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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5566fae42b06dd0ca8ff52abf56ca4a459b2bfac
--- /dev/null
+++ b/README.md
@@ -0,0 +1,195 @@
+---
+license: cc-by-4.0
+task_categories:
+- text-classification
+tags:
+- screenplay
+- narrative
+- salience
+- linguistics
+language:
+- en
+size_categories:
+- 100K<n<1M
+---
+
+# Screenplay Scene Salience Features
+
+Pre-extracted linguistic and narrative features for screenplay scene salience detection from the MENSA dataset.
+
+## Dataset Description
+
+This dataset contains **913 linguistic features** extracted from movie screenplays in the MENSA dataset. Features are organized into **24 feature groups** covering various aspects of linguistic, narrative, and discourse analysis.
+
+### Dataset Statistics
+
+| Split | Samples | Size |
+|-------|---------|------|
+| Train | 117,503 | 172.9 MB |
+| Validation | 8,052 | 16.1 MB |
+| Test | 8,156 | 16.1 MB |
+| **Total** | **133,711** | **140.1 MB** |
+
+### Feature Groups (24 groups)
+
+- `base`
+- `bert_surprisal`
+- `character_arcs`
+- `emotional`
+- `gc_academic`
+- `gc_basic`
+- `gc_char_diversity`
+- `gc_concreteness`
+- `gc_dialogue`
+- `gc_discourse`
+- `gc_narrative`
+- `gc_polarity`
+- `gc_pos`
+- `gc_pronouns`
+- `gc_punctuation`
+- `gc_readability`
+- `gc_syntax`
+- `gc_temporal`
+- `ngram`
+- `ngram_surprisal`
+- `plot_shifts`
+- `rst`
+- `structure`
+- `surprisal`
+
+## Usage
+
+### Option 1: Load with Hugging Face datasets (Recommended)
+
+```python
+from datasets import load_dataset
+
+# Load a single feature group
+ds = load_dataset("YOUR_USERNAME/screenplay-features", data_files="train/base.parquet")
+df = ds['train'].to_pandas()
+
+# Load multiple groups for training
+ds = load_dataset("YOUR_USERNAME/screenplay-features", 
+                  data_files={
+                      "train": ["train/base.parquet", "train/gc_polarity.parquet", "train/emotional.parquet"]
+                  })
+df = ds['train'].to_pandas()
+
+# Load all splits for evaluation
+ds = load_dataset("YOUR_USERNAME/screenplay-features",
+                  data_files={
+                      "train": "train/gc_polarity.parquet",
+                      "validation": "validation/gc_polarity.parquet",
+                      "test": "test/gc_polarity.parquet"
+                  })
+```
+
+### Option 2: Load with pandas directly
+
+```python
+import pandas as pd
+
+# From HuggingFace URL
+df = pd.read_parquet("hf://datasets/YOUR_USERNAME/screenplay-features/train/base.parquet")
+
+# Or if you have the repo cloned locally
+df = pd.read_parquet("train/base.parquet")
+```
+
+### Option 3: Use custom loader (Easiest)
+
+```python
+from feature_cache.load_hf import load_groups
+
+# Load features and labels
+X, y = load_groups(
+    groups=["base", "gc_polarity", "emotional", "rst"],
+    split="train",
+    hf_repo="YOUR_USERNAME/screenplay-features"
+)
+
+# Load features only (no labels)
+X = load_groups(
+    groups=["base", "gc_polarity"],
+    split="test",
+    include_label=False,
+    hf_repo="YOUR_USERNAME/screenplay-features"
+)
+```
+
+## Data Structure
+
+Each parquet file contains:
+
+- **`movie_id`** (string): Unique movie identifier
+- **`scene_index`** (int): Scene index within the movie (0-indexed)
+- **`label`** (int): Salience label
+  - `0` = Non-salient scene
+  - `1` = Salient scene
+- **Feature columns**: Various linguistic/narrative features (float/int)
+
+### Example row structure:
+
+| movie_id | scene_index | label | feature_1 | feature_2 | ... |
+|----------|-------------|-------|-----------|-----------|-----|
+| tt0111161 | 42 | 1 | 0.85 | 12.3 | ... |
+
+## Feature Categories
+
+The features are organized into the following categories:
+
+### Base Features
+- Basic linguistic statistics (token count, sentence count, etc.)
