Pipelines: miscellanea of QoL improvements and small features... (#4632)

* [hf_api] Attach all unknown attributes for future-proof compatibility

* [Pipeline] NerPipeline is really a TokenClassificationPipeline

* modelcard.py: I don't think we need to force the download

* Remove config, tokenizer from SUPPORTED_TASKS as we're moving to one model = one weight + one tokenizer

* FillMaskPipeline: also output token in string form

* TextClassificationPipeline: option to return all scores, not just the argmax

* Update docs/source/main_classes/pipelines.rst

docs/source/main_classes/pipelines.rst

@@ -8,7 +8,7 @@ Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction an
 There are two categories of pipeline abstractions to be aware about:
 
 - The :class:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines
-- The other task-specific pipelines, such as :class:`~transformers.NerPipeline`
+- The other task-specific pipelines, such as :class:`~transformers.TokenClassificationPipeline`
   or :class:`~transformers.QuestionAnsweringPipeline`
 
 The pipeline abstraction
@@ -30,15 +30,15 @@ Parent class: Pipeline
 .. autoclass:: transformers.Pipeline
     :members: predict, transform, save_pretrained
 
-NerPipeline
-==========================================
-
-.. autoclass:: transformers.NerPipeline
-
 TokenClassificationPipeline
 ==========================================
 
-This class is an alias of the :class:`~transformers.NerPipeline` defined above. Please refer to that pipeline for
+.. autoclass:: transformers.TokenClassificationPipeline
+
+NerPipeline
+==========================================
+
+This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined above. Please refer to that pipeline for
 documentation and usage examples.
 
 FillMaskPipeline

src/transformers/hf_api.py

@@ -64,6 +64,8 @@ class S3Object:
         self.lastModified = lastModified
         self.size = size
         self.rfilename = rfilename
+        for k, v in kwargs.items():
+            setattr(self, k, v)
 
 
 class ModelInfo:
@@ -78,7 +80,7 @@ class ModelInfo:
         author: Optional[str] = None,
         downloads: Optional[int] = None,
         tags: List[str] = [],
-        siblings: List[Dict] = [],  # list of files that constitute the model
+        siblings: Optional[List[Dict]] = None,  # list of files that constitute the model
         **kwargs
     ):
         self.modelId = modelId
@@ -86,7 +88,9 @@ class ModelInfo:
         self.author = author
         self.downloads = downloads
         self.tags = tags
-        self.siblings = [S3Object(**x) for x in siblings]
+        self.siblings = [S3Object(**x) for x in siblings] if siblings is not None else None
+        for k, v in kwargs.items():
+            setattr(self, k, v)
 
 
 class HfApi:

src/transformers/modelcard.py

@@ -149,9 +149,7 @@ class ModelCard:
 
         try:
             # Load from URL or cache if already cached
-            resolved_model_card_file = cached_path(
+            resolved_model_card_file = cached_path(model_card_file, cache_dir=cache_dir, proxies=proxies)
-                model_card_file, cache_dir=cache_dir, force_download=True, proxies=proxies, resume_download=False
-            )
             if resolved_model_card_file is None:
                 raise EnvironmentError
             if resolved_model_card_file == model_card_file:

src/transformers/pipelines.py

@@ -28,7 +28,7 @@ from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence,
 
 import numpy as np
 
-from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoConfig
+from .configuration_auto import AutoConfig
 from .configuration_utils import PretrainedConfig
 from .data import SquadExample, squad_convert_examples_to_features
 from .file_utils import is_tf_available, is_torch_available
@@ -717,10 +717,23 @@ class TextClassificationPipeline(Pipeline):
             on the associated CUDA device id.
     """
 
