[FEATURE]: Data Contamination Detection via Configurable Benchmark Filtering

### Feature and its Use Cases

### What is the feature?

Introduce an optional preprocessing module that detects potential **data contamination (test leakage)** by scanning the dataset for n-gram overlaps with evaluation benchmarks (e.g., MMLU, HumanEval, GSM8K).

Instead of hardcoding a fixed set of benchmarks, the benchmark selection could be **configurable** via:

- a CLI flag (e.g., `--benchmarks`)
- or a configuration file (e.g., `benchmarks.yaml`)

The pipeline could dynamically load the selected benchmark datasets (via the Hugging Face `datasets` library or local files), extract their text, and construct a **Bloom filter** from their n-grams.

During dataset processing, text chunks would be checked against this Bloom filter to detect potential overlaps with benchmark data.

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### How would users benefit from it?

While the project currently provides cryptographic verification of dataset artifacts, it does not yet provide a mechanism to verify that the dataset is free from **benchmark contamination**.

This feature would allow researchers to verify that their training dataset does not overlap with specific evaluation benchmarks, improving the **transparency, reliability, and credibility** of model evaluation results.

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### What scenarios would this feature address?

- Detecting and filtering benchmark datasets to prevent **test leakage**.
- Allowing organizations to verify datasets against **internal evaluation sets or proprietary factual checks**.
- Recording which benchmark datasets were checked in the final `dataset_manifest.json`.

Example manifest entry:

```json
{
"contamination_checks_passed": ["gsm8k", "mmlu"]
}
```
## Additional Context

### Possible Implementation Approach

#### 1. Configuration Input

Add support for a `--benchmarks` CLI argument or a `benchmarks.yaml` configuration file.

If no configuration is provided, a default benchmark suite (e.g., MMLU, GSM8K) could be used for convenience.

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#### 2. Filter Construction

Load the specified benchmark datasets using the Hugging Face `datasets` library or local files.

Extract text from benchmark questions/prompts, split them into n-grams (e.g., 10–13 tokens), and insert them into a Bloom filter.

The Bloom filter can be serialized and cached locally (e.g., `filter.bin`) to avoid recomputation.

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#### 3. Pipeline Integration

During dataset processing, text chunks are checked against the Bloom filter.

If a potential match is detected:

- perform an exact string comparison to avoid Bloom filter false positives
- optionally discard or redact the contaminated chunk

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#### 4. Manifest Update

Extend the `dataset_manifest.json` generation logic to include metadata describing:

- which benchmark datasets were used
- dataset versions
- contamination check results

This ensures **transparent and reproducible dataset verification**.

### Code of Conduct
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- [x] I have searched existing issues to avoid duplicates