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81 lines
4.8 KiB
Plaintext
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Philosophy
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🤗 Transformers is an opinionated library built for:
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- NLP researchers and educators seeking to use/study/extend large-scale transformers models
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- hands-on practitioners who want to fine-tune those models and/or serve them in production
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- engineers who just want to download a pretrained model and use it to solve a given NLP task.
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The library was designed with two strong goals in mind:
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- Be as easy and fast to use as possible:
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- We strongly limited the number of user-facing abstractions to learn, in fact, there are almost no abstractions,
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just three standard classes required to use each model: [configuration](main_classes/configuration),
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[models](main_classes/model) and [tokenizer](main_classes/tokenizer).
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- All of these classes can be initialized in a simple and unified way from pretrained instances by using a common
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`from_pretrained()` instantiation method which will take care of downloading (if needed), caching and
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loading the related class instance and associated data (configurations' hyper-parameters, tokenizers' vocabulary,
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and models' weights) from a pretrained checkpoint provided on [Hugging Face Hub](https://huggingface.co/models) or your own saved checkpoint.
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- On top of those three base classes, the library provides two APIs: [`pipeline`] for quickly
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using a model (plus its associated tokenizer and configuration) on a given task and
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[`Trainer`]/`Keras.fit` to quickly train or fine-tune a given model.
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- As a consequence, this library is NOT a modular toolbox of building blocks for neural nets. If you want to
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extend/build-upon the library, just use regular Python/PyTorch/TensorFlow/Keras modules and inherit from the base
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classes of the library to reuse functionalities like model loading/saving.
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- Provide state-of-the-art models with performances as close as possible to the original models:
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- We provide at least one example for each architecture which reproduces a result provided by the official authors
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of said architecture.
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- The code is usually as close to the original code base as possible which means some PyTorch code may be not as
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*pytorchic* as it could be as a result of being converted TensorFlow code and vice versa.
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A few other goals:
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- Expose the models' internals as consistently as possible:
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- We give access, using a single API, to the full hidden-states and attention weights.
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- Tokenizer and base model's API are standardized to easily switch between models.
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- Incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
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- A simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning.
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- Simple ways to mask and prune transformer heads.
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- Switch easily between PyTorch and TensorFlow 2.0, allowing training using one framework and inference using another.
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## Main concepts
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The library is built around three types of classes for each model:
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- **Model classes** such as [`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
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library.
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- **Configuration classes** such as [`BertConfig`], which store all the parameters required to build
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a model. You don't always need to instantiate these yourself. In particular, if you are using a pretrained model
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without any modification, creating the model will automatically take care of instantiating the configuration (which
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is part of the model).
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- **Tokenizer classes** such as [`BertTokenizer`], which store the vocabulary for each model and
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provide methods for encoding/decoding strings in a list of token embeddings indices to be fed to a model.
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All these classes can be instantiated from pretrained instances and saved locally using two methods:
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- `from_pretrained()` lets you instantiate a model/configuration/tokenizer from a pretrained version either
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provided by the library itself (the supported models can be found on the [Model Hub](https://huggingface.co/models)) or
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stored locally (or on a server) by the user,
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- `save_pretrained()` lets you save a model/configuration/tokenizer locally so that it can be reloaded using
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`from_pretrained()`.
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