Update README.md

README.md

@@ -4,6 +4,7 @@ license: apache-2.0
 datasets:
 - Lakera/gandalf_ignore_instructions
 - christopher/rosetta-code
+- HuggingFaceH4/ultrachat_200k
 language:
 - en
 pipeline_tag: text-classification
@@ -13,6 +14,7 @@ tags:
 - security
 - llm-security
 - distilbert
+base_model: distilbert/distilbert-base-uncased
 ---
 
 # Model Card - Acuvity Prompt Injection
@@ -39,22 +41,22 @@ To combat this, Acuvity Prompt Injection utilizes advanced detection algorithms
 The model operates by positioning itself between the user and the large language model (LLM), intercepting prompts before they reach the LLM. When a prompt is submitted, the model analyzes it to detect any signs of prompt injection. If the model identifies the prompt as safe, it is then forwarded to the LLM for processing. If a prompt injection is detected, the prompt is flagged or blocked, preventing any unintended behavior by the LLM. This approach ensures that only vetted inputs reach the model, thereby enhancing the overall security and reliability of your AI system.
 
 <pre>                     
-                     |                      
-                     |                      
-+-----------+        |         +-----------+
-|           |        |         |           |
-|   USER    |        |         |    LLM    |
-|           |        |         |           |
-+-----+-----+        |         +-----^-----+
-      |              |               |      
-      |              |               |      
-      |      +-----------------+     |      
-      |      |                 |     |      
-      |      |     ACUVITY     |     |      
-      +----->|      PROMPT     +-----+      
-             |    INJECTION    |            
-             |                 |            
-             +-----------------+            
+                      |                      
+                      |                      
++------------+        |         +-----------+
+|            |        |         |           |
+|  USER/APP  |        |         |    LLM    |
+|            |        |         |           |
++-----+------+        |         +-----^-----+
+       |              |               |      
+       |              |               |      
+       |      +-----------------+     |      
+       |      |                 |     |      
+       |      |     ACUVITY     |     |      
+       +----->|      PROMPT     +-----+      
+              |    INJECTION    |            
+              |                 |            
+              +-----------------+            
 </pre>
 
 
@@ -70,7 +72,7 @@ The datasets used in this model, were a mixture of publicly available datasets a
 
 In accordance with licensing requirements, proper attribution is provided as mandated by the specific licenses of the source data. The following is a summary of the licenses and the corresponding number of datasets under each:
 - No License (public domain): 1 datasets
-- MIT License: 1 datasets
+- MIT License: 2 datasets
 
 ## Evaluation metrics
 
@@ -140,4 +142,26 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
    url = "https://rosettacode.org/w/index.php?title=Rosetta_Code&oldid=322370",
    note = "[Online; accessed 8-December-2022]"
  }
+```
+
+```citation
+@misc{ding2023enhancing,
+      title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, 
+      author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou},
+      year={2023},
+      eprint={2305.14233},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+```citation
+@misc{tunstall2023zephyr,
+      title={Zephyr: Direct Distillation of LM Alignment}, 
+      author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
+      year={2023},
+      eprint={2310.16944},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
 ```