+- Structural position features (act, scene positions)
+
+### GenreClassifier (GC) Features
+- **gc_basic**: Basic linguistic metrics
+- **gc_char_diversity**: Character diversity metrics  
+- **gc_concreteness**: Concreteness scores
+- **gc_dialogue**: Dialogue-specific features
+- **gc_discourse**: Discourse markers and connectives
+- **gc_narrative**: Narrative structure features
+- **gc_polarity**: Sentiment polarity scores
+- **gc_pos**: Part-of-speech distributions
+- **gc_pronouns**: Pronoun usage patterns
+- **gc_punctuation**: Punctuation statistics
+- **gc_readability**: Readability metrics
+- **gc_syntax**: Syntactic complexity features
+- **gc_temporal**: Temporal expressions
+
+### Narrative Features
+- **character_arcs**: Character development metrics
+- **plot_shifts**: Plot progression indicators
+- **structure**: Narrative structure features
+- **emotional**: Emotional arc features
+
+### Linguistic Features
+- **ngram**: N-gram diversity metrics
+- **rst**: Rhetorical Structure Theory features
+- **bert_surprisal**: BERT-based surprisal scores
+- **ngram_surprisal**: N-gram-based surprisal
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{mensa2021,
+    title={MENSA: A Multi-Domain Dataset for Narrative Understanding},
+    author={...},
+    booktitle={...},
+    year={2021}
+}
+```
+
+## License
+
+CC-BY-4.0 (following MENSA dataset license)
+
+## Acknowledgments
+
+Features extracted using:
+- GenreClassifier toolkit
+- spaCy for NLP processing
+- BERT models for surprisal estimation
+- RST parser for discourse analysis
diff --git a/example_usage.py b/example_usage.py
new file mode 100644
index 0000000000000000000000000000000000000000..45ace54720cc53319858ff3617653240213950c0
--- /dev/null
+++ b/example_usage.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+"""
+Example usage of screenplay salience features from Hugging Face.
+"""
+
+from datasets import load_dataset
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import classification_report
+
+# ============================================================================
+# Method 1: Load with datasets library
+# ============================================================================
+
+print("Loading training data...")
+ds = load_dataset(
+    "YOUR_USERNAME/screenplay-features",
+    data_files={
+        "train": ["train/base.parquet", "train/gc_polarity.parquet"],
+        "test": ["test/base.parquet", "test/gc_polarity.parquet"]
+    }
+)
+
+train_df = ds['train'].to_pandas()
+test_df = ds['test'].to_pandas()
+
+# Separate features and labels
+feature_cols = [c for c in train_df.columns if c not in ["movie_id", "scene_index", "label"]]
+
+X_train = train_df[feature_cols]
+y_train = train_df["label"]
+
+X_test = test_df[feature_cols]
+y_test = test_df["label"]
+
+print(f"Train: {len(X_train)} samples, {len(feature_cols)} features")
+print(f"Test: {len(X_test)} samples")
+
+# ============================================================================
+# Method 2: Use custom loader (easier)
+# ============================================================================
+
+# If you have the feature_cache module:
+# from feature_cache.load_hf import load_groups
+#
+# X_train, y_train = load_groups(
+#     groups=["base", "gc_polarity", "emotional"],
+#     split="train",
+#     hf_repo="YOUR_USERNAME/screenplay-features"
+# )
+#
+# X_test, y_test = load_groups(
+#     groups=["base", "gc_polarity", "emotional"],
+#     split="test",
+#     hf_repo="YOUR_USERNAME/screenplay-features"
+# )
+
+# ============================================================================
+# Train a simple model
+# ============================================================================
+
+print("\nTraining logistic regression...")
