Update README.md

README.md

@@ -1,177 +1,177 @@
----
-language: en
-license: cc-by-nc-4.0
-tags:
-- movies
-- screenplays
-- oscar
-- text-classification
-- embeddings
-size_categories:
-- 1K<n<10K
-task_categories:
-- text-classification
-pretty_name: Movie-O-Label
-dataset_info:
-  features:
-    - name: movie_name
-      dtype: string
-    - name: imdb_id
-      dtype: string
-    - name: title
-      dtype: string
-    - name: year
-      dtype: int64
-    - name: summary
-      dtype: string
-    - name: script
-      dtype: string
-    - name: script_plain
-      dtype: string
-    - name: script_clean
-      dtype: string
-    - name: nominated
-      dtype: int64
-    - name: winner
-      dtype: int64
----
-
-
-
-
-
-# Movie-O-Label
-
-**Movie-O-Label** is a dataset created by merging the [MovieSum](https://huggingface.co/datasets/rohitsaxena/MovieSum) screenplay collection with Oscar nomination labels derived from [David V. Lu!!’s Oscar Data](https://github.com/DLu/oscar_data).  
-It provides **screenplays, summaries, and metadata** together with binary labels indicating whether a movie’s screenplay received an **Oscar nomination** and whether it **won**.
-
----
-
-##  Contents
-
-Each entry includes:
-
-
-| column          | type    | description                                                                 |
-|-----------------|---------|-----------------------------------------------------------------------------|
-| `movie_name`    | string  | Title and year combined, e.g. `The Social Network_2010`                     |
-| `title`         | string  | Movie title                                                                 |
-| `year`          | int     | Release year                                                                |
-| `imdb_id`       | string  | IMDb identifier (e.g. `tt1285016`)                                          |
-| `summary`       | string  | Plot summary of the movie                                                   |
-| `script_clean`  | string  | script_plain cleaned (unicode normaliziation, stage directions and scene transitions stripped where possible, whitespace reduced)|
-| `script_plain`  | string  | Original screenplay text (only xml-tags removed from script)                |
-| `script`        | string  | Raw script field from MovieSum (for reference)                              |
-| `nominated`     | int     | `1` if the screenplay was nominated for an Academy Award (Writing)          |
-| `winner`        | int     | `1` if the screenplay won an Academy Award (Writing)                        |
-
----
-
-## Splits
-
-The dataset is provided as a **`DatasetDict`** with:
-
-- `train` — 60% (1320 movies)
-- `validation` — 20% (440 movies)
-- `test` — 20% (440 movies)
-
-Splits were created **stratified** by the `nominated` label to preserve class balance.
-
-A file `split_60_20_20.npz` with the exact index arrays (`idx_train`, `idx_val`, `idx_test`) is also provided for full reproducibility.
-
----
-
-## Additional Resources
-
-To fully reproduce the experiments described in the paper:
-
-- [Paper (PDF)](./assets/FrancisGross_PredictionNominatedScreenplays_2025.pdf)
-- [Fixed Train/Validation/Test split (split_60_20_20.npz)](./assets/splits/split_60_20_20.npz)
-- [Script embeddings (emb_script.joblib)](./assets/embeddings/emb_script.joblib)
-- [Summary embeddings (emb_summary.joblib)](./assets/embeddings/emb_summary.joblib)
-- [Title embeddings (emb_title.joblib)](./assets/embeddings/emb_title.joblib)
-- [Model configuration (model_config.json)](./assets/config/best-performing-model_config.json)
-- [Projekt code jupyter notebook](./assets/code/FrancisGross_screenplay_pred_nom.ipynb)
-
-
-
-
-
-
-
-## License & Attribution
-
-- **MovieSum dataset**:  
-  Created and published by [Rohit Saxena](https://huggingface.co/datasets/rohitsaxena/MovieSum) (with Frank Keller).  
-  Licensed under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.  
-  If you use this dataset, please cite:  
-  *Rohit Saxena and Frank Keller. "MovieSum: An Abstractive Summarization Dataset for Movie Screenplays." Findings of ACL 2024. arXiv:2408.06281.*
-
-- **Oscar nominations**:  
-  Data adapted from [David V. Lu!!’s Oscar Data](https://github.com/DLu/oscar_data)  
-  Licensed under the **BSD 2-Clause License** © 2022 David V. Lu!!.
