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Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>

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Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

docs/source/philosophy.rst

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+Philosophy
+==========
+
+Transformers is an opinionated library built for:
+
+- NLP researchers and educators seeking to use/study/extend large-scale transformers models
+- hands-on practitioners who want to fine-tune those models and/or serve them in production
+- engineers who just want to download a pretrained model and use it to solve a given NLP task.
+
+The library was designed with two strong goals in mind:
+
+- Be as easy and fast to use as possible:
+
+    - We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
+      just three standard classes required to use each model: :doc:`configuration <main_classes/configuration>`, 
+      :doc:`models <main_classes/model>` and :doc:`tokenizer <main_classes/tokenizer>`.
+    - All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
+      :obj:`from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
+      loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary, 
+      and models' weights) from a pretrained checkpoint provided on 
+      `Hugging Face Hub <https://huggingface.co/models>`__ or your own saved checkpoint.
+    - On top of those three base classes, the library provides two APIs: :func:`~transformers.pipeline` for quickly
+      using a model (plus its associated tokenizer and configuration) on a given task and 
+      :func:`~transformers.Trainer`/:func:`~transformers.TFTrainer` to quickly train or fine-tune a given model.
+    - As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
+      extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
+      classes of the library to reuse functionalities like model loading/saving.
+
+- Provide state-of-the-art models with performances as close as possible to the original models:
+
+    - We provide at least one example for each architecture which reproduces a result provided by the official authors
+      of said architecture.
+    - The code is usually as close to the original code base as possible which means some PyTorch code may be not as
+      *pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.
+
+A few other goals:
+
+- Expose the models' internals as consistently as possible:
+
+    - We give access, using a single API, to the full hidden-states and attention weights.
+    - Tokenizer and base model's API are standardized to easily switch between models.
+
+- Incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
+
+    - A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
+    - Simple ways to mask and prune transformer heads.
+
+- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framwork and inference using another.
+
+Main concepts
+~~~~~~~~~~~~~
+
+The library is build around three types of classes for each model:
+
+- **Model classes**  such as :class:`~transformers.BertModel`, which are 30+ PyTorch models 
+  (`torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__) or Keras models 
+  (`tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__) that work with the pretrained
+  weights provided in the library.
+- **Configuration classes** such as :class:`~transformers.BertConfig`, which store all the parameters required to build
+  a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
+  without any modification, creating the model will automatically take care of instantiating the configuration (which
+  is part of the model).
+- **Tokenizer classes** such as :class:`~transformers.BertTokenizer`, which store the vocabulary for each model and
+  provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model.
+
+All these classes can be instantiated from pretrained instances and saved locally using two methods:
+
+- :obj:`from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either
+  provided by the library itself (the suported models are provided in the list :doc:`here <pretrained_models>`
+  or stored locally (or on a server) by the user,
+- :obj:`save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using
+  :obj:`from_pretrained()`.
+