infer entailment label id on zero shot pipeline (#8059)

* add entailment dim argument

* rename dim -> id

* fix last name change, style

* rm arg, auto-infer only

* typo

* rm superfluous import

tests/test_pipelines_zero_shot.py

@@ -1,4 +1,5 @@
 import unittest
+from copy import deepcopy
 
 from transformers.pipelines import Pipeline
 
@@ -18,6 +19,24 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
             sum += score
         self.assertAlmostEqual(sum, 1.0)
 
+    def _test_entailment_id(self, nlp: Pipeline):
+        config = nlp.model.config
+        original_config = deepcopy(config)
+
+        config.label2id = {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
+        self.assertEqual(nlp.entailment_id, -1)
+
+        config.label2id = {"entailment": 0, "neutral": 1, "contradiction": 2}
+        self.assertEqual(nlp.entailment_id, 0)
+
+        config.label2id = {"ENTAIL": 0, "NON-ENTAIL": 1}
+        self.assertEqual(nlp.entailment_id, 0)
+
+        config.label2id = {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
+        self.assertEqual(nlp.entailment_id, 2)
+
+        nlp.model.config = original_config
+
     def _test_pipeline(self, nlp: Pipeline):
         output_keys = {"sequence", "labels", "scores"}
         valid_mono_inputs = [
@@ -59,6 +78,8 @@ class ZeroShotClassificationPipelineTests(CustomInputPipelineCommonMixin, unitte
         ]
         self.assertIsNotNone(nlp)
 
+        self._test_entailment_id(nlp)
+
         for mono_input in valid_mono_inputs:
             mono_result = nlp(**mono_input)
             self.assertIsInstance(mono_result, dict)