-
-- **Movie-O-Label**:  
-  Created and processed by [Francis Gross](https://huggingface.co/datasets/Francis2003/Movie-O-Label), based on cleaned MovieSum screenplay texts enriched with Oscar nomination and winner labels.  
-  Released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.
-   If you use this dataset, please cite:  
-   *Francis Gross. "Movie-O-Label: Predicting Oscar-Nominated Screenplays with Sentence Embeddings." Findings of ACL 2025 on Hugging Face.*
-
-
-## Baseline Workflow
-
-This work provides a simple baseline for predicting whether a screenplay receives an Oscar nomination
-in the *Writing/Screenplays* category.
-
-1. **Load the dataset**  
-   ```python
-   from datasets import load_dataset
-   ds = load_dataset("Francis2003/Movie-O-Label")
-````
-
-The dataset includes a predefined **60/20/20 train/validation/test split** (`split_60_20_20.npz`).
-
-2. **Text preparation**
-   Use one or more of the available feature fields:
-
-   * `script_clean` (recommended for embeddings)
-   * `summary`
-   * `title`
-
-3. **Embeddings**
-   Encode the texts with [**intfloat/e5-base-v2**](https://huggingface.co/intfloat/e5-base-v2).
-   Each screenplay can be chunked (e.g., 400 words with 80-word overlap), encoded,
-   and mean+max pooling and L2 normalized.
-
-4. **Classifier**
-   Train a logistic regression classifier with:
-
-   ```python
-   from sklearn.linear_model import LogisticRegression
-   clf = LogisticRegression(max_iter=5000, class_weight="balanced", C=1.0)
-   ```
-
-   Select the threshold on the **validation set** to maximize F1 for the positive class (nominated).
-
-5. **Evaluation**
-   Report metrics such as Accuracy, ROC-AUC, PR-AUC, F1 (positive/negative) and Macro-F1.
-
-> The best-performing baseline used **script_clean + summary + title** embeddings
-> and achieved **ROC-AUC ≈ 0.79** and **Macro-F1 ≈ 0.68** on the test set.
-
-```
-
-
-## Usage
-
-```python
-from datasets import load_dataset
-
-# Public dataset:
-ds = load_dataset("Francis2003/Movie-O-Label")
-
-print(ds)
-print(ds["train"][0])
-
-
-
+---
+language: en
+license: cc-by-nc-4.0
+tags:
+- movies
+- screenplays
+- oscar
+- text-classification
+- embeddings
+size_categories:
+- 1K<n<10K
+task_categories:
+- text-classification
+pretty_name: Movie-O-Label
+dataset_info:
+  features:
+    - name: movie_name
+      dtype: string
+    - name: imdb_id
+      dtype: string
+    - name: title
+      dtype: string
+    - name: year
+      dtype: int64
+    - name: summary
+      dtype: string
+    - name: script
+      dtype: string
+    - name: script_plain
+      dtype: string
+    - name: script_clean
+      dtype: string
+    - name: nominated
+      dtype: int64
+    - name: winner
+      dtype: int64
+---
+
+
+
+
+
+# Movie-O-Label
+
+**Movie-O-Label** is a dataset created by merging the [MovieSum](https://huggingface.co/datasets/rohitsaxena/MovieSum) screenplay collection with Oscar nomination labels derived from [David V. Lu’s Oscar Data](https://github.com/DLu/oscar_data).  
+It provides **screenplays, summaries, and metadata** together with binary labels indicating whether a movie’s screenplay received an **Oscar nomination** and whether it **won**.
+
+---
+
+##  Contents
+
+Each entry includes:
+
+
+| column          | type    | description                                                                 |
+|-----------------|---------|-----------------------------------------------------------------------------|
+| `movie_name`    | string  | Title and year combined, e.g. `The Social Network_2010`                     |
+| `title`         | string  | Movie title                                                                 |
+| `year`          | int     | Release year                                                                |
+| `imdb_id`       | string  | IMDb identifier (e.g. `tt1285016`)                                          |
+| `summary`       | string  | Plot summary of the movie                                                   |
+| `script_clean`  | string  | script_plain cleaned (unicode normaliziation, stage directions and scene transitions stripped where possible, whitespace reduced)|
+| `script_plain`  | string  | Original screenplay text (only xml-tags removed from script)                |
+| `script`        | string  | Raw script field from MovieSum (for reference)                              |
+| `nominated`     | int     | `1` if the screenplay was nominated for an Academy Award (Writing)          |
+| `winner`        | int     | `1` if the screenplay won an Academy Award (Writing)                        |
+
+---
+
+## Splits
+
+The dataset is provided as a **`DatasetDict`** with:
+
+- `train` — 60% (1320 movies)
+- `validation` — 20% (440 movies)
+- `test` — 20% (440 movies)
+
+Splits were created **stratified** by the `nominated` label to preserve class balance.
