Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- text-classification
+- sentiment
+- distilbert
+- sst2
+- fine-tuning
+- transformers
+- huggingface
+pipeline_tag: text-classification
+widget:
+- text: I really enjoyed this movie!
+- text: This was an absolute waste of time.
+datasets:
+- gimmaru/glue-sst2
+base_model:
+- distilbert/distilbert-base-uncased-finetuned-sst-2-english
+---
+
+# SST-2 Demo: DistilBERT fine-tuned on 5% of SST-2
+
+This model is a minimal demonstration of fine-tuning [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) on the [SST-2 (Stanford Sentiment Treebank v2)](https://huggingface.co/datasets/glue/viewer/sst2/train) dataset.
+
+It was trained for **1 epoch on 5% of the training set (\~3,300 examples)** using a consumer GPU (RTX 4060) and mixed-precision (`fp16=True`). The model achieves \~86% validation accuracy in under 10 minutes.
+
+---
+
+## 📊 Evaluation
+
+This model was evaluated on the SST-2 validation set after training for one epoch on 5% of the training data (~3,300 examples).
+
+| Metric     | Value |
+|------------|-------|
+| Accuracy   | ~86%  |
+| Loss       | ~0.35 |
+
+> Evaluated using the Hugging Face `Trainer`'s default compute_metrics function. These results are not representative of full training performance due to the limited data and short training schedule.
+
+---
+
+**Not intended for production use** — this version is trained on a tiny subset of the dataset.
+
+---
+
+## 🗃️ Training details
+
+* **Base model**: distilbert-base-uncased
+* **Dataset**: [GLUE/SST-2](https://huggingface.co/datasets/glue/viewer/sst2/train) (5%)
+* **Hardware**: NVIDIA RTX 4060 Laptop GPU
+* **Batch size**: 32
+* **Epochs**: 1
+* **Precision**: mixed-precision (`fp16`)
+* **Trainer**: Hugging Face `Trainer`
+
+---
+
+## 📎 Files
+
+* `pytorch_model.bin` — model weights
+* `config.json` — architecture details
+* `tokenizer.json`, `vocab.txt`, etc. — tokenizer files from distilbert-base-uncased
+
+---
+
+## ✍️ Author
+
+This model was fine-tuned and published by [@akryshtal](https://huggingface.co/akryshtal) as part of a machine learning engineering demo project.