Removing run_pl_glue.py from text classification docs, include run_xnli.py & run_tf_text_classification.py (#10066)

* Removing run_pl_glue.py from seq classification docs

* Adding run_tf_text_classification.py

* Using :prefix_link: to refer local files

* Applying "make style" to the branch

* Update docs/source/task_summary.rst

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

* Removing last underscores

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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Juan Cruz-Benito 2021-02-08 19:04:21 +01:00 committed by GitHub
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@ -54,12 +54,11 @@ Sequence Classification
Sequence classification is the task of classifying sequences according to a given number of classes. An example of
sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune a
model on a GLUE sequence classification task, you may leverage the `run_glue.py
<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`__ and
`run_pl_glue.py
<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_pl_glue.py>`__ or
`run_tf_glue.py
<https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`__ scripts.
model on a GLUE sequence classification task, you may leverage the :prefix_link:`run_glue.py
<examples/text-classification/run_glue.py>`, :prefix_link:`run_tf_glue.py
<examples/text-classification/run_tf_glue.py>`, :prefix_link:`run_tf_text_classification.py
<examples/text-classification/run_tf_text_classification.py>` or :prefix_link:`run_xnli.py
<examples/text-classification/run_xnli.py>` scripts.
Here is an example of using pipelines to do sentiment analysis: identifying if a sequence is positive or negative. It
leverages a fine-tuned model on sst2, which is a GLUE task.