+clf = LogisticRegression(max_iter=1000, random_state=42)
+clf.fit(X_train.fillna(0), y_train)
+
+# Evaluate
+y_pred = clf.predict(X_test.fillna(0))
+print("\nTest Results:")
+print(classification_report(y_test, y_pred, target_names=["Non-salient", "Salient"]))
+
+# ============================================================================
+# Explore features
+# ============================================================================
+
+print("\nTop 10 features by coefficient:")
+feature_importance = pd.DataFrame({
+    'feature': feature_cols,
+    'coefficient': clf.coef_[0]
+}).sort_values('coefficient', key=abs, ascending=False)
+
+print(feature_importance.head(10))
diff --git a/metadata.json b/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..73ff7700c7d302b4eb94c93f5655b3ad16d5387f
--- /dev/null
+++ b/metadata.json
@@ -0,0 +1,402 @@
+{
+  "splits": [
+    "train",
+    "validation",
+    "test"
+  ],
+  "groups": [
+    "base",
+    "bert_surprisal",
+    "character_arcs",
+    "emotional",
+    "gc_academic",
+    "gc_basic",
+    "gc_char_diversity",
+    "gc_concreteness",
+    "gc_dialogue",
+    "gc_discourse",
+    "gc_narrative",
+    "gc_polarity",
+    "gc_pos",
+    "gc_pronouns",
+    "gc_punctuation",
+    "gc_readability",
+    "gc_syntax",
+    "gc_temporal",
+    "ngram",
+    "ngram_surprisal",
+    "plot_shifts",
+    "rst",
+    "structure",
+    "surprisal"
+  ],
+  "stats": {
+    "train": {
+      "base": {
+        "n_samples": 117503,
+        "n_features": 21,
+        "size_mb": 7.204872131347656
+      },
+      "bert_surprisal": {
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+        "n_features": 29,
+        "size_mb": 15.838018417358398
+      },
+      "character_arcs": {
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+        "n_features": 4,
+        "size_mb": 0.20000839233398438
+      },
+      "emotional": {
+        "n_samples": 117503,
+        "n_features": 5,
+        "size_mb": 4.4886064529418945
+      },
+      "gc_academic": {
+        "n_samples": 117503,
+        "n_features": 8,
+        "size_mb": 0.6636857986450195
+      },
+      "gc_basic": {
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+        "n_features": 3,
+        "size_mb": 1.3420581817626953
+      },
+      "gc_char_diversity": {
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+        "n_features": 8,
+        "size_mb": 3.832453727722168
+      },
+      "gc_concreteness": {
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+        "n_features": 22,
+        "size_mb": 10.268058776855469
+      },
+      "gc_dialogue": {
+        "n_samples": 117503,
+        "n_features": 6,
+        "size_mb": 0.571375846862793
+      },
+      "gc_discourse": {
+        "n_samples": 117503,
+        "n_features": 7,
+        "size_mb": 1.0773344039916992
+      },
+      "gc_narrative": {
+        "n_samples": 117503,
+        "n_features": 10,
+        "size_mb": 0.8629426956176758
+      },
+      "gc_polarity": {
+        "n_samples": 117503,
+        "n_features": 23,
+        "size_mb": 19.75270366668701
+      },
+      "gc_pos": {
+        "n_samples": 117503,
+        "n_features": 8,
+        "size_mb": 1.6754398345947266
+      },
+      "gc_pronouns": {
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+        "n_features": 12,
+        "size_mb": 1.9187965393066406
+      },
+      "gc_punctuation": {
+        "n_samples": 117503,
+        "n_features": 8,
+        "size_mb": 0.7266168594360352
+      },
+      "gc_readability": {
+        "n_samples": 117503,
+        "n_features": 4,
+        "size_mb": 1.960618019104004
+      },
+      "gc_syntax": {
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+        "n_features": 575,
+        "size_mb": 30.994938850402832
+      },
+      "gc_temporal": {
+        "n_samples": 117503,
+        "n_features": 5,
+        "size_mb": 0.5964765548706055
+      },
+      "ngram": {
+        "n_samples": 117503,
+        "n_features": 9,
+        "size_mb": 1.9910192489624023
+      },
+      "ngram_surprisal": {
+        "n_samples": 117503,
+        "n_features": 86,
+        "size_mb": 1.2012948989868164
+      },
+      "plot_shifts": {
+        "n_samples": 117503,
+        "n_features": 3,
+        "size_mb": 1.5663232803344727
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+      "rst": {
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+        "n_features": 46,
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+      "structure": {
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+        "n_features": 5,
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+      "surprisal": {
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+        "n_features": 6,
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+        "n_features": 3,
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+        "n_features": 6,
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+    },
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+        "n_features": 21,
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