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README.md

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+---
+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-newspapers
+---
+
+# 📰 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:**
+- 📄 [Institutional Newspapers: Boston Public Library technical report](TODO)
+
+**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
+```

config.json

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+{
+    "normalize": true,
+    "embedding_dtype": "float32",
+    "vocabulary_quantization": 63091
+}

eval.txt

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+                                     precision    recall  f1-score   support
+
+                      Advertisement       0.95      0.94      0.95     11244
+                            Cartoon       0.85      0.57      0.68       430
+                            Content       0.90      0.96      0.93     11245
+Masthead, nameplate or running head       0.93      0.86      0.89      2250
+         Photograph or illustration       0.63      0.38      0.48       551
+                    Section heading       0.94      0.93      0.94      5295
+
+                           accuracy                           0.92     31015
+                          macro avg       0.87      0.77      0.81     31015
+                       weighted avg       0.92      0.92      0.92     31015

model.safetensors

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+size 129967700

modules.json

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+[
+    {
+        "idx": 0,
+        "name": "0",
+        "path": ".",
+        "type": "sentence_transformers.models.StaticEmbedding"
+    },
+    {
+        "idx": 1,
+        "name": "1",
+        "path": "1_Normalize",
+        "type": "sentence_transformers.models.Normalize"
+    }
+]

pipeline.skops

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training-set-stats.json

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+{"train": {"Content": 52466, "Section heading": 11762, "Advertisement": 52475, "Photograph or illustration": 1223, "Cartoon": 947, "Masthead, nameplate or running head": 5022, "Empty": 0}, "val": {"Content": 11245, "Section heading": 5295, "Advertisement": 11244, "Photograph or illustration": 538, "Cartoon": 422, "Masthead, nameplate or running head": 2246, "Empty": 0}, "test": {"Content": 11245, "Section heading": 5295, "Advertisement": 11244, "Photograph or illustration": 551, "Cartoon": 430, "Masthead, nameplate or running head": 2250, "Empty": 0}}