Update README.md

README.md

@@ -1,5 +1,29 @@
 # 🧠 Sentiment Analysis Model — DistilBERT Fine-Tuned on IMDb 🎬
-
+language: en
+license: apache-2.0
+tags:
+  - sentiment-analysis
+  - text-classification
+  - transformers
+  - distilbert
+datasets:
+  - imdb
+metrics:
+  - accuracy
+model-index:
+  - name: DistilBERT IMDb Sentiment Classifier
+    results:
+      - task:
+          name: Sentiment Analysis
+          type: text-classification
+        dataset:
+          name: IMDb
+          type: imdb
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.88  # You can update this with your actual score later
+            
 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
@@ -26,3 +50,39 @@ outputs = model(**inputs)
 prediction = torch.argmax(outputs.logits).item()
 
 print("Sentiment:", "Positive" if prediction == 1 else "Negative")
+
+📊 Dataset
+IMDb Dataset
+
+25,000 training samples
+
+25,000 test samples
+
+Labels: 0 = Negative, 1 = Positive
+
+🧠 Model Details
+Base Model: distilbert-base-uncased
+
+Architecture: Transformer (BERT-like)
+
+Framework: PyTorch
+
+Tokenizer: WordPiece
+
+🛠️ Training
+Epochs: 3
+
+Batch Size: 8
+
+Optimizer: AdamW
+
+Loss: CrossEntropy
+
+Trainer API used
+
+🔐 License
+This model is released under the Apache 2.0 license.
+
+✍️ Author
+Created by Brody Davis (@bmdavis)
+Trained and uploaded using Hugging Face Hub and Transformers