Correcting data points in README

README.md

@@ -14,7 +14,7 @@ annotations_creators:
 language_details: "en-US"  # Example: en-US
 pretty_name: "LLMFineTuningBench" 
 size_categories:
-- ">15K"  # Example: n<1K, 100K<n<1M, …
+- ">30K"  # Example: n<1K, 100K<n<1M, …
 task_categories:  # Full list at https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/pipelines.ts
 - "tabular-regression"  # Example: question-answering
 - "tabular-classification"  # Example: image-classification
@@ -30,7 +30,7 @@ configs:  # Optional. This can be used to pass additional parameters to the data
 
 # Dataset Card for LLMFineTuningBench
 
-A dataset of over 15,000 LLM fine-tuning experiments, capturing detailed performance metrics from jobs run on high-performance computing (HPC) clusters. It spans a wide range of models, fine-tuning methods, and hardware configurations, and is intended to support research on predictive resource allocation, performance optimization, and cost estimation for LLM fine-tuning workloads.
+A dataset of over 30,000 LLM fine-tuning experiments, capturing detailed performance metrics from jobs run on high-performance computing (HPC) clusters. It spans a wide range of models, fine-tuning methods, and hardware configurations, and is intended to support research on predictive resource allocation, performance optimization, and cost estimation for LLM fine-tuning workloads.
 
 ## Dataset Details
 
@@ -85,7 +85,7 @@ Other columns capture additional metrics such as GPU memory utilization, GPU com
 
 #### Data Collection and Processing
 
-We benchmarked **15,154 jobs**, varying parameters of `SFTTrainer` including the base model, fine-tuning method, number of GPUs, and batch size. Training data consisted of random text, since the primary goal was to measure computational performance rather than model accuracy — the resulting fine-tuned model weights were discarded after each run.
+We benchmarked **30,190 jobs**, varying parameters of `SFTTrainer` including the base model, fine-tuning method, number of GPUs, and batch size. Training data consisted of random text, since the primary goal was to measure computational performance rather than model accuracy — the resulting fine-tuned model weights were discarded after each run.
 
 ## Citation [optional]