| --- |
| language: en |
| license: mit |
| tags: |
| - token-classification |
| - ner |
| - math |
| - mathematics |
| - equations |
| base_model: roberta-base |
| datasets: |
| - lighteval/MATH-Hard |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| pipeline_tag: token-classification |
| model-index: |
| - name: math_token_classification |
| results: |
| - task: |
| type: token-classification |
| name: Named Entity Recognition |
| dataset: |
| name: lighteval/MATH-Hard |
| type: lighteval/MATH-Hard |
| split: train |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.0 |
| - name: F1 |
| type: f1 |
| value: 0.0 |
| --- |
| |
| # FOR TESTING PURPOSES TO SEE IF TOKEN CLASSIFICATION CAN BE USED FOR MATCHING MATH EXPRESSIONS TO MATH FIELDS |
|
|
| # Math Token Classification (RoBERTa-base) |
|
|
| This model is a fine-tuned version of **roberta-base** optimized to perform **Named Entity Recognition (NER) / Token Classification** on mathematical texts. Its primary objective is to detect inline and block LaTeX mathematical expressions within standard English text and classify them into their respective mathematical domains. |
|
|
| ## Target Domains & Labels |
| The model uses a **BIO (Beginning, Inside, Outside)** tagging schema across 4 distinct mathematical disciplines: |
| - `ALGEBRA` (e.g., linear equations, polynomials, systems of equations) |
| - `CALCULUS` (e.g., integrals, derivatives, limits) |
| - `GEOMETRY` (e.g., shapes, coordinate planes, geometric theorems) |
| - `TRIGONOMETRY` (e.g., sine, cosine, identity equations) |
|
|
| ### Label Mapping Indices |
| ```json |
| { |
| "0": "O", |
| "1": "B-ALGEBRA", "2": "I-ALGEBRA", |
| "3": "B-CALCULUS", "4": "I-CALCULUS", |
| "5": "B-GEOMETRY", "6": "I-GEOMETRY", |
| "7": "B-TRIGONOMETRY", "8": "I-TRIGONOMETRY" |
| } |
| ``` |
|
|
| ## Intended Uses & Limitations |
| This model is intended for educational tools, math tutoring software, or documentation engines to help parse, analyze, or visually highlight mathematical syntax automatically. |
|
|
| ### Limitations |
| - **Tokenizer Fragility:** While RoBERTa's Byte-Level BPE tokenizer handles rare math symbols effectively, highly non-standard or custom LaTeX macros not present in standard datasets might fragment predictions. |
| - **Context Dependence:** The expression classification depends heavily on the context words around the formula in the prompt sentence. |
|
|
| ## Training Data & Methodology |
| The model was fine-tuned on **1,553 highly complex mathematical problems** extracted from the **`lighteval/MATH-Hard`** dataset (specifically Level 5 competition-grade questions). |
| - **Sequence Preprocessing:** Regular expressions isolated math spans wrapped in standard delimiters (`$...$`, `$$...$$`, `\[...\]`, `\(...\)`). |
| - **Subword Alignment:** Character boundaries were aligned to RoBERTa token vectors via structural sequence index matching (`char_to_token`). |
|
|
| ### Training Hyperparameters |
| - **Base Architecture:** RoBERTa-base (~125M parameters) |
| - **Learning Rate:** 3e-5 |
| - **Batch Size:** 8 |
| - **Epochs:** 3 |
| - **Optimizer:** AdamW with weight decay (0.01) |
| - **Precision:** Mixed Precision (FP16) where available |
|
|
| ## How to Use |
|
|
| You can run inference using the Hugging Face `pipeline` API: |
|
|
| ```python |
| from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification |
| |
| model_id = "EricAI03/math_token_classification" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForTokenClassification.from_pretrained(model_id) |
| |
| nlp_ner = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
| |
| text = "Find the derivative of \(\int x^2\) dx and then solve the linear equation \(y = 2x + 3\)." |
| print(nlp_ner(text)) |
| ``` |
|
|
