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README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- text-classification
+- text-classification
+- sst2
+- fine-tuned
+language:
+- en
+datasets:
+- sst2
+pipeline_tag: text-classification
+---
+
+# bert-medium-tiny
+
+## Model Description
+
+Fine-tuned BERT model for sentiment classification on SST-2 dataset
+
+## Base Model
+- **Base Model**: [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)
+- **Task**: text-classification
+- **Dataset**: sst2
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("takedarn/bert-medium-tiny")
+model = AutoModelForSequenceClassification.from_pretrained("takedarn/bert-medium-tiny")
+
+text = "This movie is great!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1)
+    
+print(f"Predicted class: {predicted_class.item()}")
+```
+
+## Training Details
+
+This model was fine-tuned using the following configuration:
+- Task: text-classification
+- Dataset: sst2
+- Base model: google-bert/bert-base-uncased
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{bert_medium_tiny,
+  author = {Your Name},
+  title = {bert-medium-tiny},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/takedarn/bert-medium-tiny}
+}
+```