Tokenizers: ability to load from model subfolder (#8586)

* <small>tiny typo</small>

* Tokenizers: ability to load from model subfolder

* use subfolder for local files as well

* Uniformize model shortcut name => model id

* from s3 => from huggingface.co
Co-authored-by: Quentin Lhoest <lhoest.q@gmail.com>

examples/rag/finetune.sh

@@ -7,7 +7,7 @@ export PYTHONPATH="../":"${PYTHONPATH}"
 python examples/rag/finetune.py \
     --data_dir $DATA_DIR \
     --output_dir $OUTPUT_DIR \
-    --model_name_or_path $MODLE_NAME_OR_PATH \
+    --model_name_or_path $MODEL_NAME_OR_PATH \
     --model_type rag_sequence \
     --fp16 \
     --gpus 8 \

examples/seq2seq/finetune_trainer.py

@@ -43,7 +43,8 @@ class ModelArguments:
         default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
     )
     cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
     )
     freeze_encoder: bool = field(default=False, metadata={"help": "Whether tp freeze the encoder."})
     freeze_embeds: bool = field(default=False, metadata={"help": "Whether  to freeze the embeddings."})

examples/text-classification/run_glue.py

@@ -124,7 +124,8 @@ class ModelArguments:
         default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
     )
     cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
     )
     use_fast_tokenizer: bool = field(
         default=True,

examples/text-classification/run_tf_glue.py

@@ -117,7 +117,8 @@ class ModelArguments:
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
     )
 
 

examples/text-classification/run_tf_text_classification.py

@@ -182,7 +182,8 @@ class ModelArguments:
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
     )
 
 

examples/text-classification/run_xnli.py

@@ -406,7 +406,7 @@ def main():
         "--cache_dir",
         default=None,
         type=str,
-        help="Where do you want to store the pre-trained models downloaded from s3",
+        help="Where do you want to store the pre-trained models downloaded from huggingface.co",
     )
     parser.add_argument(
         "--max_seq_length",

examples/token-classification/run_ner.py

@@ -60,7 +60,8 @@ class ModelArguments:
         default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
     )
     cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
     )
 
 

examples/token-classification/run_ner_old.py

@@ -65,7 +65,8 @@ class ModelArguments:
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
     )
 
 

examples/token-classification/run_tf_ner.py

@@ -67,7 +67,8 @@ class ModelArguments:
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
     )
 
 

hubconf.py

@@ -25,7 +25,7 @@ def config(*args, **kwargs):
                 # Using torch.hub !
                 import torch
 
-                config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased')  # Download configuration from S3 and cache.
+                config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased')  # Download configuration from huggingface.co and cache.
                 config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
                 config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json')
                 config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attentions=True, foo=False)
@@ -45,7 +45,7 @@ def tokenizer(*args, **kwargs):
         # Using torch.hub !
         import torch
 
-        tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased')    # Download vocabulary from S3 and cache.
+        tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased')    # Download vocabulary from huggingface.co and cache.
         tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', './test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
 
     """
@@ -59,7 +59,7 @@ def model(*args, **kwargs):
             # Using torch.hub !
             import torch
 
-            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
             model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
             model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
             assert model.config.output_attentions == True
@@ -78,7 +78,7 @@ def modelWithLMHead(*args, **kwargs):
         # Using torch.hub !
         import torch
 
-        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
         model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
         model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
         assert model.config.output_attentions == True
@@ -96,7 +96,7 @@ def modelForSequenceClassification(*args, **kwargs):
             # Using torch.hub !
             import torch
 
-            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
             model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
             model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
             assert model.config.output_attentions == True
@@ -115,7 +115,7 @@ def modelForQuestionAnswering(*args, **kwargs):
         # Using torch.hub !
         import torch
 
