Update README.md

README.md

@@ -57,30 +57,39 @@ configs:
   - split: test
     path: data/test-*
 ---
-# Dataset Card for "gids"
-
-## Dataset Description
-- **Paper:** [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://arxiv.org/abs/1804.06987)
-- **Size of downloaded dataset files:** 8.94 MB
-- **Size of the generated dataset:** 11.82 MB
-
-### Dataset Summary
-This is the GIDS dataset (see information below) reformatted into entity1, entity2, text, entity1Type, entity2Type, and relation label features to facilitate downstream applications.
-The Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction.
-GIDS is seeded from the human-judged Google relation extraction corpus. 
-See the paper for full details: [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://arxiv.org/abs/1804.06987)
-
-### Supported Tasks and Leaderboards
-- **Tasks:** Relation Classification
-- **Leaderboards:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Languages
-The language in the dataset is English.
-## Dataset Structure
-### Data Instances
-
-#### gids
-- **Size of downloaded dataset files:** 8.94 MB
-- **Size of the generated dataset:** 8.5 MB
+
+# GIDS (Relation Extraction)
+
+GIDS (**Google-IISc Distant Supervision**) is a **general-domain sentence-level relation
+extraction (RE)** dataset. Each example pairs a sentence (mentioning a head and tail entity)
+with the relation that holds between the two entities. It was built by extending the
+human-judged **Google Relation Extraction** corpus with additional distantly-supervised
+sentences, and is designed so that every entity pair has at least one sentence that expresses
+the target relation (reducing the noise typical of distant supervision).
+
+This copy is packaged for the paper **"Sub-Billion, Super-Frontier: Fine-Tuned Small Language
+Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction"**
+(Christou & Tsoumakas, 2026) [arXiv:2606.22606](https://arxiv.org/abs/2606.22606). Rows are
+pre-formatted as instruction prompts so the dataset can be used directly for prompt-conditioned
+fine-tuning and evaluation.
+
+## Dataset structure
+
+Splits: `train`, `validation`, `test`.
+
+| Column | Description |
+|---|---|
+| `prompt_0_shot` | Zero-shot instruction prompt (task instructions + the input sentence). |
+| `prompt_2_shot` | Same prompt with 2 in-context demonstrations prepended. |
+| `prompt_5_shot` | Same prompt with 5 in-context demonstrations prepended. |
+| `relation` | Gold relation label (the target/completion). |
+
+The `relation` column holds a small set of Freebase-style relation types (e.g. birth/death place
+and education-related relations) plus a no-relation / `NA` label; inspect the column for the exact
+set. The three `prompt_*` columns are alternative renderings of the **same** example at different
+shot counts, so pick one shot setting per experiment rather than concatenating them.
+
+
 An example of 'train' looks as follows:
 ```json
 {
@@ -94,66 +103,40 @@ An example of 'train' looks as follows:
 ```
 
 
-### Data Splits
+## Data Splits
 
 |      | Train | Dev  | Test |
 |------|-------|------|------|
 | GIDS | 11297 | 1864 | 5663 |
 
-## Dataset Creation
-### Curation Rationale
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
+## Usage
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+```python
+from datasets import load_dataset
 
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
+ds = load_dataset("Despina/gids")
+print(ds["test"][0]["prompt_2_shot"])  # formatted input
+print(ds["test"][0]["relation"])       # gold label
+```
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Source and licensing
 
-### Licensing Information
+GIDS is derived from the **Google Relation Extraction** corpus (distantly extended over
+Wikipedia text) and was introduced by Jat, Khandelwal & Talukdar (2018). It is available for
+research use; please respect the terms of the underlying Google Relation Extraction corpus and
+cite the original GIDS resource alongside the paper below.
 
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## Citation
 
-### Citation Information
+**If you use this dataset, please cite our paper:**
 
+```bibtex
+@article{christou2026subbillion,
+  title        = {Sub-Billion, Super-Frontier: Small Language Models Rival
+                  Zero-Shot Frontier LLMs on General and Literary Relation Extraction},
+  author       = {Christou, Despina and Tsoumakas, Grigorios},
+  journal      = {arXiv preprint arXiv:2606.22606},
+  year         = {2026},
+  url          = {https://arxiv.org/abs/2606.22606}
+}
 ```
-
-```