SUMIN/HuggingFace_transformer
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typo in readme/doc

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thomwolf
2019-09-26 16:23:28 +02:00
parent f83b35b77d
commit 6c3b131516
2 changed files with 4 additions and 4 deletions
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6
README.md
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@@ -207,7 +207,7 @@ Let's do a quick example of how a TensorFlow 2.0 model can be trained in 12 line
```python ```python
import tensorflow as tf import tensorflow as tf
import tensorflow_datasets import tensorflow_datasets
from pytorch_transformers import * from transformers import *
# Load dataset, tokenizer, model from pretrained model/vocabulary # Load dataset, tokenizer, model from pretrained model/vocabulary
tokenizer = BertTokenizer.from_pretrained('bert-base-cased') tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
@@ -215,8 +215,8 @@ model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
data = tensorflow_datasets.load('glue/mrpc') data = tensorflow_datasets.load('glue/mrpc')
# Prepare dataset for GLUE as a tf.data.Dataset instance # Prepare dataset for GLUE as a tf.data.Dataset instance
train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, 128, 'mrpc') train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, max_length=128, task='mrpc')
valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, 128, 'mrpc') valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, max_length=128, task='mrpc')
train_dataset = train_dataset.shuffle(100).batch(32).repeat(2) train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
valid_dataset = valid_dataset.batch(64) valid_dataset = valid_dataset.batch(64)

2
docs/source/model_doc/xlm.rst
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@@ -44,7 +44,7 @@ XLM
``TFXLMModel`` ``TFXLMModel``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: pytorch_transformers.TFXLMModel .. autoclass:: transformers.TFXLMModel
:members: :members: