Update README.md

README.md

@@ -1,61 +1,95 @@
 ---
-library_name: transformers
+language: en
 license: mit
-base_model: roberta-base
 tags:
-- generated_from_trainer
+- 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: []
+  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
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# math_token_classification
-
-This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2459
-
-## Model description
+# Math Token Classification (RoBERTa-base)
 
-More information needed
+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.
 
-## Intended uses & limitations
+## 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)
 
-More information needed
+### 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"
+}
+```
 
-## Training and evaluation data
+## 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.
 
-More information needed
+### 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 procedure
+## 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
+### 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
 
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
-- mixed_precision_training: Native AMP
+## How to Use
 
-### Training results
+You can run inference using the Hugging Face `pipeline` API:
 
-| Training Loss | Epoch | Step | Validation Loss |
-|:-------------:|:-----:|:----:|:---------------:|
-| 1.4177        | 1.0   | 83   | 0.3656          |
-| 0.4853        | 2.0   | 166  | 0.2629          |
-| 0.2995        | 3.0   | 249  | 0.2459          |
+```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)
 
-### Framework versions
+nlp_ner = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
 
-- Transformers 5.16.1
-- Pytorch 2.11.0+cu128
-- Datasets 4.8.5
-- Tokenizers 0.23.1
+text = "Find the derivative of \(\int x^2\) dx and then solve the linear equation \(y = 2x + 3\)."
+print(nlp_ner(text))
+```