Update README.md

README.md

@@ -369,7 +369,115 @@ dataset_info:
     num_examples: 1654
   download_size: 2426369
   dataset_size: 9148253
+license: cc-by-nc-sa-3.0
+task_categories:
+- token-classification
+language:
+- es
+pretty_name: SpanishSRL
+size_categories:
+- 10K<n<100K
 ---
-# Dataset Card for "spanish_srl"
+# Dataset Card for SpanishSRL
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Repository:** [SpanishSRL Project Hub](https://github.com/mbruton0426/GalicianSRL)
+- **Paper:** To be updated 
+- **Point of Contact:** [Micaella Bruton](mailto:micaellabruton@gmail.com)
+
+### Dataset Summary
+
+The SpanishSRL dataset is a Spanish-language dataset of tokenized sentences and the semantic role for each token within a sentence. Standard semantic roles for Spanish are identified as well as verbal root; standard roles include "arg0|[agt, cau, exp, src]", "arg1|[ext, loc, pat, tem]", "arg2[atr, ben, efi, exp, ext, ins, loc]", "arg3[ben, ein, fin, ori]", "arg4[des, efi]", and "argM[adv, atr, cau, ext, fin, ins, loc, mnr, tmp]".
+
+### Languages
+
+The text in the dataset is in Spanish.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical data point comprises a tokenized sentence, tags for each token, and a sentence id number. An example from the SpanishSRL dataset looks as follows:
+```
+{'tokens': ['Ante', 'unas', 'mil', 'personas', ',', 'entre', 'ellas', 'la', 'ministra', 'de', 'Ciencia_y_Tecnología', ',', 'Anna_Birulés', ',', 'el', 'alcalde', 'de', 'Barcelona', ',', 'Joan_Clos', ',', 'la', 'Delegada', 'del', 'Gobierno', ',', 'Julia_García_Valdecasas', ',', 'y', 'una', 'nutrida', 'representación', 'del', 'gobierno', 'catalán', ',', 'Pujol', 'dio', 'un', 'toque', 'de', 'alerta', 'sobre', 'el', 'aumento', 'de', 'los', 'accidentes', 'laborales', '.'],
+ 'tags': [34, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 37, 0, 0, 0, 0, 28, 0, 0, 0, 0, 0, 0, 0],
+ 'ids': 66}
+```
+Tags are assigned an id number according to the index of its label as listed in:
+
+```python
+>>> dataset['train'].features['tags'].feature.names
+```
+
+### Data Fields
+
+- `tokens`: a list of strings
+- `tags`: a list of integers
+- `ids`: a sentence id, as an integer
+
+### Data Splits
+
+The data is split into a development, training, and test set. The final structure and split sizes are as follow:
+
+```
+DatasetDict({
+    dev: Dataset({
+        features: ['tokens', 'tags', 'ids'],
+        num_rows: 1654
+    })
+    test: Dataset({
+        features: ['tokens', 'tags', 'ids'],
+        num_rows: 1724
+    })
+    train: Dataset({
+        features: ['tokens', 'tags', 'ids'],
+        num_rows: 14328
+    })
+})
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+SpanishSRL was built to test the verbal indexing method as introduced in the publication listed in the citation against an established baseline.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was collected from the [2009 CoNLL Shared Task](https://ufal.mff.cuni.cz/conll2009-st/). For more information, please refer to the publication listed in the citation.
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was created by Micaella Bruton, as part of her Master's thesis.
+
+### Citation Information
+
+```
+@mastersthesis{bruton-galician-srl-23,
+    author = {Bruton, Micaella},
+    title = {BERTie Bott's Every Flavor Labels: A Tasty Guide to Developing a Semantic Role Labeling Model for Galician},
+    school = {Uppsala University},
+    year = {2023},
+    type = {Master's thesis},
+}
+```