Update README.md

README.md

@@ -5,62 +5,71 @@ model_name: tmpqsu1ee6a
 tags:
 - embeddings
 - static-embeddings
+datasets:
+- HuggingFaceFW/fineweb-edu-llama3-annotations
+language:
+- en
+base_model:
+- minishlab/potion-base-8M
 ---
 
-# tmpqsu1ee6a Model Card
+# potion-8m-edu-classifier 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.
+This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of [potion-base-8m](https://huggingface.co/minishlab/potion-base-8M).
+It was trained to predict educational content, analogous to how the [fineweb-edu-classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) was used to filter educational content.
 
+It achieves the following performance on the evaluation split:
 
-## Installation
-
-Install model2vec using pip:
 ```
-pip install model2vec
+              precision    recall  f1-score   support
+
+           0       0.70      0.42      0.52      5694
+           1       0.75      0.86      0.80     26512
+           2       0.55      0.51      0.53     10322
+           3       0.54      0.45      0.49      3407
+           4       0.59      0.30      0.40       807
+           5       0.00      0.00      0.00         1
+
+    accuracy                           0.69     46743
+   macro avg       0.52      0.42      0.46     46743
+weighted avg       0.68      0.69      0.68     46743
 ```
 
-## Usage
-Load this model using the `from_pretrained` method:
-```python
-from model2vec import StaticModel
+When thresholded to a binary classifier, it achieves a macro-averaged F1-score of `0.79`. The original classifier achieves `0.81` on the same dataset, but this classifier is orders of magnitude faster on CPU.
 
-# Load a pretrained Model2Vec model
-model = StaticModel.from_pretrained("tmpqsu1ee6a")
-
-# Compute text embeddings
-embeddings = model.encode(["Example sentence"])
 ```
+              precision    recall  f1-score   support
 
-Alternatively, you can distill your own model using the `distill` method:
-```python
-from model2vec.distill import distill
-
-# Choose a Sentence Transformer model
-model_name = "BAAI/bge-base-en-v1.5"
+     not edu       0.96      0.98      0.97     42528
+         edu       0.70      0.54      0.61      4215
 
-# Distill the model
-m2v_model = distill(model_name=model_name, pca_dims=256)
-
-# Save the model
-m2v_model.save_pretrained("m2v_model")
+    accuracy                           0.94     46743
+   macro avg       0.83      0.76      0.79     46743
+weighted avg       0.93      0.94      0.93     46743
 ```
 
-## How it works
+## Installation
 
-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.
+Install model2vec with the inference extra using pip:
+```
+pip install model2vec[inference]
+```
 
-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 zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.
+## Usage
+Load this model using the `from_pretrained` method:
+```python
+from model2vec.inference import StaticModelPipeline
 
-## Additional Resources
+# Load a pretrained Model2Vec model
+model = StaticModelPipeline.from_pretrained("minishlab/potion-8m-edu-classifier")
 
-- [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec)
-- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
-- [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results)
-- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
+# Predict labels
+label = model.predict(["Example sentence"])
+```
 
 ## 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).
+Model2Vec was developed by [Minish](https://github.com/MinishLab).
 
 ## Citation