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.
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
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.