-        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
         model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
         model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
         assert model.config.output_attentions == True

src/transformers/commands/user.py

@@ -31,7 +31,7 @@ class UserCommands(BaseTransformersCLICommand):
         ls_parser.add_argument("--organization", type=str, help="Optional: organization namespace.")
         ls_parser.set_defaults(func=lambda args: ListObjsCommand(args))
         rm_parser = s3_subparsers.add_parser("rm")
-        rm_parser.add_argument("filename", type=str, help="individual object filename to delete from S3.")
+        rm_parser.add_argument("filename", type=str, help="individual object filename to delete from huggingface.co.")
         rm_parser.add_argument("--organization", type=str, help="Optional: organization namespace.")
         rm_parser.set_defaults(func=lambda args: DeleteObjCommand(args))
         upload_parser = s3_subparsers.add_parser("upload", help="Upload a file to S3.")

src/transformers/configuration_utils.py

@@ -291,10 +291,9 @@ class PretrainedConfig(object):
             pretrained_model_name_or_path (:obj:`str`):
                 This can be either:
 
-                - the `shortcut name` of a pretrained model configuration to load from cache or download, e.g.,
-                  ``bert-base-uncased``.
-                - the `identifier name` of a pretrained model configuration that was uploaded to our S3 by any user,
-                  e.g., ``dbmdz/bert-base-german-cased``.
+                - a string, the `model id` of a pretrained model configuration hosted inside a model repo on
+                  huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or
+                  namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``.
                 - a path to a `directory` containing a configuration file saved using the
                   :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g., ``./my_model_directory/``.
                 - a path or url to a saved configuration JSON `file`, e.g.,
@@ -333,7 +332,7 @@ class PretrainedConfig(object):
 
             # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
             # derived class: BertConfig
-            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
+            config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from huggingface.co and cache.
             config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
             config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
             config = BertConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)

src/transformers/file_utils.py

@@ -855,7 +855,9 @@ def is_remote_url(url_or_filename):
     return parsed.scheme in ("http", "https")
 
 
-def hf_bucket_url(model_id: str, filename: str, revision: Optional[str] = None, mirror=None) -> str:
+def hf_bucket_url(
+    model_id: str, filename: str, subfolder: Optional[str] = None, revision: Optional[str] = None, mirror=None
+) -> str:
     """
     Resolve a model identifier, a file name, and an optional revision id, to a huggingface.co-hosted url, redirecting
     to Cloudfront (a Content Delivery Network, or CDN) for large files.
@@ -872,6 +874,9 @@ def hf_bucket_url(model_id: str, filename: str, revision: Optional[str] = None,
     its sha1 if stored in git, or its sha256 if stored in git-lfs. Files cached locally from transformers before v3.5.0
     are not shared with those new files, because the cached file's name contains a hash of the url (which changed).
     """
+    if subfolder is not None:
+        filename = f"{subfolder}/{filename}"
+
     if mirror:
         endpoint = PRESET_MIRROR_DICT.get(mirror, mirror)
         legacy_format = "/" not in model_id

src/transformers/generation_tf_utils.py

@@ -148,12 +148,12 @@ class TFGenerationMixin:
         Examples::
 
             tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
-            model = TFAutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
+            model = TFAutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from huggingface.co and cache.
             outputs = model.generate(max_length=40)  # do greedy decoding
             print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
 
             tokenizer = AutoTokenizer.from_pretrained('openai-gpt')   # Initialize tokenizer
-            model = TFAutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
+            model = TFAutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from huggingface.co and cache.
             input_context = 'The dog'
             input_ids = tokenizer.encode(input_context, return_tensors='tf')  # encode input context
             outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
@@ -161,7 +161,7 @@ class TFGenerationMixin:
                 print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
 
             tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
-            model = TFAutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
+            model = TFAutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from huggingface.co and cache.
             input_context = 'The dog'
             input_ids = tokenizer.encode(input_context, return_tensors='tf')  # encode input context
             outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True)  # generate 3 candidates using sampling
@@ -169,14 +169,14 @@ class TFGenerationMixin:
                 print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))
 
             tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer
-            model = TFAutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from S3 and cache.
+            model = TFAutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from huggingface.co and cache.
             input_context = 'Legal My neighbor is'  # "Legal" is one of the control codes for ctrl
             input_ids = tokenizer.encode(input_context, return_tensors='tf')  # encode input context
             outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)  # generate sequences
             print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))
 
             tokenizer = AutoTokenizer.from_pretrained('gpt2')   # Initialize tokenizer
-            model = TFAutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from S3 and cache.
+            model = TFAutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from huggingface.co and cache.
             input_context = 'My cute dog'
             bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
             input_ids = tokenizer.encode(input_context, return_tensors='tf')  # encode input context

src/transformers/modelcard.py

@@ -87,10 +87,9 @@ class ModelCard:
         Parameters:
             pretrained_model_name_or_path: either:
 
