README.md

---
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])