Update README.md

README.md

@@ -5,30 +5,32 @@ tags:
 model-index:
 - name: chatgpt-prompts-bart-long
   results: []
+datasets:
+- fka/awesome-chatgpt-prompts
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
 
-# chatgpt-prompts-bart-long
+# ChatGPT Prompt Generator
 
-This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
+This model is a fine-tuned version of [BART-large](https://huggingface.co/facebook/bart-large) on a ChatGPT prompts dataset.
 It achieves the following results on the evaluation set:
 - Train Loss: 2.8329
 - Validation Loss: 2.5015
 - Epoch: 4
 
-## Model description
-
-More information needed
-
 ## Intended uses & limitations
 
-More information needed
+You can use this to generate ChatGPT personas. Simply input a persona like below:
+
+```
+from transformers import BartForConditionalGeneration, BartTokenizer
 
-## Training and evaluation data
+example_english_phrase = "photographer"
+batch = tokenizer(example_english_phrase, return_tensors="pt")
+generated_ids = model.generate(batch["input_ids"], max_new_tokens=150)
+output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+```
 
-More information needed
 
 ## Training procedure
 
@@ -54,4 +56,4 @@ The following hyperparameters were used during training:
 - Transformers 4.26.0
 - TensorFlow 2.9.2
 - Datasets 2.8.0
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2