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[Dependencies|tokenizers] Make both SentencePiece and Tokenizers optional dependencies (#7659)

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* splitting fast and slow tokenizers [WIP]

* [WIP] splitting sentencepiece and tokenizers dependencies

* update dummy objects

* add name_or_path to models and tokenizers

* prefix added to file names

* prefix

* styling + quality

* spliting all the tokenizer files - sorting sentencepiece based ones

* update tokenizer version up to 0.9.0

* remove hard dependency on sentencepiece 🎉

* and removed hard dependency on tokenizers 🎉

* update conversion script

* update missing models

* fixing tests

* move test_tokenization_fast to main tokenization tests - fix bugs

* bump up tokenizers

* fix bert_generation

* update ad fix several tokenizers

* keep sentencepiece in deps for now

* fix funnel and deberta tests

* fix fsmt

* fix marian tests

* fix layoutlm

* fix squeezebert and gpt2

* fix T5 tokenization

* fix xlnet tests

* style

* fix mbart

* bump up tokenizers to 0.9.2

* fix model tests

* fix tf models

* fix seq2seq examples

* fix tests without sentencepiece

* fix slow => fast  conversion without sentencepiece

* update auto and bert generation tests

* fix mbart tests

* fix auto and common test without tokenizers

* fix tests without tokenizers

* clean up tests lighten up when tokenizers + sentencepiece are both off

* style quality and tests fixing

* add sentencepiece to doc/examples reqs

* leave sentencepiece on for now

* style quality split hebert and fix pegasus

* WIP Herbert fast

* add sample_text_no_unicode and fix hebert tokenization

* skip FSMT example test for now

* fix style

* fix fsmt in example tests

* update following Lysandre and Sylvain's comments

* Update src/transformers/testing_utils.py

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

* Update src/transformers/testing_utils.py

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

* Update src/transformers/tokenization_utils_base.py

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

* Update src/transformers/tokenization_utils_base.py

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

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Thomas Wolf
2020-10-18 20:51:24 +02:00
committed by GitHub
parent c65863ce53
commit ba8c4d0ac0
140 changed files with 6551 additions and 3961 deletions
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12
docs/source/task_summary.rst
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@@ -758,8 +758,8 @@ Here is an example of using the pipelines to do summarization. It leverages a Ba
... If convicted, Barrientos faces up to four years in prison. Her next court appearance is scheduled for May 18.
... """
Because the summarization pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments
of ``PretrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown below.
Because the summarization pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default arguments
of ``PreTrainedModel.generate()`` directly in the pipeline for ``max_length`` and ``min_length`` as shown below.
This outputs the following summary:
.. code-block::
@@ -772,7 +772,7 @@ Here is an example of doing summarization using a model and a tokenizer. The pro
1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
2. Define the article that should be summarized.
3. Add the T5 specific prefix "summarize: ".
4. Use the ``PretrainedModel.generate()`` method to generate the summary.
4. Use the ``PreTrainedModel.generate()`` method to generate the summary.
In this example we use Google`s T5 model. Even though it was pre-trained only on a multi-task mixed dataset (including CNN / Daily Mail), it yields very good results.
@@ -819,15 +819,15 @@ translation results.
>>> print(translator("Hugging Face is a technology company based in New York and Paris", max_length=40))
[{'translation_text': 'Hugging Face ist ein Technologieunternehmen mit Sitz in New York und Paris.'}]
Because the translation pipeline depends on the ``PretrainedModel.generate()`` method, we can override the default arguments
of ``PretrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.
Because the translation pipeline depends on the ``PreTrainedModel.generate()`` method, we can override the default arguments
of ``PreTrainedModel.generate()`` directly in the pipeline as is shown for ``max_length`` above.
Here is an example of doing translation using a model and a tokenizer. The process is the following:
1. Instantiate a tokenizer and a model from the checkpoint name. Summarization is usually done using an encoder-decoder model, such as ``Bart`` or ``T5``.
2. Define the article that should be summarizaed.
3. Add the T5 specific prefix "translate English to German: "
4. Use the ``PretrainedModel.generate()`` method to perform the translation.
4. Use the ``PreTrainedModel.generate()`` method to perform the translation.
.. code-block::