| --- |
| base_model: minishlab/potion-base-32m |
| library_name: model2vec |
| license: mit |
| tags: |
| - model2vec |
| - static-embeddings |
| - text-classification |
| - newspaper-classification |
| - crop-classification |
| - historical-newspapers |
| - newspapers |
| datasets: |
| - institutional/institutional-newspapers-bpl |
| --- |
| |
| # 📰 Institutional Newspapers Crop Classifier (Text) |
|
|
| A text-based classifier that categorizes crops extracted from historical newspaper scans into high-level categories. This model is a [Model2Vec](https://github.com/MinishLab/model2vec) fine-tune of [minishlab/potion-base-32m](https://huggingface.co/minishlab/potion-base-32m) with a classifier head, which makes it light and efficient. |
|
|
| We recommend using this model alongside [`institutional/institutional-newspapers-crop-classifier-image-yolo26m-cls`](https://huggingface.co/institutional/institutional-newspapers-crop-classifier-image-yolo26m-cls), as neither visual nor textual signal alone is sufficient to classify newspaper crops accurately. |
|
|
| **More information:** |
| - 📄 [Full analysis available in our technical report](https://arxiv.org/abs/2608.18972) |
|
|
| **See also:** |
| - 🗂️ [Institutional Newspapers Collection](https://huggingface.co/collections/institutional/institutional-newspapers) |
| - ⚙️ [Pipeline](https://github.com/institutional/institutional-newspapers-pipeline) |
| - 🤖 [Segmentation model](https://huggingface.co/institutional/institutional-newspapers-segmenter-yolo26x) |
| - 🤖 [Crop-type image classifier](https://huggingface.co/institutional/institutional-newspapers-crop-classifier-image-yolo26m-cls) |
|
|
| The Institutional Data Initiative at the Harvard Law School Library works with knowledge institutions—from libraries and museums to cultural groups and government agencies—to refine and publish their collections as data. [Reach out to collaborate on your collections](https://institutional.org/). |
|
|
| ## Evaluation results |
|
|
| Evaluated on a held-out test set of 31,015 crops: |
|
|
| | Class | Precision | Recall | F1-Score | Support | |
| |---|---|---|---|---| |
| | Advertisement | 0.95 | 0.94 | 0.95 | 11,244 | |
| | Cartoon | 0.85 | 0.57 | 0.68 | 430 | |
| | Content | 0.90 | 0.96 | 0.93 | 11,245 | |
| | Masthead, nameplate or running head | 0.93 | 0.86 | 0.89 | 2,250 | |
| | Photograph or illustration | 0.63 | 0.38 | 0.48 | 551 | |
| | Section heading | 0.94 | 0.93 | 0.94 | 5,295 | |
|
|
| | Metric | Score | |
| |---|---| |
| | **Accuracy** | 0.92 | |
| | **Macro avg F1** | 0.81 | |
| | **Weighted avg F1** | 0.92 | |
|
|
| ## Training data |
|
|
| This model was trained on part of **Boston Public Library's** newspapers collection. |
|
|
| - **Total annotated crops**: 185,900 |
| - **Classes**: 6 |
| - **Split**: ~67% train / ~17% val / ~17% test |
|
|
| ## Usage |
|
|
| ```python |
| from model2vec.inference import StaticModelPipeline |
| |
| model_name = "institutional/institutional-newspapers-crop-classifier-text-model2vec" |
| model = StaticModelPipeline.from_pretrained(model_name) |
| |
| texts = ["CLASSIFIED ADVERTISING — Rooms for rent, furnished apartments..."] |
| predictions = model.predict(texts, max_length=None) |
| |
| print(predictions) |
| # ['Advertisement'] |
| ``` |
|
|
| ### Recommended inference parameters |
|
|
| | Parameter | Value | Note | |
| |---|---|---| |
| | `max_length` | `None` | Do not truncate input text | |
|
|
| ## Citation |
|
|
| ```bibtex |
| TODO |
| ``` |