-                - a string with the `shortcut name` of a pre-trained model card to load from cache or download, e.g.:
-                  ``bert-base-uncased``.
-                - a string with the `identifier name` of a pre-trained model card that was user-uploaded to our S3,
-                  e.g.: ``dbmdz/bert-base-german-cased``.
+                - a string, the `model id` of a pretrained model card hosted inside a model repo on huggingface.co.
+                  Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a
+                  user or organization name, like ``dbmdz/bert-base-german-cased``.
                 - a path to a `directory` containing a model card file saved using the
                   :func:`~transformers.ModelCard.save_pretrained` method, e.g.: ``./my_model_directory/``.
                 - a path or url to a saved model card JSON `file`, e.g.: ``./my_model_directory/modelcard.json``.
@@ -124,7 +123,7 @@ class ModelCard:
 
         Examples::
 
-            modelcard = ModelCard.from_pretrained('bert-base-uncased')    # Download model card from S3 and cache.
+            modelcard = ModelCard.from_pretrained('bert-base-uncased')    # Download model card from huggingface.co and cache.
             modelcard = ModelCard.from_pretrained('./test/saved_model/')  # E.g. model card was saved using `save_pretrained('./test/saved_model/')`
             modelcard = ModelCard.from_pretrained('./test/saved_model/modelcard.json')
             modelcard = ModelCard.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)

src/transformers/modeling_tf_utils.py

@@ -544,10 +544,9 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
             pretrained_model_name_or_path (:obj:`str`, `optional`):
                 Can be either:
 
-                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
-                      ``bert-base-uncased``.
-                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
-                      ``dbmdz/bert-base-german-cased``.
+                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
+                      a user or organization name, like ``dbmdz/bert-base-german-cased``.
                     - A path to a `directory` containing model weights saved using
                       :func:`~transformersTF.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                     - A path or url to a `PyTorch state_dict save file` (e.g, ``./pt_model/pytorch_model.bin``). In
@@ -568,8 +567,8 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can
                 be automatically loaded when:
 
-                    - The model is a model provided by the library (loaded with the `shortcut name` string of a
-                      pretrained model).
+                    - The model is a model provided by the library (loaded with the `model id` string of a pretrained
+                      model).
                     - The model was saved using :func:`~transformers.TFPreTrainedModel.save_pretrained` and is reloaded
                       by supplying the save directory.
                     - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
@@ -618,7 +617,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
         Examples::
 
             >>> from transformers import BertConfig, TFBertModel
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = TFBertModel.from_pretrained('bert-base-uncased')
             >>> # Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
             >>> model = TFBertModel.from_pretrained('./test/saved_model/')

src/transformers/modeling_utils.py

@@ -758,10 +758,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
             pretrained_model_name_or_path (:obj:`str`, `optional`):
                 Can be either:
 
-                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
-                      ``bert-base-uncased``.
-                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
-                      ``dbmdz/bert-base-german-cased``.
+                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
+                      a user or organization name, like ``dbmdz/bert-base-german-cased``.
                     - A path to a `directory` containing model weights saved using
                       :func:`~transformers.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                     - A path or url to a `tensorflow index checkpoint file` (e.g, ``./tf_model/model.ckpt.index``). In
@@ -781,8 +780,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can
                 be automatically loaded when:
 
-                    - The model is a model provided by the library (loaded with the `shortcut name` string of a
-                      pretrained model).
+                    - The model is a model provided by the library (loaded with the `model id` string of a pretrained
+                      model).
                     - The model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded
                       by supplying the save directory.
                     - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
@@ -838,7 +837,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
         Examples::
 
