chunked/content_aware_chunking/_philosophy/chunk_1.txt

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:

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,
    models, and a preprocessing class (tokenizer for NLP, image processor for vision, feature extractor for audio, and processor 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 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 (all TensorFlow models are compatible with Keras.fit).

As a consequence, this library is NOT a modular toolbox of building blocks for neural nets.