Update README.md

README.md

@@ -20,8 +20,21 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the generator dataset.
 It achieves the following results on the evaluation set:
+
 - Loss: 0.4605
 
+Perplexity of 10.40
+
+Perplexity Article: https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf 
+https://medium.com/@AyushmanPranav/perplexity-calculation-in-nlp-0699fbda4594
+
+ The perplexity of 10.40 achieved on the dataset indicates that the fine-tuned Mistral-7B model reasonably understands natural language and SQL syntax. 
+ However, further evaluation using task-specific metrics is necessary to assess the model's effectiveness in real-world scenarios. 
+ By combining quantitative metrics like perplexity with qualitative analysis of generated queries, 
+ we can comprehensively understand the model's strengths and weaknesses, ultimately 
+ leading to improved performance and more reliable text-to-SQL translation capabilities. 
+ 
+
 Dataset : [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
 
 ## Model description
@@ -59,7 +72,7 @@ args = TrainingArguments(
     gradient_accumulation_steps=8,      #2  # number of steps before performing a backward/update pass
     gradient_checkpointing=True,            # use gradient checkpointing to save memory
     optim="adamw_torch_fused",              # use fused adamw optimizer
-    logging_steps=10,                       # log every 10 steps
+    logging_steps=10,                       # log every ten steps
     #save_strategy="epoch",                  # save checkpoint every epoch
     learning_rate=2e-4,                     # learning rate, based on QLoRA paper
     bf16=True,                              # use bfloat16 precision