+    def __init__(self, return_all_scores: bool = False, **kwargs):
+        super().__init__(**kwargs)
+
+        self.return_all_scores = return_all_scores
+
     def __call__(self, *args, **kwargs):
         outputs = super().__call__(*args, **kwargs)
         scores = np.exp(outputs) / np.exp(outputs).sum(-1, keepdims=True)
-        return [{"label": self.model.config.id2label[item.argmax()], "score": item.max().item()} for item in scores]
+        if self.return_all_scores:
+            return [
+                [{"label": self.model.config.id2label[i], "score": score} for i, score in enumerate(item)]
+                for item in scores
+            ]
+        else:
+            return [
+                {"label": self.model.config.id2label[item.argmax()], "score": item.max().item()} for item in scores
+            ]
 
 
 class FillMaskPipeline(Pipeline):
@@ -813,7 +826,14 @@ class FillMaskPipeline(Pipeline):
                 tokens[masked_index] = p
                 # Filter padding out:
                 tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
-                result.append({"sequence": self.tokenizer.decode(tokens), "score": v, "token": p})
+                result.append(
+                    {
+                        "sequence": self.tokenizer.decode(tokens),
+                        "score": v,
+                        "token": p,
+                        "token_str": self.tokenizer.convert_ids_to_tokens(p),
+                    }
+                )
 
             # Append
             results += [result]
@@ -823,7 +843,7 @@ class FillMaskPipeline(Pipeline):
         return results
 
 
-class NerPipeline(Pipeline):
+class TokenClassificationPipeline(Pipeline):
     """
     Named Entity Recognition pipeline using ModelForTokenClassification head. See the
     `named entity recognition usage <../usage.html#named-entity-recognition>`__ examples for more information.
@@ -987,7 +1007,7 @@ class NerPipeline(Pipeline):
         return entity_group
 
 
-TokenClassificationPipeline = NerPipeline
+NerPipeline = TokenClassificationPipeline
 
