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transformers/docs/source/en/philosophy.md

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+
+# Philosophy
+
+🤗 Transformers is an opinionated library built for:
+
+- machine learning researchers and educators seeking to use, study or extend large-scale Transformers models.
+- hands-on practitioners who want to fine-tune those models or serve them in production, or both.
+- engineers who just want to download a pretrained model and use it to solve a given machine learning task.
+
+The library was designed with two strong goals in mind:
+
+1. 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: [configuration](main_classes/configuration),
+    [models](main_classes/model), and a preprocessing class ([tokenizer](main_classes/tokenizer) for NLP, [image processor](main_classes/image_processor) for vision, [feature extractor](main_classes/feature_extractor) for audio, and [processor](main_classes/processors) for multimodal inputs).
+  - All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
+    `from_pretrained()` method which downloads (if needed), caches and
+    loads the related class instance and associated data (configurations' hyperparameters, 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: [`pipeline`] for quickly
+    using a model for inference on a given task and [`Trainer`] to quickly train or fine-tune a PyTorch model.
+  - As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
+    extend or build upon the library, just use regular Python or PyTorch and inherit from the base
+    classes of the library to reuse functionalities like model loading and saving. If you'd like to learn more about our coding philosophy for models, check out our [Repeat Yourself](https://huggingface.co/blog/transformers-design-philosophy) blog post.
+
+2. 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 from other Deep Learning frameworks.
+
+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.
+  - The preprocessing classes and base model APIs are standardized to easily switch between models.
+
+- Incorporate a subjective selection of promising tools for fine-tuning and investigating these models:
+
+  - A simple and consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
+  - Simple ways to mask and prune Transformer heads.
+
+## Main concepts
+
+The library is built around three types of classes for each model:
+
+- **Model classes** are be PyTorch models ([torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)).
+- **Configuration classes** store the hyperparameters required to build a model (such as the number of layers and hidden size). 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).
+- **Preprocessing classes** convert the raw data into a format accepted by the model. A [tokenizer](main_classes/tokenizer) stores the vocabulary for each model and provide methods for encoding and decoding strings in a list of token embedding indices to be fed to a model. [Image processors](main_classes/image_processor) preprocess vision inputs, [feature extractors](main_classes/feature_extractor) preprocess audio inputs, and a [processor](main_classes/processors) handles multimodal inputs.
+
+All these classes can be instantiated from pretrained instances, saved locally, and shared on the Hub with three methods:
+
+- `from_pretrained()` lets you instantiate a model, configuration, and preprocessing class from a pretrained version either
+  provided by the library itself (the supported models can be found on the [Model Hub](https://huggingface.co/models)) or
+  stored locally (or on a server) by the user.
+- `save_pretrained()` lets you save a model, configuration, and preprocessing class locally so that it can be reloaded using
+  `from_pretrained()`.
+- `push_to_hub()` lets you share a model, configuration, and a preprocessing class to the Hub, so it is easily accessible to everyone.