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README.md

@@ -1,12 +1,117 @@
 ---
-title: Errant Gec
-emoji: 👀
-colorFrom: green
-colorTo: pink
+title: ERRANT GEC
+emoji: "📝"
+colorFrom: blue
+colorTo: green
 sdk: gradio
-sdk_version: 6.0.2
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
+tags:
+  - evaluate
+  - metric
+  - grammatical-error-correction
+  - gec
+description: ERRANT metric for evaluating grammatical error correction systems
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ERRANT GEC Metric
+
+ERRANT (ERRor ANnotation Toolkit) is a metric for evaluating grammatical error correction (GEC) systems.
+
+## Description
+
+This metric computes precision, recall, and F-score by comparing the edit operations needed to transform source sentences into predictions versus the edit operations needed to transform source sentences into references.
+
+The metric uses the [ERRANT library](https://github.com/chrisjbryant/errant) to extract and compare edits.
+
+## Installation
+
+```bash
+pip install evaluate errant spacy
+# Install the appropriate spaCy model for your language
+python -m spacy download en_core_web_sm  # English
+python -m spacy download nb_core_news_sm  # Norwegian
+```
+
+## Usage
+
+```python
+import evaluate
+
+errant_gec = evaluate.load("marksverdhei/errant_gec")
+
+results = errant_gec.compute(
+    sources=["This are a sentence ."],
+    predictions=["This is a sentence ."],
+    references=["This is a sentence ."],
+    lang="en"
+)
+
+print(results)
+# {'precision': 1.0, 'recall': 1.0, 'f0.5': 1.0}
+```
+
+## Inputs
+
+- **sources** (`list[str]`): The original (uncorrected) sentences
+- **predictions** (`list[str]`): The model's corrected sentences
+- **references** (`list[str]`): The gold standard corrected sentences
+- **lang** (`str`, optional): Language code for spaCy model. Default: `"en"`
+  - `"en"`: English (requires `en_core_web_sm`)
+  - `"nb"`: Norwegian Bokmål (requires `nb_core_news_sm`)
+  - `"de"`: German (requires `de_core_news_sm`)
+  - etc. (any language with a spaCy model)
+- **beta** (`float`, optional): Beta value for F-score calculation. Default: `0.5`
+
+## Outputs
+
+- **precision** (`float`): Fraction of predicted edits that are correct
+- **recall** (`float`): Fraction of gold edits that were predicted
+- **f{beta}** (`float`): F-score with the specified beta value (default key: `f0.5`)
+
+## Example with Norwegian
+
+```python
+import evaluate
+
+errant_gec = evaluate.load("marksverdhei/errant_gec")
+
+results = errant_gec.compute(
+    sources=["Jeg har spist mye mat i går ."],
+    predictions=["Jeg spiste mye mat i går ."],
+    references=["Jeg spiste mye mat i går ."],
+    lang="nb"
+)
+```
+
+## Why F0.5?
+
+In grammatical error correction, precision is typically weighted more heavily than recall (beta=0.5) because:
+- False positives (incorrect "corrections") are more harmful to the user experience
+- It's better to miss some errors than to introduce new ones
+
+## Limitations
+
+- Requires the appropriate spaCy model to be installed for the target language
+- ERRANT was originally designed for English; performance on other languages depends on the quality of the spaCy model
+- The metric operates at the edit level, not the sentence level
+
+## Citation
+
+```bibtex
+@inproceedings{bryant-etal-2017-automatic,
+    title = "Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction",
+    author = "Bryant, Christopher  and
+      Felice, Mariano  and
+      Briscoe, Ted",
+    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P17-1074",
+    doi = "10.18653/v1/P17-1074",
+    pages = "793--805",
+}
+```