Update README.md

README.md

@@ -32,8 +32,8 @@ model-index:
 * **Base Model:** Qwen/Qwen3-Embedding-0.6B
 * **Model Type:** Embedding Model
 * **Fine-tuning Method:** Low-Rank Adaptation (LoRA)
-* **Developer:** [https://github.com/gauravprasadgp]
-* **Contact:** [https://github.com/gauravprasadgp]
+* **Developer:** https://github.com/gauravprasadgp
+* **Contact:** https://github.com/gauravprasadgp
 * **Date:** July 13, 2025
 
 ---
@@ -63,30 +63,28 @@ This model is intended for:
 * **Domain Specificity:** While fine-tuned, the model's performance may degrade on data significantly different from its training distribution.
 * **Inherited Biases:** As it is based on a pre-trained model, it may inherit biases present in the original training data. Users should be aware of potential biases related to gender, race, religion, etc., and test for them in their specific applications.
 * **Computational Resources:** While LoRA reduces resource demands for fine-tuning, inference still requires appropriate computational resources.
-* **Language:** Primarily designed for [Specify Language(s) if known, e.g., English] text. Performance on other languages may vary.
+* **Language:** Primarily designed for English text. Performance on other languages may vary.
 
 ---
 
 ## Training Details
 
 * **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
-* **LoRA Rank (r):** [Specify LoRA rank used, e.g., 8, 16, 32]
+* **LoRA Rank (r):** 16
 * **LoRA Alpha (alpha):** [Specify LoRA alpha used, e.g., 16, 32, 64]
 * **Target Modules for LoRA:** [Specify which modules were targeted, e.g., `q_proj`, `k_proj`, `v_proj`, `out_proj`]
-* **Training Dataset:** [Brief description of your training dataset, e.g., "A proprietary dataset of customer reviews," "A collection of scientific abstracts," "Paragraphs from Wikipedia articles."]
-    * **Dataset Size:** [Number of samples in your training dataset]
+* **Training Dataset:** Semantic Similar Sentences
+    * **Dataset Size:** 100
     * **Dataset Characteristics:** [Any relevant details about the dataset, e.g., "Contains highly technical language," "Focuses on conversational text," "Balanced across different topics."]
 * **Training Hardware:** [e.g., NVIDIA A100 GPU, Google Cloud TPU v3]
 * **Training Time:** [e.g., 4 hours, 2 days]
-* **Optimization Strategy:** [e.g., AdamW, learning rate schedule]
-* **Software Frameworks:** [e.g., PyTorch, Hugging Face Transformers, PEFT library]
+* **Optimization Strategy:** AdamW
+* **Software Frameworks:** PyTorch, Hugging Face Transformers, PEFT library
 
 ---
 
 ## Performance Metrics
 
-*(Note: Provide actual metrics from your evaluation. Examples below are placeholders.)*
-
 * **Evaluation Dataset:** [Brief description of your evaluation dataset]
 * **Metric 1 (e.g., Average Precision @ K):** [Value] (e.g., 0.85)
 * **Metric 2 (e.g., Recall @ K):** [Value] (e.g., 0.92)