Update README.md

README.md

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-license: mit
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+---
+license: mit
+datasets:
+- stanfordnlp/sst2
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- DornierDo17/RoBERTa_17.7M
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+- '#sst2'
+- '#text-classification'
+- '#ai'
+---
+
+
+
+This model is a fine-tuned version of my base MiniRoBERTa (17.7M parameters) model. The goal of this fine-tuning experiment was to demonstrate that a RoBERTa-style model, built entirely from scratch and trained on a single GPU with limited compute, can still learn meaningful patterns and adapt effectively to downstream tasks.
+
+The model was fine-tuned on the SST-2 sentiment classification dataset and achieved an accuracy of 80%, which is a strong result given the scale and simplicity of the pretraining setup.
+
+This validates that the model has learned generalizable representations and can be adapted successfully to real-world NLP tasks through fine-tuning.
+
+#### Highlights:
+- Fine-tuned from scratch-trained RoBERTa (17.7M) model
+- Dataset: SST-2 (Stanford Sentiment Treebank)
+- Accuracy: **80%**
+- Trained on: Single GPU (limited compute)