             >>> from transformers import BertConfig, BertModel
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = BertModel.from_pretrained('bert-base-uncased')
             >>> # Model was saved using `save_pretrained('./test/saved_model/')` (for example purposes, not runnable).
             >>> model = BertModel.from_pretrained('./test/saved_model/')

src/transformers/models/auto/configuration_auto.py

@@ -277,10 +277,9 @@ class AutoConfig:
             pretrained_model_name_or_path (:obj:`str`):
                 Can be either:
 
-                    - A string with the `shortcut name` of a pretrained model configuration to load from cache or
-                      download, e.g., ``bert-base-uncased``.
-                    - A string with the `identifier name` of a pretrained model configuration that was user-uploaded to
-                      our S3, e.g., ``dbmdz/bert-base-german-cased``.
+                    - A string, the `model id` of a pretrained model configuration hosted inside a model repo on
+                      huggingface.co. Valid model ids can be located at the root-level, like ``bert-base-uncased``, or
+                      namespaced under a user or organization name, like ``dbmdz/bert-base-german-cased``.
                     - A path to a `directory` containing a configuration file saved using the
                       :meth:`~transformers.PretrainedConfig.save_pretrained` method, or the
                       :meth:`~transformers.PreTrainedModel.save_pretrained` method, e.g., ``./my_model_directory/``.
@@ -317,10 +316,10 @@ class AutoConfig:
 
             >>> from transformers import AutoConfig
 
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
 
-            >>> # Download configuration from S3 (user-uploaded) and cache.
+            >>> # Download configuration from huggingface.co (user-uploaded) and cache.
             >>> config = AutoConfig.from_pretrained('dbmdz/bert-base-german-cased')
 
             >>> # If configuration file is in a directory (e.g., was saved using `save_pretrained('./test/saved_model/')`).

src/transformers/models/auto/modeling_auto.py

@@ -505,10 +505,9 @@ AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
             pretrained_model_name_or_path:
                 Can be either:
 
-                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
-                      ``bert-base-uncased``.
-                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
-                      ``dbmdz/bert-base-german-cased``.
+                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
+                      a user or organization name, like ``dbmdz/bert-base-german-cased``.
                     - A path to a `directory` containing model weights saved using
                       :func:`~transformers.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                     - A path or url to a `tensorflow index checkpoint file` (e.g, ``./tf_model/model.ckpt.index``). In
@@ -521,8 +520,8 @@ AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
                 Configuration for the model to use instead of an automatically loaded configuration. Configuration can
                 be automatically loaded when:
 
-                    - The model is a model provided by the library (loaded with the `shortcut name` string of a
-                      pretrained model).
+                    - The model is a model provided by the library (loaded with the `model id` string of a pretrained
+                      model).
                     - The model was saved using :meth:`~transformers.PreTrainedModel.save_pretrained` and is reloaded
                       by supplying the save directory.
                     - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
@@ -608,7 +607,7 @@ class AutoModel:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModel
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModel.from_config(config)
         """
@@ -634,7 +633,7 @@ class AutoModel:
 
             >>> from transformers import AutoConfig, AutoModel
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModel.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -702,7 +701,7 @@ class AutoModelForPreTraining:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForPreTraining
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelForPreTraining.from_config(config)
         """
@@ -728,7 +727,7 @@ class AutoModelForPreTraining:
 
             >>> from transformers import AutoConfig, AutoModelForPreTraining
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForPreTraining.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -802,7 +801,7 @@ class AutoModelWithLMHead:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelWithLMHead
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelWithLMHead.from_config(config)
         """
@@ -834,7 +833,7 @@ class AutoModelWithLMHead:
 
             >>> from transformers import AutoConfig, AutoModelWithLMHead
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelWithLMHead.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -908,7 +907,7 @@ class AutoModelForCausalLM:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForCausalLM
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('gpt2')
             >>> model = AutoModelForCausalLM.from_config(config)
         """
@@ -934,7 +933,7 @@ class AutoModelForCausalLM:
 
             >>> from transformers import AutoConfig, AutoModelForCausalLM
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForCausalLM.from_pretrained('gpt2')
 
