Pipeline doc (#3055)

* Pipeline doc initial commit

* pipeline abstraction

* Remove modelcard argument from pipeline

* Task-specific pipelines can be instantiated with no model or tokenizer

* All pipelines doc

tests/test_pipelines.py

@@ -2,9 +2,16 @@ import unittest
 from typing import Iterable, List, Optional
 
 from transformers import pipeline
-from transformers.pipelines import Pipeline
+from transformers.pipelines import (
+    FeatureExtractionPipeline,
+    FillMaskPipeline,
+    NerPipeline,
+    Pipeline,
+    QuestionAnsweringPipeline,
+    TextClassificationPipeline,
+)
 
-from .utils import require_tf, require_torch
+from .utils import require_tf, require_torch, slow
 
 
 QA_FINETUNED_MODELS = [
@@ -304,3 +311,30 @@ class MultiColumnInputTestCase(unittest.TestCase):
         for tokenizer, model, config in TF_QA_FINETUNED_MODELS:
             nlp = pipeline(task="question-answering", model=model, config=config, tokenizer=tokenizer, framework="tf")
             self._test_multicolumn_pipeline(nlp, valid_samples, invalid_samples, mandatory_output_keys)
+
+
+class PipelineCommonTests(unittest.TestCase):
+
+    pipelines = (
+        NerPipeline,
+        FeatureExtractionPipeline,
+        QuestionAnsweringPipeline,
+        FillMaskPipeline,
+        TextClassificationPipeline,
+    )
+
+    @slow
+    @require_tf
+    def test_tf_defaults(self):
+        # Test that pipelines can be correctly loaded without any argument
+        for default_pipeline in self.pipelines:
+            with self.subTest(msg="Testing Torch defaults with PyTorch and {}".format(default_pipeline.task)):
+                default_pipeline(framework="tf")
+
+    @slow
+    @require_torch
+    def test_pt_defaults(self):
+        # Test that pipelines can be correctly loaded without any argument
+        for default_pipeline in self.pipelines:
+            with self.subTest(msg="Testing Torch defaults with PyTorch and {}".format(default_pipeline.task)):
+                default_pipeline(framework="pt")