-image prompt datasets on huggingface  :)
 
 ## Training procedure