hey @akshat-suwalka i think the reason why you’re getting a much lower score on the snli dataset is due to a misalignment between the label → label_id mappings in the model and dataset.
to explain what i mean, note that the config.json of the deberta model has the following mappings:
  "id2label": {
    "0": "CONTRADICTION",
    "1": "NEUTRAL",
    "2": "ENTAILMENT"
  },
  "label2id": {
    "CONTRADICTION": 0,
    "ENTAILMENT": 2,
    "NEUTRAL": 1
  }

while the snli dataset has the contradiction and neutral labels swapped:
id2label = {0: 'entailment', 1: 'neutral', 2: 'contradiction'}
label2id = {'contradiction': 2, 'entailment': 0, 'neutral': 1}

To fix this you can use the Dataset.align_labels_with_mapping function (docs):
hg_model_hub_name = "microsoft/deberta-base-mnli"

model = DebertaForSequenceClassification.from_pretrained(hg_model_hub_name)
config = model.config

snli = load_dataset("snli")
snli_aligned = snli.align_labels_with_mapping(label2id=config.label2id, label_column="label")

hth!