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--- |
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license: cc-by-4.0 |
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datasets: |
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- kenhktsui/math-classifiers-data |
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language: |
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- en |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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base_model: |
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- facebook/fasttext-en-vectors |
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pipeline_tag: text-classification |
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library_name: fasttext |
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--- |
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# Model Card for FastText Math vs. Non-Math Classifier |
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A FastText-based binary classifier trained to distinguish “math” text from “non-math” text in English webpages. It is fine-tuned on the `kenhktsui/math-classifiers-data` dataset using `facebook/fasttext-en-vectors` as the base word-embedding model. |
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--- |
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## Model Details |
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### Overview |
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This model takes raw English text (for example, the plain-text extraction of an HTML page) and predicts whether the content is math-related (label `__label__math`) or not (label `__label__non-math`). It was developed by user **herooooooooo** and is released under the CC-BY-4.0 license. |
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- **Model type:** Supervised FastText classifier (binary classification) |
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- **Developed by:** herooooooooo |
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- **License:** CC-BY-4.0 |
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- **Language:** English (en) |
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- **Base model:** `facebook/fasttext-en-vectors` (pretrained word vectors) |
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- **Fine-tuned on:** `kenhktsui/math-classifiers-data` (a public Hugging Face dataset of labeled math vs. non-math examples) |
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### Intended Use |
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- **Primary application:** Filtering or labeling large corpora of webpages or documents for math content (e.g., selecting only math-related pages from web crawls). |
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- **Foreseeable users:** Researchers preparing math-focused corpora, data engineers curating domain-specific text, or educators building math content pipelines. |
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- **Out-of-scope:** |
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- Not intended for general topic classification beyond “math vs. non-math.” |
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- Performance may degrade on extremely short texts (less than ~20 tokens) or on highly technical subdomains not well represented in the training set (e.g., very specialized LaTeX macros not covered by the dataset). |
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- Should not be used for any safety- or compliance-critical pipeline without additional validation. |
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--- |
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## Bias, Risks, and Limitations |
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- **Biases:** |
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- The model is trained on the `kenhktsui/math-classifiers-data` dataset, which predominately contains English posts from math forums and random English web text. It may underperform on non-North American or non-European English dialects (e.g., Indian English math blogs) if they were underrepresented. |
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- The classifier can mislabel “math-adjacent” text (e.g., computer science blogs discussing algorithms, physics pages dense with formulas) as “non-math” if the training set did not include similar examples. |
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- **Technical limitations:** |
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- Since FastText is a bag-of-words (BoW + n-gram) approach, it does not capture very long-range dependencies or advanced context. Very subtle math content (e.g., a single embedded formula in an otherwise non-math article) may be missed. |
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- Very short snippets (e.g., a single equation or a title) may be misclassified because there may not be enough context to distinguish “math” from “non-math.” |
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### Recommendations |
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- Before applying at scale, evaluate on a held-out set of your target webpages (especially if they come from a domain not represented in the original dataset). |
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- If you encounter persistent misclassification on a new subdomain (e.g., a specialized math blog), collect additional labeled examples from that source and fine-tune or retrain a new FastText model. |
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- Use appropriate preprocessing (HTML-to-text extraction, removal of boilerplate navigation) to feed only the main article content into the model for best results. |
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--- |
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## How to Get Started with the Model |
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Install dependencies: |
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```bash |
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pip install fasttext tiktoken |
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