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New Feature : Adding Pipeline and refine Introduction of doc. #43
Description
Description
The example in Introduction docs is too long and complex.
Many settings can be wrappered into a default configuration, such as epoch, batchsize, optimizer, etc.
users can custom their own configuration through some string options instead of declaring a specific class.
The all process can also be wrappered into a default class (called pipeline in huggingface)
This is an example
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]By this way, we can refine README docs, make it clear and easy-understanding.
As for some users who want to further custom their model, we can provide more complex example scripts under test directory.
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