             >>> # Update configuration during loading
@@ -1002,7 +1001,7 @@ class AutoModelForMaskedLM:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForMaskedLM
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelForMaskedLM.from_config(config)
         """
@@ -1028,7 +1027,7 @@ class AutoModelForMaskedLM:
 
             >>> from transformers import AutoConfig, AutoModelForMaskedLM
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForMaskedLM.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -1096,7 +1095,7 @@ class AutoModelForSeq2SeqLM:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForSeq2SeqLM
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('t5')
             >>> model = AutoModelForSeq2SeqLM.from_config(config)
         """
@@ -1124,7 +1123,7 @@ class AutoModelForSeq2SeqLM:
 
             >>> from transformers import AutoConfig, AutoModelForSeq2SeqLM
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForSeq2SeqLM.from_pretrained('t5-base')
 
             >>> # Update configuration during loading
@@ -1194,7 +1193,7 @@ class AutoModelForSequenceClassification:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForSequenceClassification
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelForSequenceClassification.from_config(config)
         """
@@ -1222,7 +1221,7 @@ class AutoModelForSequenceClassification:
 
             >>> from transformers import AutoConfig, AutoModelForSequenceClassification
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -1291,7 +1290,7 @@ class AutoModelForQuestionAnswering:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForQuestionAnswering
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelForQuestionAnswering.from_config(config)
         """
@@ -1320,7 +1319,7 @@ class AutoModelForQuestionAnswering:
 
             >>> from transformers import AutoConfig, AutoModelForQuestionAnswering
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -1390,7 +1389,7 @@ class AutoModelForTokenClassification:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForTokenClassification
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelForTokenClassification.from_config(config)
         """
@@ -1419,7 +1418,7 @@ class AutoModelForTokenClassification:
 
             >>> from transformers import AutoConfig, AutoModelForTokenClassification
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -1490,7 +1489,7 @@ class AutoModelForMultipleChoice:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForMultipleChoice
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelForMultipleChoice.from_config(config)
         """
@@ -1519,7 +1518,7 @@ class AutoModelForMultipleChoice:
 
             >>> from transformers import AutoConfig, AutoModelForMultipleChoice
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForMultipleChoice.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading
@@ -1590,7 +1589,7 @@ class AutoModelForNextSentencePrediction:
         Examples::
 
             >>> from transformers import AutoConfig, AutoModelForNextSentencePrediction
-            >>> # Download configuration from S3 and cache.
+            >>> # Download configuration from huggingface.co and cache.
             >>> config = AutoConfig.from_pretrained('bert-base-uncased')
             >>> model = AutoModelForNextSentencePrediction.from_config(config)
         """
@@ -1619,7 +1618,7 @@ class AutoModelForNextSentencePrediction:
 
             >>> from transformers import AutoConfig, AutoModelForNextSentencePrediction
 
-            >>> # Download model and configuration from S3 and cache.
+            >>> # Download model and configuration from huggingface.co and cache.
             >>> model = AutoModelForNextSentencePrediction.from_pretrained('bert-base-uncased')
 
             >>> # Update configuration during loading

src/transformers/models/auto/modeling_flax_auto.py

@@ -75,7 +75,7 @@ class FlaxAutoModel(object):
         Examples::
 
             config = BertConfig.from_pretrained('bert-base-uncased')
-            # Download configuration from S3 and cache.
+            # Download configuration from huggingface.co and cache.
             model = FlaxAutoModel.from_config(config)
             # E.g. model was saved using `save_pretrained('./test/saved_model/')`
         """
@@ -109,10 +109,9 @@ class FlaxAutoModel(object):
         Args:
             pretrained_model_name_or_path: either:
 
-                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.:
-                  ``bert-base-uncased``.
-                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.:
-                  ``dbmdz/bert-base-german-cased``.
+                - a string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co. Valid
+                  model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a user or
+                  organization name, like ``dbmdz/bert-base-german-cased``.
                 - a path to a `directory` containing model weights saved using
                   :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this
@@ -165,7 +164,7 @@ class FlaxAutoModel(object):
 
         Examples::
 
-            model = FlaxAutoModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = FlaxAutoModel.from_pretrained('bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
             model = FlaxAutoModel.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
             assert model.config.output_attention == True