 
 class QuestionAnsweringArgumentHandler(ArgumentHandler):
@@ -1577,11 +1597,7 @@ SUPPORTED_TASKS = {
         "impl": FeatureExtractionPipeline,
         "tf": TFAutoModel if is_tf_available() else None,
         "pt": AutoModel if is_torch_available() else None,
-        "default": {
+        "default": {"model": {"pt": "distilbert-base-cased", "tf": "distilbert-base-cased"}},
-            "model": {"pt": "distilbert-base-cased", "tf": "distilbert-base-cased"},
-            "config": None,
-            "tokenizer": "distilbert-base-cased",
-        },
     },
     "sentiment-analysis": {
         "impl": TextClassificationPipeline,
@@ -1592,12 +1608,10 @@ SUPPORTED_TASKS = {
                 "pt": "distilbert-base-uncased-finetuned-sst-2-english",
                 "tf": "distilbert-base-uncased-finetuned-sst-2-english",
             },
-            "config": "distilbert-base-uncased-finetuned-sst-2-english",
-            "tokenizer": "distilbert-base-uncased",
         },
     },
     "ner": {
-        "impl": NerPipeline,
+        "impl": TokenClassificationPipeline,
         "tf": TFAutoModelForTokenClassification if is_tf_available() else None,
         "pt": AutoModelForTokenClassification if is_torch_available() else None,
         "default": {
@@ -1605,8 +1619,6 @@ SUPPORTED_TASKS = {
                 "pt": "dbmdz/bert-large-cased-finetuned-conll03-english",
                 "tf": "dbmdz/bert-large-cased-finetuned-conll03-english",
             },
-            "config": "dbmdz/bert-large-cased-finetuned-conll03-english",
-            "tokenizer": "bert-large-cased",
         },
     },
     "question-answering": {
@@ -1615,61 +1627,43 @@ SUPPORTED_TASKS = {
         "pt": AutoModelForQuestionAnswering if is_torch_available() else None,
         "default": {
             "model": {"pt": "distilbert-base-cased-distilled-squad", "tf": "distilbert-base-cased-distilled-squad"},
-            "config": None,
-            "tokenizer": ("distilbert-base-cased", {"use_fast": False}),
         },
     },
     "fill-mask": {
         "impl": FillMaskPipeline,
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
-        "default": {
+        "default": {"model": {"pt": "distilroberta-base", "tf": "distilroberta-base"}},
-            "model": {"pt": "distilroberta-base", "tf": "distilroberta-base"},
-            "config": None,
-            "tokenizer": ("distilroberta-base", {"use_fast": False}),
-        },
     },
     "summarization": {
         "impl": SummarizationPipeline,
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
-        "default": {"model": {"pt": "facebook/bart-large-cnn", "tf": "t5-small"}, "config": None, "tokenizer": None},
+        "default": {"model": {"pt": "facebook/bart-large-cnn", "tf": "t5-small"}},
     },
     "translation_en_to_fr": {
         "impl": TranslationPipeline,
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
-        "default": {
+        "default": {"model": {"pt": "t5-base", "tf": "t5-base"}},
-            "model": {"pt": "t5-base", "tf": "t5-base"},
-            "config": None,
-            "tokenizer": ("t5-base", {"use_fast": False}),
-        },
     },
     "translation_en_to_de": {
         "impl": TranslationPipeline,
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
-        "default": {
+        "default": {"model": {"pt": "t5-base", "tf": "t5-base"}},
-            "model": {"pt": "t5-base", "tf": "t5-base"},
-            "config": None,
-            "tokenizer": ("t5-base", {"use_fast": False}),
-        },
     },
     "translation_en_to_ro": {
         "impl": TranslationPipeline,
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
-        "default": {
+        "default": {"model": {"pt": "t5-base", "tf": "t5-base"}},
-            "model": {"pt": "t5-base", "tf": "t5-base"},
-            "config": None,
-            "tokenizer": ("t5-base", {"use_fast": False}),
-        },
     },
     "text-generation": {
         "impl": TextGenerationPipeline,
         "tf": TFAutoModelWithLMHead if is_tf_available() else None,
         "pt": AutoModelWithLMHead if is_torch_available() else None,
-        "default": {"model": {"pt": "gpt2", "tf": "gpt2"}, "config": None, "tokenizer": "gpt2"},
+        "default": {"model": {"pt": "gpt2", "tf": "gpt2"}},
     },
 }
 
@@ -1698,11 +1692,12 @@ def pipeline(
 
             - "feature-extraction": will return a :class:`~transformers.FeatureExtractionPipeline`
             - "sentiment-analysis": will return a :class:`~transformers.TextClassificationPipeline`
-            - "ner": will return a :class:`~transformers.NerPipeline`
+            - "ner": will return a :class:`~transformers.TokenClassificationPipeline`
             - "question-answering": will return a :class:`~transformers.QuestionAnsweringPipeline`
             - "fill-mask": will return a :class:`~transformers.FillMaskPipeline`
             - "summarization": will return a :class:`~transformers.SummarizationPipeline`
             - "translation_xx_to_yy": will return a :class:`~transformers.TranslationPipeline`
+            - "text-generation": will return a :class:`~transformers.TextGenerationPipeline`
         model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
             The model that will be used by the pipeline to make predictions. This can be :obj:`None`,
             a model identifier or an actual pre-trained model inheriting from
@@ -1759,14 +1754,13 @@ def pipeline(
 
     # Use default model/config/tokenizer for the task if no model is provided
     if model is None:
-        models, config, tokenizer = [targeted_task["default"][k] for k in ["model", "config", "tokenizer"]]
+        model = targeted_task["default"]["model"][framework]
-        model = models[framework]
 
     # Try to infer tokenizer from model or config name (if provided as str)
     if tokenizer is None:
-        if isinstance(model, str) and model in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
+        if isinstance(model, str):
             tokenizer = model
-        elif isinstance(config, str) and config in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
+        elif isinstance(config, str):
             tokenizer = config
         else:
             # Impossible to guest what is the right tokenizer here