Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+- text-classification
+- spoiler-detection
+- bert
+- imdb
+datasets:
+- imdb-review-dataset
+metrics:
+- accuracy
+- f1
+model-index:
+- name: bert-base-spoiler-detection
+  results:
+  - task:
+      type: text-classification
+      name: Spoiler Detection
+    dataset:
+      name: IMDB Review Dataset
+      type: imdb-reviews
+    metrics:
+    - type: accuracy
+      value: 0.76
+      name: Test Accuracy
+---
+
+# BERT Base Spoiler Detection
+
+## Model Description
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) for detecting spoilers in movie and TV show reviews. It classifies reviews as either containing spoilers or being spoiler-free.
+
+**Developed by:** Tyler Jordan  
+**Model type:** Text Classification  
+**Language:** English  
+**License:** MIT  
+**Base model:** bert-base-uncased  
+
+## Intended Use
+
+### Primary Use Case
+Automatically detect spoilers in user-generated movie and TV show reviews to warn readers before they encounter plot-revealing content.
+
+### Intended Users
+- Movie review platforms
+- Content moderation systems
+- Personal projects for filtering spoilers
+
+### Out-of-Scope Uses
+- Reviews in languages other than English
+- Non-entertainment content (news, academic papers, etc.)
+- Legal or medical content requiring high accuracy
+
+## Training Data
+
+**Dataset:** [IMDB Review Dataset](https://www.kaggle.com/datasets/ebiswas/imdb-review-dataset) by Enam Biswas (2021)
+
+**Preprocessing:**
+- Sampled 200,000 balanced reviews (100k spoilers, 100k non-spoilers) from 5.5M total reviews
+- Train/Validation/Test split: 140k/30k/30k (70%/15%/15%)
+- Text cleaning: HTML tag removal, whitespace normalization
+- Minimum review length: 30 characters
+- Maximum sequence length: 512 tokens
+
+**Class Distribution:**
+- Spoiler: 50%
+- Non-spoiler: 50%
+
+## Training Procedure
+
+### Training Hyperparameters
+
+- **Optimizer:** AdamW
+- **Learning rate:** 1e-5
+- **Batch size:** 32
+- **Epochs:** 5
+- **Max sequence length:** 512
+- **Dropout:** 0.3
+- **Weight decay:** 0.01
+- **Warmup steps:** 10% of total steps
+- **Learning rate schedule:** Linear warmup with decay
+
+### Training Hardware
+- GPU: NVIDIA T4 (Google Colab)
+- Training time: ~2-3 hours
+
+### Framework
+- PyTorch 2.5.1
+- Transformers 4.x
+- CUDA 12.1
+
+## Evaluation
+
+### Metrics
+
+| Metric | Value |
+|--------|-------|
+| Test Accuracy | 76.0% |
+| Validation Accuracy | 76.3% |
+
+### Evaluation Data
+- 30,000 held-out reviews from the IMDB dataset
+- Balanced split (50% spoilers, 50% non-spoilers)