Move source code inside a src subdirectory.
This prevents transformers from being importable simply because the CWD
is the root of the git repository, while not being importable from other
directories. That led to inconsistent behavior, especially in examples.
Once you fetch this commit, in your dev environment, you must run:
$ pip uninstall transformers
$ pip install -e .
This commit is contained in:
Aymeric Augustin
242
src/transformers/modelcard.py
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242
src/transformers/modelcard.py
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# coding=utf-8
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# Copyright 2018 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Configuration base class and utilities."""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import copy
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import json
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import logging
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import os
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from io import open
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from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP
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from .file_utils import (
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CONFIG_NAME,
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MODEL_CARD_NAME,
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TF2_WEIGHTS_NAME,
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WEIGHTS_NAME,
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cached_path,
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hf_bucket_url,
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is_remote_url,
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)
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logger = logging.getLogger(__name__)
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class ModelCard(object):
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r""" Model Card class.
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Store model card as well as methods for loading/downloading/saving model cards.
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Please read the following paper for details and explanation on the sections:
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"Model Cards for Model Reporting"
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by Margaret Mitchell, Simone Wu,
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Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer,
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Inioluwa Deborah Raji and Timnit Gebru for the proposal behind model cards.
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Link: https://arxiv.org/abs/1810.03993
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Note:
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A model card can be loaded and saved to disk.
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Parameters:
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"""
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def __init__(self, **kwargs):
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# Recomended attributes from https://arxiv.org/abs/1810.03993 (see papers)
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self.model_details = kwargs.pop("model_details", {})
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self.intended_use = kwargs.pop("intended_use", {})
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self.factors = kwargs.pop("factors", {})
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self.metrics = kwargs.pop("metrics", {})
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self.evaluation_data = kwargs.pop("evaluation_data", {})
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self.training_data = kwargs.pop("training_data", {})
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self.quantitative_analyses = kwargs.pop("quantitative_analyses", {})
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self.ethical_considerations = kwargs.pop("ethical_considerations", {})
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self.caveats_and_recommendations = kwargs.pop("caveats_and_recommendations", {})
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# Open additional attributes
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for key, value in kwargs.items():
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try:
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setattr(self, key, value)
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except AttributeError as err:
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logger.error("Can't set {} with value {} for {}".format(key, value, self))
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raise err
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def save_pretrained(self, save_directory_or_file):
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""" Save a model card object to the directory or file `save_directory_or_file`.
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"""
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if os.path.isdir(save_directory_or_file):
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# If we save using the predefined names, we can load using `from_pretrained`
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output_model_card_file = os.path.join(save_directory_or_file, MODEL_CARD_NAME)
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else:
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output_model_card_file = save_directory_or_file
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self.to_json_file(output_model_card_file)
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logger.info("Model card saved in {}".format(output_model_card_file))
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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r""" Instantiate a :class:`~transformers.ModelCard` from a pre-trained model model card.
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Parameters:
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pretrained_model_name_or_path: either:
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- a string with the `shortcut name` of a pre-trained model card to load from cache or download, e.g.: ``bert-base-uncased``.
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- 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``.
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- a path to a `directory` containing a mode card file saved using the :func:`~transformers.ModelCard.save_pretrained` method, e.g.: ``./my_model_directory/``.
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- a path or url to a saved model card JSON `file`, e.g.: ``./my_model_directory/modelcard.json``.
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cache_dir: (`optional`) string:
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Path to a directory in which a downloaded pre-trained model
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card should be cached if the standard cache should not be used.
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kwargs: (`optional`) dict: key/value pairs with which to update the ModelCard object after loading.
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- The values in kwargs of any keys which are model card attributes will be used to override the loaded values.
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- Behavior concerning key/value pairs whose keys are *not* model card attributes is controlled by the `return_unused_kwargs` keyword parameter.
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proxies: (`optional`) dict, default None:
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A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
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The proxies are used on each request.
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find_from_standard_name: (`optional`) boolean, default True:
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If the pretrained_model_name_or_path ends with our standard model or config filenames, replace them with our standard modelcard filename.
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Can be used to directly feed a model/config url and access the colocated modelcard.
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return_unused_kwargs: (`optional`) bool:
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- If False, then this function returns just the final model card object.