+
+A file `split_60_20_20.npz` with the exact index arrays (`idx_train`, `idx_val`, `idx_test`) is also provided for full reproducibility.
+
+---
+
+## Additional Resources
+
+To fully reproduce the experiments described in the paper:
+
+- [Paper (PDF)](./assets/FrancisGross_PredictionNominatedScreenplays_2025.pdf)
+- [Fixed Train/Validation/Test split (split_60_20_20.npz)](./assets/splits/split_60_20_20.npz)
+- [Script embeddings (emb_script.joblib)](./assets/embeddings/emb_script.joblib)
+- [Summary embeddings (emb_summary.joblib)](./assets/embeddings/emb_summary.joblib)
+- [Title embeddings (emb_title.joblib)](./assets/embeddings/emb_title.joblib)
+- [Model configuration (model_config.json)](./assets/config/best-performing-model_config.json)
+- [Projekt code jupyter notebook](./assets/code/FrancisGross_screenplay_pred_nom.ipynb)
+
+
+
+
+
+
+
+## License & Attribution
+
+- **MovieSum dataset**:  
+  Created and published by [Rohit Saxena](https://huggingface.co/datasets/rohitsaxena/MovieSum) (with Frank Keller).  
+  Licensed under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.  
+  If you use this dataset, please cite:  
+  *Rohit Saxena and Frank Keller. "MovieSum: An Abstractive Summarization Dataset for Movie Screenplays." Findings of ACL 2024. arXiv:2408.06281.*
+
+- **Oscar nominations**:  
+  Data adapted from [David V. Lu!!’s Oscar Data](https://github.com/DLu/oscar_data)  
+  Licensed under the **BSD 2-Clause License** © 2022 David V. Lu!!.
+
+- **Movie-O-Label**:  
+  Created and processed by [Francis Gross](https://huggingface.co/datasets/Francis2003/Movie-O-Label), based on cleaned MovieSum screenplay texts enriched with Oscar nomination and winner labels.  
+  Released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license.
+   If you use this dataset, please cite:  
+   *Francis Gross. "Movie-O-Label: Predicting Oscar-Nominated Screenplays with Sentence Embeddings." Findings of ACL 2025 on Hugging Face.*
+
+
+## Baseline Workflow
+
+This work provides a simple baseline for predicting whether a screenplay receives an Oscar nomination
+in the *Writing/Screenplays* category.
+
+1. **Load the dataset**  
+   ```python
+   from datasets import load_dataset
+   ds = load_dataset("Francis2003/Movie-O-Label")
+````
+
+The dataset includes a predefined **60/20/20 train/validation/test split** (`split_60_20_20.npz`).
+
+2. **Text preparation**
+   Use one or more of the available feature fields:
+
+   * `script_clean` (recommended for embeddings)
+   * `summary`
+   * `title`
+
+3. **Embeddings**
+   Encode the texts with [**intfloat/e5-base-v2**](https://huggingface.co/intfloat/e5-base-v2).
+   Each screenplay can be chunked (e.g., 400 words with 80-word overlap), encoded,
+   and mean+max pooling and L2 normalized.
+
+4. **Classifier**
+   Train a logistic regression classifier with:
+
+   ```python
+   from sklearn.linear_model import LogisticRegression
+   clf = LogisticRegression(max_iter=5000, class_weight="balanced", C=1.0)
+   ```
+
+   Select the threshold on the **validation set** to maximize F1 for the positive class (nominated).
+
+5. **Evaluation**
+   Report metrics such as Accuracy, ROC-AUC, PR-AUC, F1 (positive/negative) and Macro-F1.
+
+> The best-performing baseline used **script_clean + summary + title** embeddings
+> and achieved **ROC-AUC ≈ 0.79** and **Macro-F1 ≈ 0.68** on the test set.
+
+```
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Public dataset:
+ds = load_dataset("Francis2003/Movie-O-Label")
+
+print(ds)
+print(ds["train"][0])
+
+
+