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README.md

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-license: apache-2.0
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+# PL-BERT Fine-Tuned on Hindi Wikipedia Dataset
+
+This model is a fine-tuned version of **PL-BERT**, specifically trained on the Hindi subset of the Wiki40b dataset. The model has been optimized to understand and generate high-quality Hindi text, making it suitable for various NLP tasks in the Hindi language.
+For more information about this model, check out the [GitHub](https://github.com/Ionio-io/PL-BERT-Fine-Tuned-hi-) repository.
+
+## Model Overview
+
+- **Model Name:** PL-BERT (Fine-tuned on Hindi)
+- **Base Model:** PL-BERT (Multilingual BERT variant)
+- **Dataset:** Hindi subset from Wiki40b (51,000 cleaned Wikipedia articles)
+- **Precision:** Mixed precision (FP16)
+
+The fine-tuning process focused on improving the model's ability to handle Hindi text more effectively by leveraging a large, cleaned corpus of Wikipedia articles in Hindi.
+
+## Training Details
+
+- **Model:** PL-BERT
+- **Dataset:** Hindi subset from Wiki40b
+- **Batch Size:** 64
+- **Mixed Precision:** FP16
+- **Optimizer:** AdamW
+- **Training Steps:** 15,000
+
+### Training Progress
+
+- **Final Loss:** 1.879
+- **Vocabulary Loss:** 0.49
+- **Token Loss:** 1.465
+
+### Validation Results
+
+During training, we monitored performance with validation metrics:
+
+- **Validation Loss:** 1.879
+- **Vocabulary Accuracy:** 78.54%
+- **Token Accuracy:** 82.30%
+
+
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+license: apache-2.0
+---