following:
Not all models were fine-tuned on all tasks. If you want to fine-tune a model on a specific task, you can leverage
  one of the run_$TASK.py scripts in the examples directory.
Fine-tuned models were fine-tuned on a specific dataset. This dataset may or may not overlap with your use-case and
  domain. As mentioned previously, you may leverage the examples scripts to fine-tune your model, or you may
  create your own training script.
In order to do an inference on a task, several mechanisms are made available by the library:
Pipelines: very easy-to-use abstractions, which require as little as two lines of code.
Direct model use: Less abstractions, but more flexibility and power via a direct access to a tokenizer
  (PyTorch/TensorFlow) and full inference capacity.
Both approaches are showcased here.
