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README.md

@@ -1,97 +1,70 @@
 ---
-library_name: model2vec
+language: en
 license: mit
-model_name: static_model
+library_name: model2vec
 tags:
-- embeddings
-- static-embeddings
-- sentence-transformers
+  - model2vec
+  - static-embeddings
+  - topic-classification
+  - openalex
+  - scientific-papers
+pipeline_tag: text-classification
+datasets:
+  - jimnoneill/paper-to-field-training
 ---
 
-# static_model Model Card
-
-This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of a Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
-
+# Paper-to-Field Classifier
 
-## Installation
-
-Install model2vec using pip:
-```
-pip install model2vec
-```
+Lightweight CPU-based topic classifier for scientific paper abstracts using the
+[OpenAlex taxonomy](https://docs.openalex.org/api-entities/topics) (4,516 topics → 245 subfields → 26 fields → 4 domains).
 
 ## Usage
 
-### Using Model2Vec
-
-The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
-
-Load this model using the `from_pretrained` method:
 ```python
-from model2vec import StaticModel
-
-# Load a pretrained Model2Vec model
-model = StaticModel.from_pretrained("static_model")
-
-# Compute text embeddings
-embeddings = model.encode(["Example sentence"])
+from paper_classifier import PaperClassifier
+
+classifier = PaperClassifier()
+classifier.initialize()
+
+result = classifier.classify(
+    title="Attention Is All You Need",
+    abstract="The dominant sequence transduction models are based on complex recurrent or convolutional neural networks..."
+)
+
+print(result)
+# {
+#   'topic': {'id': 10209, 'name': 'Neural Machine Translation and Sequence Models', 'score': 0.87},
+#   'subfield': {'id': 1702, 'name': 'Artificial Intelligence'},
+#   'field': {'id': 17, 'name': 'Computer Science'},
+#   'domain': {'id': 3, 'name': 'Physical Sciences'}
+# }
 ```
 
-### Using Sentence Transformers
+## Model Details
 
-You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
+- **Base model**: [minishlab/potion-base-32M](https://huggingface.co/minishlab/potion-base-32M) (Model2Vec)
+- **Fine-tuned on**: ~50K domain-balanced paper abstracts from OpenAlex
+- **Taxonomy**: OpenAlex (4,516 topics, 245 subfields, 26 fields, 4 domains)
+- **Input**: Paper title + abstract (truncated to 500 chars)
+- **Inference**: CPU-only, ~3,000 papers/second
 
-```python
-from sentence_transformers import SentenceTransformer
-
-# Load a pretrained Sentence Transformer model
-model = SentenceTransformer("static_model")
+## Training
 
-# Compute text embeddings
-embeddings = model.encode(["Example sentence"])
-```
+Trained on OpenAlex bulk data with papers filtered for:
+- English language
+- Has abstract
+- Primary topic confidence score > 0.8
+- Domain-balanced sampling (~12.5K per domain)
 
-### Distilling a Model2Vec model
+## Install
 
-You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
-
-```python
-from model2vec.distill import distill
-
-# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
-m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
-
-# Save the model
-m2v_model.save_pretrained("m2v_model")
+```bash
+pip install paper-classifier
 ```
 
-## How it works
-
-Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
-
-It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
-
-## Additional Resources
-
-- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
-- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
-- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
-- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
-- [Website](https://minishlab.github.io/)
-
-
-## Library Authors
-
-Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
-
-## Citation
-
-Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
+Or from source:
+```bash
+git clone https://github.com/jimnoneill/paper-to-field.git
+cd paper-to-field
+pip install -e .
 ```
-@article{minishlab2024model2vec,
-  author = {Tulkens, Stephan and {van Dongen}, Thomas},
-  title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
-  year = {2024},
-  url = {https://github.com/MinishLab/model2vec}
-}
-```