Datasets:

P0L3
/

CliReNER_v_1_1_28_GOLD

@@ -106,14 +106,14 @@ tags:
 ## Dataset Summary
-**CliReNER *gold*** is a high-fidelity, expert-curated Named Entity Recognition (NER) dataset for the climate change research domain. Designed specifically as a gold-standard evaluation benchmark, it consists of 192 sentences annotated by a panel of 12 domain experts.
-While the [*silver*](https://huggingface.co/datasets/P0L3/CliReNER_v_1_1_28_SILVER) version of this dataset is intended for model training, **CliReNER *gold*** serves as the authoritative ground truth for assessing model performance, ensuring high inter-annotator agreement and consensus through Weighted Expert Voting (WEV).
 ## Dataset Structure
 ### Data Instances
-Instances are provided in the standard BIO format, consistent with the CliReNER Silver schema to ensure seamless model evaluation.
 ```python
 {
@@ -132,17 +132,17 @@ The dataset utilizes the same 28-class taxonomy as the *silver* version, coverin
 - **Source Data:** Sentences were selected from the same corpus as the *silver* dataset using a multi-set multi-cover formulation to ensure that, where possible, each of the 28 entity types appears at least 50 times across the set.
 - **Expert Annotation Protocol:** We recruited 12 experts organized into six thematic pairs (e.g., "Biochemistry, ecology and pathology", "Atmospheric science and geophysics"). Annotators were assigned specific clusters of entity types matching their academic expertise.
-- **Consensus Methodology (Weighted Expert Voting):** To resolve overlapping annotations from multiple experts, we applied a **Weighted Expert Voting (WEV)** scheme. Annotations from experts with domain-specific knowledge were weighted higher (W*e* = 1.1) than non-experts (W*n* = 0.9), with a deterministic two-stage voting process to resolve BIO boundary conflicts.
-- **Agreement:** The final inter-annotator agreement among the 12 experts resulted in a Krippendorff's $\alpha$ of 0.6984. The WEV aggregation method showed high convergence with MACE probabilistic models (Cohen's $\kappa$ = 0.9076).
 ## Limitations
-- **Dataset Size:** As a high-fidelity evaluation set, the sample size (192 sentences) is smaller than the Silver training set. It is primarily intended for evaluation rather than training.
-- **Flat Schema:** Like the Silver dataset, this follows a flat NER schema, meaning nested and discontinuous entities are excluded to prioritize consistency and reduce complexity.
 ## Usage
-You can load the Gold dataset for evaluation:
 ```python
 from datasets import load_dataset

 ## Dataset Summary
+**CliReNER<sub>gold</sub>** is a high-fidelity, expert-curated Named Entity Recognition (NER) dataset for the climate change research domain. Designed specifically as a gold-standard evaluation benchmark, it consists of 192 sentences annotated by a panel of 12 domain experts.
+While the [*silver*](https://huggingface.co/datasets/P0L3/CliReNER_v_1_1_28_SILVER) version of this dataset is intended for model training, **CliReNER<sub>gold</sub>** serves as the authoritative ground truth for assessing model performance, ensuring high inter-annotator agreement and consensus through Weighted Expert Voting (WEV).
 ## Dataset Structure
 ### Data Instances
+Instances are provided in the standard BIO format, consistent with the CliReNER<sub>silver</sub> schema to ensure seamless model evaluation.
 ```python
 {
 - **Source Data:** Sentences were selected from the same corpus as the *silver* dataset using a multi-set multi-cover formulation to ensure that, where possible, each of the 28 entity types appears at least 50 times across the set.
 - **Expert Annotation Protocol:** We recruited 12 experts organized into six thematic pairs (e.g., "Biochemistry, ecology and pathology", "Atmospheric science and geophysics"). Annotators were assigned specific clusters of entity types matching their academic expertise.
+- **Consensus Methodology (Weighted Expert Voting):** To resolve overlapping annotations from multiple experts, we applied a **Weighted Expert Voting (WEV)** scheme. Annotations from experts with domain-specific knowledge were weighted higher (w<sub>e</sub> = 1.1) than non-experts (w<sub>n</sub> = 0.9), with a deterministic two-stage voting process to resolve BIO boundary conflicts.
+- **Agreement:** The final inter-annotator agreement among the 12 experts resulted in a Krippendorff's α of 0.6984. The WEV aggregation method showed high convergence with MACE probabilistic models (Cohen's κ = 0.9076).
 ## Limitations
+- **Dataset Size:** As a high-fidelity evaluation set, the sample size (192 sentences) is smaller than the *silver* training set. It is primarily intended for evaluation rather than training.
+- **Flat Schema:** Like the *silver* dataset, this follows a flat NER schema, meaning nested and discontinuous entities are excluded to prioritize consistency and reduce complexity.
 ## Usage
+You can load the *gold* dataset for evaluation:
 ```python
 from datasets import load_dataset