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Refactor Code samples; Test code samples (#5036)

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* Refactor code samples

* Test docstrings

* Style

* Tokenization examples

* Run rust of tests

* First step to testing source docs

* Style and BART comment

* Test the remainder of the code samples

* Style

* let to const

* Formatting fixes

* Ready for merge

* Fix fixture + Style

* Fix last tests

* Update docs/source/quicktour.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Addressing @sgugger's comments + Fix MobileBERT in TF

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Lysandre Debut
2020-06-25 16:46:00 -04:00
committed by GitHub
parent 315f464b0a
commit 364a5ae1f0
68 changed files with 1962 additions and 2979 deletions
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31
src/transformers/modeling_tf_ctrl.py
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@@ -22,7 +22,7 @@ import numpy as np
import tensorflow as tf
from .configuration_ctrl import CTRLConfig
from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_utils import (
TFPreTrainedModel,
TFSharedEmbeddings,
@@ -35,6 +35,8 @@ from .tokenization_utils import BatchEncoding
logger = logging.getLogger(__name__)
_TOKENIZER_FOR_DOC = "CtrlTokenizer"
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = [
"ctrl"
# See all CTRL models at https://huggingface.co/models?filter=ctrl
@@ -489,6 +491,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel):
self.transformer = TFCTRLMainLayer(config, name="transformer")
@add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="ctrl")
def call(self, inputs, **kwargs):
r"""
Return:
@@ -510,18 +513,6 @@ class TFCTRLModel(TFCTRLPreTrainedModel):
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
Examples::
import tensorflow as tf
from transformers import CTRLTokenizer, TFCTRLModel
tokenizer = CTRLTokenizer.from_pretrained('ctrl')
model = TFCTRLModel.from_pretrained('ctrl')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
outputs = self.transformer(inputs, **kwargs)
return outputs
@@ -569,6 +560,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
return {"inputs": inputs, "past": past, "use_cache": kwargs["use_cache"]}
@add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING)
@add_code_sample_docstrings(tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="ctrl")
def call(self, inputs, **kwargs):
r"""
Return:
@@ -590,19 +582,6 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
Examples::
import tensorflow as tf
from transformers import CTRLTokenizer, TFCTRLLMHeadModel
tokenizer = CTRLTokenizer.from_pretrained('ctrl')
model = TFCTRLLMHeadModel.from_pretrained('ctrl')
input_ids = tf.constant([tokenizer.encode("Links Hello, my dog is cute", add_special_tokens=True)])
outputs = model(input_ids)
loss, logits = outputs[:2]
"""
transformer_outputs = self.transformer(inputs, **kwargs)
hidden_states = transformer_outputs[0]