SUMIN/HuggingFace_transformer
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Remove trailing whitespace in README.

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Aymeric Augustin
2019-12-22 13:29:58 +01:00
parent c11b3e2926
commit 698f9e3d7a
1 changed files with 6 additions and 6 deletions
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README.md
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@@ -251,7 +251,7 @@ valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer,
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)
# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule # Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0) optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy') metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
@@ -281,7 +281,7 @@ print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sen
## Quick tour of the fine-tuning/usage scripts ## Quick tour of the fine-tuning/usage scripts
**Important** **Important**
Before running the fine-tuning scripts, please read the Before running the fine-tuning scripts, please read the
[instructions](#run-the-examples) on how to [instructions](#run-the-examples) on how to
setup your environment to run the examples. setup your environment to run the examples.
@@ -442,7 +442,7 @@ python ./examples/run_generation.py \
--model_name_or_path=gpt2 \ --model_name_or_path=gpt2 \
``` ```
and from the Salesforce CTRL model: and from the Salesforce CTRL model:
```shell ```shell
python ./examples/run_generation.py \ python ./examples/run_generation.py \
--model_type=ctrl \ --model_type=ctrl \
@@ -495,13 +495,13 @@ transformers-cli ls
## Quick tour of pipelines ## Quick tour of pipelines
New in version `v2.3`: `Pipeline` are high-level objects which automatically handle tokenization, running your data through a transformers model New in version `v2.3`: `Pipeline` are high-level objects which automatically handle tokenization, running your data through a transformers model
and outputting the result in a structured object. and outputting the result in a structured object.
You can create `Pipeline` objects for the following down-stream tasks: You can create `Pipeline` objects for the following down-stream tasks:
- `feature-extraction`: Generates a tensor representation for the input sequence - `feature-extraction`: Generates a tensor representation for the input sequence
- `ner`: Generates named entity mapping for each word in the input sequence. - `ner`: Generates named entity mapping for each word in the input sequence.
- `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence. - `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence.
- `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question - `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question
in the context. in the context.
@@ -516,7 +516,7 @@ nlp('We are very happy to include pipeline into the transformers repository.')
# Allocate a pipeline for question-answering # Allocate a pipeline for question-answering
nlp = pipeline('question-answering') nlp = pipeline('question-answering')
nlp({ nlp({
'question': 'What is the name of the repository ?', 'question': 'What is the name of the repository ?',
'context': 'Pipeline have been included in the huggingface/transformers repository' 'context': 'Pipeline have been included in the huggingface/transformers repository'
}) })
>>> {'score': 0.28756016668193496, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'} >>> {'score': 0.28756016668193496, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'}