Repository: huggingface/transformers
Pull request: Delete hubconf.py

Remove an old file that's no longer in use

Changed files:
filename: hubconf.py
patch: @@ -1,162 +0,0 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-
-
-SRC_DIR = os.path.join(os.path.dirname(__file__), "src")
-sys.path.append(SRC_DIR)
-
-
-from transformers import (
-    AutoConfig,
-    AutoModel,
-    AutoModelForCausalLM,
-    AutoModelForMaskedLM,
-    AutoModelForQuestionAnswering,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    add_start_docstrings,
-)
-
-
-dependencies = ["torch", "numpy", "tokenizers", "filelock", "requests", "tqdm", "regex", "sentencepiece", "sacremoses", "importlib_metadata", "huggingface_hub"]
-
-
-@add_start_docstrings(AutoConfig.__doc__)
-def config(*args, **kwargs):
-    r"""
-                # Using torch.hub !
-                import torch
-
-                config = torch.hub.load('huggingface/transformers', 'config', 'google-bert/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', 'google-bert/bert-base-uncased', output_attentions=True, foo=False)
-                assert config.output_attentions == True
-                config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'google-bert/bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True)
-                assert config.output_attentions == True
-                assert unused_kwargs == {'foo': False}
-
-            """
-
-    return AutoConfig.from_pretrained(*args, **kwargs)
-
-
-@add_start_docstrings(AutoTokenizer.__doc__)
-def tokenizer(*args, **kwargs):
-    r"""
-        # Using torch.hub !
-        import torch
-
-        tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'google-bert/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/')`
-
-    """
-
-    return AutoTokenizer.from_pretrained(*args, **kwargs)
-
-
-@add_start_docstrings(AutoModel.__doc__)
-def model(*args, **kwargs):
-    r"""
-            # Using torch.hub !
-            import torch
-
-            model = torch.hub.load('huggingface/transformers', 'model', 'google-bert/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', 'google-bert/bert-base-uncased', output_attentions=True)  # Update configuration during loading
-            assert model.config.output_attentions == True
-            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
-            config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
-            model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
-
-        """
-
-    return AutoModel.from_pretrained(*args, **kwargs)
-
-
-@add_start_docstrings(AutoModelForCausalLM.__doc__)
-def modelForCausalLM(*args, **kwargs):
-    r"""
-        # Using torch.hub !
-        import torch
-
-        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', 'openai-community/gpt2')    # Download model and configuration from huggingface.co and cache.
-        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', './test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', 'openai-community/gpt2', output_attentions=True)  # Update configuration during loading
-        assert model.config.output_attentions == True
-        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
-        config = AutoConfig.from_pretrained('./tf_model/gpt_tf_model_config.json')
-        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', './tf_model/gpt_tf_checkpoint.ckpt.index', from_tf=True, config=config)
-
-    """
-    return AutoModelForCausalLM.from_pretrained(*args, **kwargs)
-
-
-@add_start_docstrings(AutoModelForMaskedLM.__doc__)
-def modelForMaskedLM(*args, **kwargs):
-    r"""
-            # Using torch.hub !
-            import torch
-
-            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', 'google-bert/bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
-            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', 'google-bert/bert-base-uncased', output_attentions=True)  # Update configuration during loading
-            assert model.config.output_attentions == True
-            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
-            config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
-            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
-
-        """
-
-    return AutoModelForMaskedLM.from_pretrained(*args, **kwargs)
-
-
-@add_start_docstrings(AutoModelForSequenceClassification.__doc__)
-def modelForSequenceClassification(*args, **kwargs):
-    r"""
-            # Using torch.hub !
-            import torch
-
-            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'google-bert/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', 'google-bert/bert-base-uncased', output_attentions=True)  # Update configuration during loading
-            assert model.config.output_attentions == True
-            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
-            config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
-            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
-
-        """
-
-    return AutoModelForSequenceClassification.from_pretrained(*args, **kwargs)
-
-
-@add_start_docstrings(AutoModelForQuestionAnswering.__doc__)
-def modelForQuestionAnswering(*args, **kwargs):
-    r"""
-        # Using torch.hub !
-        import torch
-
-        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'google-bert/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', 'google-bert/bert-base-uncased', output_attentions=True)  # Update configuration during loading
-        assert model.config.output_attentions == True
-        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
-        config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
-        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
-
-    """
-    return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)