Improve dataset card: Add paper, code, project links, task category, and sample usage

This PR significantly enhances the `Code-Regression` dataset card by:
- Adding direct links to the associated paper, GitHub repository, and project page at the top of the README for quick access to source information.
- Updating the introductory paragraph to provide clearer context about the dataset's role in "code-to-metric regression."
- Incorporating `task_categories: ['text-generation']` into the metadata, making the dataset more discoverable under relevant AI task filters.
- Adding a dedicated "Sample Usage with `RegressLM`" section, featuring a Python code snippet from the official GitHub README that demonstrates how to perform inference with models trained using this dataset.
- Wrapping the existing BibTeX citations with ````bibtex` markers for better formatting.

README.md

@@ -7,12 +7,15 @@ tags:
 - leetcode
 - kernel
 - text regression
+task_categories:
+- text-generation
 ---
+
 # Code-Regression
 
-A unified regression dataset collated from three sources (APPS, KBSS, CDSS) along with our own custom profiling for training and evaluating regression models that map code strings to a target metric.
+[Paper](https://huggingface.co/papers/2509.26476) | [GitHub Repository](https://github.com/google-deepmind/regress-lm/tree/main) | [Project Page](https://research.google/blog/simulating-large-systems-with-regression-language-models/)
 
-This is part of a larger research paper on regression-language models for code. 
+A unified regression dataset collated from three sources (APPS, KBSS, CDSS) along with our own custom profiling for training and evaluating regression models that map code strings to a target metric. This dataset supports "code-to-metric regression," which involves predicting numeric outcomes of code executions using Regression Language Models (RLM), as described in the linked paper.
 
 **Link for Graph-Regression dataset**: https://huggingface.co/datasets/akhauriyash/GraphArch-Regression
 
@@ -49,6 +52,26 @@ from datasets import load_dataset
 ds = load_dataset("akhauriyash/Code-Regression")
 ```
 
+## Sample Usage with `RegressLM`
+
+The `regress_lm` library provides the `RegressLM` class for decoding floating-point predictions from a given input and fine-tuning against new data. Below is an example of how to instantiate `RegressLM` and use it for inference.
+
+```python
+from regress_lm import core
+from regress_lm import rlm
+
+# Create RegressLM from scratch. Optionally, use `from_t5gemma_encoder`.
+reg_lm = rlm.RegressLM.from_scratch(max_input_len=2048)
+
+# Example (x,y) pairs, which can be fine-tuned against.
+examples = [core.Example(x='hello', y=0.3), core.Example(x='world', y=-0.3)]
+reg_lm.fine_tune(examples)
+
+# Query inputs.
+query1, query2 = core.ExampleInput(x='hi'), core.ExampleInput(x='bye')
+samples1, samples2 = reg_lm.sample([query1, query2], num_samples=128)
+```
+
 ## Testing Code-Regression with a basic Gemma RLM model
 
 Use the code below as reference for evaluating a basic RegressLM model ( better, more models to come! :) )
@@ -81,9 +104,12 @@ for SPACE in spaces:
                 if SPACE != "CDSS" or language is None or lang == language:
                     targets.append(float(row["target"]))
                     if SPACE == "CDSS":
-                        inputs.append(f"# {SPACE}\n# Language: {lang}\n{row['input']}")
+                        inputs.append(f"# {SPACE}
+# Language: {lang}
+{row['input']}")
                     else:
-                        inputs.append(f"{SPACE}\n{row['input']}")
+                        inputs.append(f"{SPACE}
+{row['input']}")
             except: continue
             if len(inputs) >= MAX_ITEMS: break
     preds = []
@@ -129,7 +155,7 @@ Paliskara, S., & Saroufim, M. (2025). KernelBook. https://huggingface.co/dataset
 
 If you found this dataset useful for your research, please cite the original sources above as well as:
 
-```
+```bibtex
 @article{akhauri2025regressionlanguagemodelscode,
       title={Regression Language Models for Code}, 
       author={Yash Akhauri and Xingyou Song and Arissa Wongpanich and Bryan Lewandowski and Mohamed S. Abdelfattah},