fix(datasets): use fully qualified HF dataset ids

[zhenchaoni]

huggingface_hub now requires repository ids in namespace/name form, so the legacy bare canonical ids no longer resolve. The default calibration dataset was still requesting glue, which fails with:

HfUriError: Invalid HF URI 'hf://datasets/glue@.../.huggingface.yaml'.
Repository id must be 'namespace/name', got 'glue'.

This never surfaced as a hard failure. When TextDataset construction fails, universal_calib_dataset silently falls back to RandomDataset, so winml quantize still reported success while calibrating on random noise instead of real text. That silently degrades quantization accuracy for every text task routed through TextDataset (text-classification, feature-extraction, fill-mask, sentence-similarity, next-sentence-prediction, zero-shot-classification).

The fix is to use fully qualified dataset ids:

• glue -> nyu-mll/glue (calibration default in datasets/text.py)
• tweet_eval -> cardiffnlp/tweet_eval (e2e testset, recipe configs, and the matching baseline cache keys)

The baseline cache keys are derived from dataset_config["path"], so renaming the path required renaming the keys to keep the cached entries reachable. The recorded metrics are unchanged, so no baselines needed recomputing. Everything else is example paths in help text and docstrings.

Verified with winml export followed by winml quantize on philschmid/tiny-bert-sst2-distilled and cross-encoder/nli-deberta-v3-small. Before the fix both runs logged the HfUriError and fell back to RandomDataset; after the fix both load real MRPC sentence pairs.

This also covers the zero-shot problem reported in #1261. zero-shot-classification routes through the same TextDataset, and MRPC is natively a sentence-pair dataset, so calibration now gets the premise/hypothesis style input those models expect.