Update README.md

README.md

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-# nlp_model_training
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-license: apache-2.0
-pipeline_tag: text-classification
-tags:
-- not-for-all-audiences
----
-
----
-license: apache-2.0
-pipeline_tag: text-classification
----
 # Model Card: Fine-Tuned DistilBERT for Offensive/Hate Speech Detection
 
 ## Model Description
@@ -22,11 +10,11 @@ The model, named "distilbert-base-uncased," is pre-trained on a substantial amou
 which allows it to capture semantic nuances and contextual information present in natural language text.
 It has been fine-tuned with meticulous attention to hyperparameter settings, including batch size and learning rate, to ensure optimal model performance for the offensive/hate speech detection task.
 
-During the fine-tuning process, a batch size suitable for efficient computation and learning was chosen.
-Additionally, a learning rate was selected to strike a balance between rapid convergence and steady optimization,
- ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.
+During the fine-tuning process, a batch size of 16 for efficient computation and learning was chosen.
+Additionally, a learning rate (2e-5) was selected to strike a balance between rapid convergence and steady optimization,
+ensuring the model not only learns quickly but also steadily refines its capabilities throughout training.
 
-This model has been trained on a proprietary dataset specifically designed for offensive/hate speech detection.
+This model has been trained on a proprietary dataset < 100k, specifically designed for offensive/hate speech detection.
  The dataset consists of text samples, each labeled as "non-offensive" or "offensive."
 The diversity within the dataset allowed the model to learn to identify offensive content accurately.