Update README.md

README.md

@@ -1,44 +1,20 @@
----
-dataset_info:
-  features:
-  - name: source_identifier
-    dtype: string
-  - name: input
-    dtype: string
-  - name: output
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 37877859
-    num_examples: 45739
-  download_size: 21921650
-  dataset_size: 37877859
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-language:
-- en
-datasets:
-- kl3m-derived
-license: cc-by-4.0
-tags:
-- kl3m
-- kl3m-derived
-- legal
-- sbd
-- sentence-boundary-detection
-- paragraph-boundary-detection
-- legal-nlp
-- benchmark
-- evaluation
-task_categories:
-- token-classification
-- text2text-generation
-size_categories:
-- 10K<n<100K
----
+---
+language:
+- en
+datasets:
+- kl3m-derived
+license: cc-by-4.0
+tags:
+- kl3m
+- kl3m-derived
+- legal
+- sbd
+- sentence-boundary-detection
+- paragraph-boundary-detection
+- legal-nlp
+- benchmark
+- evaluation
+---
 
 # ALEA Legal Benchmark: Sentence and Paragraph Boundaries
 
@@ -62,8 +38,8 @@ For more information about the original KL3M Data Project, please visit the [Git
 
 The dataset was created through a sophisticated multi-stage annotation process:
 
-1. Source documents were extracted from the KL3M Data Project
-2. Random segments of text were selected from each document using a controlled token-length window (between 32-128 tokens)
+1. Source documents were extracted from the KL3M corpus, which includes public domain legal materials
+2. Random segments of legal text were selected from each document using a controlled token-length window (between 32-128 tokens)
 3. A generate-judge-correct framework was employed:
    - **Generate**: A large language model was used to add `<|sentence|>` and `<|paragraph|>` boundary markers to the text
    - **Judge**: A second LLM verified the correctness of annotations, with strict validation to ensure:
@@ -71,7 +47,7 @@ The dataset was created through a sophisticated multi-stage annotation process:
      - Boundary markers were placed correctly according to legal conventions
    - **Correct**: When needed, a third LLM phase corrected any incorrectly placed boundaries
 4. Additional programmatic validation ensured character-level fidelity between input and annotated output
-5. The resulting dataset was reviewed for quality and consistency
+5. The resulting dataset was reviewed for quality and consistency by legal experts
 
 This dataset was used to develop and evaluate the NUPunkt and CharBoundary libraries described in [arXiv:2504.04131](https://arxiv.org/abs/2504.04131), which achieved 91.1% precision and the highest F1 scores (0.782) among tested methods for legal sentence boundary detection.
 
@@ -134,10 +110,11 @@ def prepare_training_data(dataset):
 
 This dataset enables:
 
-1. Training and evaluating sentence boundary detection models for specialized domains like legal or financial
-2. Developing other hierarchical segmentation models like paragraph and section models
+1. Training and evaluating sentence boundary detection models for legal text
+2. Developing paragraph segmentation tools for legal documents
 3. Benchmarking existing NLP tools on challenging legal text
-4. Improving RAG and information retrieval and extraction, especially in legal and financial contexts
+4. Improving information retrieval and extraction from legal corpora
+5. Enhancing retrieval-augmented generation (RAG) systems for legal applications
 
 ## Related Libraries
 
@@ -155,14 +132,38 @@ pip install nupunkt
 pip install charboundary
 ```
 
-## Legal Basis
+Example usage with this dataset:
 
-This dataset maintains the same copyright compliance as the original KL3M Data Project:
+```python
+from datasets import load_dataset
+import nupunkt
+import charboundary
+
+# Load dataset
+dataset = load_dataset("alea-institute/alea-legal-benchmark-sentence-paragraph-boundaries")
+
+# Initialize detectors
+np_detector = nupunkt.NUPunkt()
+cb_detector = charboundary.CharBoundary()
+
+# Compare detections with ground truth
+for example in dataset["train"]:
+    # Ground truth from dataset
+    true_boundaries = example["output"]
+    
+    # Automated detection
+    np_boundaries = np_detector.segment_text(example["input"])
+    cb_boundaries = cb_detector.segment_text(example["input"])
+    
+    # Compare and evaluate
+    # ...
+```
+
+## Legal Basis
 
-- Public domain materials
-- US government works
-- Open access content under permissive licenses
-- Content explicitly licensed for AI training
+This dataset maintains the same copyright compliance as the original KL3M Data Project, as LLM
+annotation is solely used to insert `<|sentence|>` or `<|paragraph|>` tokens, but users should
+review their position on output use restrictions related to this data.
 
 ## Papers