Added pipelines quick tour in README
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README.md
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README.md
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@ -490,6 +490,35 @@ transformers-cli ls
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# List all your S3 objects.
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```
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## Quick tour of pipelines
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New in version `v2.3`: `Pipeline` are high-level objects which automatically handle tokenization, running your data through a transformers model
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and outputting the result in a structured object.
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You can create `Pipeline` objects for the following down-stream tasks:
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- `feature-extraction`: Generates a tensor representation for the input sequence
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- `ner`: Generates named entity mapping for each word in the input sequence.
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- `sentiment-analysis`: Gives the polarity (positive / negative) of the whole input sequence.
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- `question-answering`: Provided some context and a question refering to the context, it will extract the answer to the question
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in the context.
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```python
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from transformers import pipeline
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# Allocate a pipeline for sentiment-analysis
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nlp = pipeline('sentiment-analysis')
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nlp('We are very happy to include pipeline into the transformers repository.')
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>>> {'label': 'POSITIVE', 'score': 0.99893874}
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# Allocate a pipeline for question-answering
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nlp = pipeline('question-answering')
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nlp({
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'question': 'What is the name of the repository ?',
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'context': 'Pipeline have been included in the huggingface/transformers repository'
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})
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>>> {'score': 0.28756016668193496, 'start': 35, 'end': 59, 'answer': 'huggingface/transformers'}
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```
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## Migrating from pytorch-transformers to transformers
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Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
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