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- If True, then this functions returns a tuple `(model card, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not model card attributes: ie the part of kwargs which has not been used to update `ModelCard` and is otherwise ignored.
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Examples::
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modelcard = ModelCard.from_pretrained('bert-base-uncased') # Download model card from S3 and cache.
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modelcard = ModelCard.from_pretrained('./test/saved_model/') # E.g. model card was saved using `save_pretrained('./test/saved_model/')`
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modelcard = ModelCard.from_pretrained('./test/saved_model/modelcard.json')
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modelcard = ModelCard.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
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"""
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cache_dir = kwargs.pop("cache_dir", None)
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proxies = kwargs.pop("proxies", None)
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find_from_standard_name = kwargs.pop("find_from_standard_name", True)
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return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
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if pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
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# For simplicity we use the same pretrained url than the configuration files
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# but with a different suffix (modelcard.json). This suffix is replaced below.
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model_card_file = ALL_PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
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elif os.path.isdir(pretrained_model_name_or_path):
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model_card_file = os.path.join(pretrained_model_name_or_path, MODEL_CARD_NAME)
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elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
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model_card_file = pretrained_model_name_or_path
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else:
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model_card_file = hf_bucket_url(pretrained_model_name_or_path, postfix=MODEL_CARD_NAME)
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if find_from_standard_name or pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
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model_card_file = model_card_file.replace(CONFIG_NAME, MODEL_CARD_NAME)
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model_card_file = model_card_file.replace(WEIGHTS_NAME, MODEL_CARD_NAME)
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model_card_file = model_card_file.replace(TF2_WEIGHTS_NAME, MODEL_CARD_NAME)
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try:
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# Load from URL or cache if already cached
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resolved_model_card_file = cached_path(
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model_card_file, cache_dir=cache_dir, force_download=True, proxies=proxies, resume_download=False
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)
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if resolved_model_card_file == model_card_file:
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logger.info("loading model card file {}".format(model_card_file))
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else:
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logger.info(
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"loading model card file {} from cache at {}".format(model_card_file, resolved_model_card_file)
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)
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# Load model card
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modelcard = cls.from_json_file(resolved_model_card_file)
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except EnvironmentError:
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if pretrained_model_name_or_path in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
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logger.warning("Couldn't reach server at '{}' to download model card file.".format(model_card_file))
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else:
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logger.warning(
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"Model name '{}' was not found in model name list ({}). "
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"We assumed '{}' was a path or url to a model card file named {} or "
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"a directory containing such a file but couldn't find any such file at this path or url.".format(
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pretrained_model_name_or_path,
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", ".join(ALL_PRETRAINED_CONFIG_ARCHIVE_MAP.keys()),
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model_card_file,
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MODEL_CARD_NAME,
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)
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)
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logger.warning("Creating an empty model card.")
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# We fall back on creating an empty model card
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modelcard = cls()
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except json.JSONDecodeError:
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logger.warning(
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"Couldn't reach server at '{}' to download model card file or "
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"model card file is not a valid JSON file. "
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"Please check network or file content here: {}.".format(model_card_file, resolved_model_card_file)
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)
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logger.warning("Creating an empty model card.")
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# We fall back on creating an empty model card
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modelcard = cls()
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# Update model card with kwargs if needed
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to_remove = []
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for key, value in kwargs.items():
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if hasattr(modelcard, key):
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setattr(modelcard, key, value)
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to_remove.append(key)
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for key in to_remove:
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kwargs.pop(key, None)
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logger.info("Model card: %s", str(modelcard))
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if return_unused_kwargs:
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return modelcard, kwargs
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else:
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return modelcard
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@classmethod
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def from_dict(cls, json_object):
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"""Constructs a `ModelCard` from a Python dictionary of parameters."""
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return cls(**json_object)
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@classmethod
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def from_json_file(cls, json_file):
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"""Constructs a `ModelCard` from a json file of parameters."""
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with open(json_file, "r", encoding="utf-8") as reader:
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text = reader.read()
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dict_obj = json.loads(text)
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return cls(**dict_obj)
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def __eq__(self, other):
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return self.__dict__ == other.__dict__
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def __repr__(self):
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return str(self.to_json_string())
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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def to_json_file(self, json_file_path):
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""" Save this instance to a json file."""
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with open(json_file_path, "w", encoding="utf-8") as writer:
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writer.write(self.to_json_string())
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