Update README.md

README.md

@@ -19,32 +19,21 @@ The model identfies the trigger for the emotion flip of the last utterance in mu
 
 ### Model Description
 
-The model presented here is tailored for the EDiReF shared task at SemEval 2024, specifically addressing Emotion Flip Reasoning (EFR) in English multi-party conversations. By harnessing the capabilities of openchat and leveraging large language models, this approach taps into the power of models like GPT-3.5, adept at comprehending and generating human-like text in diverse conversational contexts.
+The model presented here is tailored for the EDiReF shared task at SemEval 2024, specifically addressing Emotion Flip Reasoning (EFR) in English multi-party conversations.
 
-The model utilizes the strengths of large language models (LLMs) pre-trained on extensive textual data, enabling it to capture complex linguistic patterns and relationships. To enhance its performance for the EFR task, strategic prompt engineering is employed. This involves crafting input prompts that guide the model in identifying trigger utterances responsible for emotion-flips in multi-party conversations.
+The model utilizes the strengths of large language models (LLMs) pre-trained on extensive textual data, enabling it to capture complex linguistic patterns and relationships. To enhance its performance for the EFR task, the model has been finetuned using Quantized Low Rank Adaptation (QLoRA) on the dataset with strategic prompt engineering. This involves crafting input prompts that guide the model in identifying trigger utterances responsible for emotion-flips in multi-party conversations.
 
-In summary, this model excels in pinpointing trigger utterances for emotion-flips in English dialogues, showcasing the effectiveness of openchat, LLM capabilities, and strategic prompt engineering.
+In summary, this model excels in pinpointing trigger utterances for emotion-flips in English dialogues, showcasing the effectiveness of openchat, LLM capabilities, QLoRA and strategic prompt engineering.
 
 
 - **Developed by:** Hasan et al
-- **Shared by [optional]:** Hasan et al
-- **Model type:** Trigger Identifier 
+- **Model type:** LoRA Adapter for openchat_3.5 (Text classification)
 - **Language(s) (NLP):** English
 - **License:** MIT
-- **Finetuned from model [optional]:** OpenChat
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- **Repository:** [Multi-Party-DialoZ](https://github.com/Zuhashaik/Multi-Party-DialoZ)
+- **Paper [Soon]:** [More Information Needed]