defaults models for tf and pt - update tests

transformers/pipelines.py

@@ -776,7 +776,10 @@ SUPPORTED_TASKS = {
         'tf': TFAutoModel if is_tf_available() else None,
         'pt': AutoModel if is_torch_available() else None,
         'default': {
-            'model': 'distilbert-base-uncased',
+            'model': {
+                'pt': 'distilbert-base-uncased',
+                'tf': 'distilbert-base-uncased',
+            },
             'config': None,
             'tokenizer': 'distilbert-base-uncased'
         }
@@ -786,7 +789,10 @@ SUPPORTED_TASKS = {
         'tf': TFAutoModelForSequenceClassification if is_tf_available() else None,
         'pt': AutoModelForSequenceClassification if is_torch_available() else None,
         'default': {
-            'model': 'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-pytorch_model.bin',
+            'model': {
+                'pt': 'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-pytorch_model.bin',
+                'tf': 'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-tf_model.h5',
+            },
             'config': 'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-config.json',
             'tokenizer': 'distilbert-base-uncased'
         }
@@ -796,7 +802,10 @@ SUPPORTED_TASKS = {
         'tf': TFAutoModelForTokenClassification if is_tf_available() else None,
         'pt': AutoModelForTokenClassification if is_torch_available() else None,
         'default': {
-            'model': 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-pytorch_model.bin',
+            'model': {
+                'pt':'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-pytorch_model.bin',
+                'tf': 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-tf_model.h5',
+            },
             'config': 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-config.json',
             'tokenizer': 'bert-large-cased'
         }
@@ -806,7 +815,10 @@ SUPPORTED_TASKS = {
         'tf': TFAutoModelForQuestionAnswering if is_tf_available() else None,
         'pt': AutoModelForQuestionAnswering if is_torch_available() else None,
         'default': {
-            'model': 'distilbert-base-uncased-distilled-squad',
+            'model': {
+                'pt': 'distilbert-base-uncased-distilled-squad',
+                'tf': 'distilbert-base-uncased-distilled-squad',
+            },
             'config': None,
             'tokenizer': 'distilbert-base-uncased'
         }
@@ -843,7 +855,8 @@ def pipeline(task: str, model: Optional = None,
 
     # Use default model/config/tokenizer for the task if no model is provided
     if model is None:
-        model, config, tokenizer = tuple(targeted_task['default'].values())
+        models, config, tokenizer = tuple(targeted_task['default'].values())
+        model = models[framework]
 
     # Try to infer tokenizer from model or config name (if provided as str)
     if tokenizer is None:

transformers/tests/pipelines_test.py

@@ -11,6 +11,20 @@ QA_FINETUNED_MODELS = {
     ('bert-base-uncased', 'distilbert-base-uncased-distilled-squad', None)
 }
 
+TF_QA_FINETUNED_MODELS = {
+    ('bert-base-uncased', 'bert-large-uncased-whole-word-masking-finetuned-squad', None),
+    ('bert-base-cased', 'bert-large-cased-whole-word-masking-finetuned-squad', None),
+    ('bert-base-uncased', 'distilbert-base-uncased-distilled-squad', None)
+}
+
+TF_NER_FINETUNED_MODELS = {
+    (
+        'bert-base-cased',
+        'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-tf_model.h5',
+        'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-finetuned-conll03-english-config.json'
+    )
+}
+
 NER_FINETUNED_MODELS = {
     (
         'bert-base-cased',
@@ -25,6 +39,20 @@ FEATURE_EXTRACT_FINETUNED_MODELS = {
    ('distilbert-base-uncased', 'distilbert-base-uncased', None)
 }
 
+TF_FEATURE_EXTRACT_FINETUNED_MODELS = {
+   ('bert-base-cased', 'bert-base-cased', None),
+   # ('xlnet-base-cased', 'xlnet-base-cased', None), # Disabled for now as it crash for TF2
+   ('distilbert-base-uncased', 'distilbert-base-uncased', None)
+}
+
+TF_TEXT_CLASSIF_FINETUNED_MODELS = {
+    (
+        'bert-base-uncased',
+        'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-tf_model.h5',
+        'https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-config.json'
+    )
+}
+
 TEXT_CLASSIF_FINETUNED_MODELS = {
     (
         'bert-base-uncased',
@@ -75,7 +103,7 @@ class MonoColumnInputTestCase(unittest.TestCase):
         mandatory_keys = {'entity', 'word', 'score'}
         valid_inputs = ['HuggingFace is solving NLP one commit at a time.', 'HuggingFace is based in New-York & Paris']
         invalid_inputs = [None]
-        for tokenizer, model, config in NER_FINETUNED_MODELS:
+        for tokenizer, model, config in TF_NER_FINETUNED_MODELS:
             nlp = pipeline(task='ner', model=model, config=config, tokenizer=tokenizer)
             self._test_mono_column_pipeline(nlp, valid_inputs, invalid_inputs, mandatory_keys)
 
@@ -93,7 +121,7 @@ class MonoColumnInputTestCase(unittest.TestCase):
         mandatory_keys = {'label'}
         valid_inputs = ['HuggingFace is solving NLP one commit at a time.', 'HuggingFace is based in New-York & Paris']
         invalid_inputs = [None]
-        for tokenizer, model, config in TEXT_CLASSIF_FINETUNED_MODELS:
+        for tokenizer, model, config in TF_TEXT_CLASSIF_FINETUNED_MODELS:
             nlp = pipeline(task='sentiment-analysis', model=model, config=config, tokenizer=tokenizer)
             self._test_mono_column_pipeline(nlp, valid_inputs, invalid_inputs, mandatory_keys)
 
@@ -109,7 +137,7 @@ class MonoColumnInputTestCase(unittest.TestCase):
     def test_tf_features_extraction(self):
         valid_inputs = ['HuggingFace is solving NLP one commit at a time.', 'HuggingFace is based in New-York & Paris']
         invalid_inputs = [None]
-        for tokenizer, model, config in FEATURE_EXTRACT_FINETUNED_MODELS:
+        for tokenizer, model, config in TF_FEATURE_EXTRACT_FINETUNED_MODELS:
             nlp = pipeline(task='sentiment-analysis', model=model, config=config, tokenizer=tokenizer)
             self._test_mono_column_pipeline(nlp, valid_inputs, invalid_inputs, {})
 
@@ -173,7 +201,7 @@ class MultiColumnInputTestCase(unittest.TestCase):
             {'question': 'What is does with empty context ?', 'context': None},
         ]
 
-        for tokenizer, model, config in QA_FINETUNED_MODELS:
+        for tokenizer, model, config in TF_QA_FINETUNED_MODELS:
             nlp = pipeline(task='question-answering', model=model, config=config, tokenizer=tokenizer)
             self._test_multicolumn_pipeline(nlp, valid_samples, invalid_samples, mandatory_output_keys)