Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- google-t5/t5-base
+pipeline_tag: summarization
+---
+
+**Model Name:** LoRA Fine-Tuned Model for Dialogue Summarization  
+**Model Type:** Seq2Seq with Low-Rank Adaptation (LoRA)  
+**Base Model:** `google/t5-base` 
+
+## Model Details
+- **Architecture**: T5-base
+- **Finetuning Technique**: LoRA (Low-Rank Adaptation)
+- **PEFT Method**: Parameter Efficient Fine-Tuning
+- **Data**: samsumdataset
+- **Metrics**: Evaluated using ROUGE (ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-Lsum)
+
+## Intended Use
+This model is designed for summarizing dialogues, such as conversations between individuals in a chat or messaging context. It’s suitable for applications in:
+- **Customer Service**: Summarizing chat logs for quality monitoring or training.
+- **Messaging Apps**: Generating conversation summaries for user convenience.
+- **Content Creation**: Assisting writers by summarizing character dialogues.
+
+## Training Process
+
+Optimizer: AdamW with learning rate 3e-5
+
+Batch Size: 4 (gradient accumulation steps of 2)
+
+Training Epochs: 2
+
+Evaluation Metrics: ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-Lsum
+
+Hardware: Trained on a single GPU with mixed precision to optimize performance.
+
+The model was trained using the Seq2SeqTrainer class from transformers, with LoRA parameters applied to selected attention layers to reduce computation without compromising accuracy.