Add README.md with model description

README.md

@@ -0,0 +1,28 @@
+# 🧠 Sentiment Analysis Model — DistilBERT Fine-Tuned on IMDb 🎬
+
+This model is a fine-tuned version of [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) on the [IMDb movie review dataset](https://huggingface.co/datasets/imdb) for **binary sentiment classification** (positive/negative). It was trained using Hugging Face Transformers and PyTorch.
+
+## 🔍 Intended Use
+
+This model is designed to classify movie reviews (or other English text) as **positive** or **negative** sentiment. It's ideal for:
+- Opinion mining
+- Social media analysis
+- Review classification
+- Text classification demos
+
+## 🧪 Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "bmdavis/my-language-model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "This movie was amazing and really well-acted!"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+prediction = torch.argmax(outputs.logits).item()
+
+print("Sentiment:", "Positive" if prediction == 1 else "Negative")