[WIP] Tapas v4 (tres) (#9117)

* First commit: adding all files from tapas_v3 * Fix multiple bugs including soft dependency and new structure of the library * Improve testing by adding torch_device to inputs and adding dependency on scatter * Use Python 3 inheritance rather than Python 2 * First draft model cards of base sized models * Remove model cards as they are already on the hub * Fix multiple bugs with integration tests * All model integration tests pass * Remove print statement * Add test for convert_logits_to_predictions method of TapasTokenizer * Incorporate suggestions by Google authors * Fix remaining tests * Change position embeddings sizes to 512 instead of 1024 * Comment out positional embedding sizes * Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES * Added more model names * Fix truncation when no max length is specified * Disable torchscript test * Make style & make quality * Quality * Address CI needs * Test the Masked LM model * Fix the masked LM model * Truncate when overflowing * More much needed docs improvements * Fix some URLs * Some more docs improvements * Test PyTorch scatter * Set to slow + minify * Calm flake8 down * First commit: adding all files from tapas_v3 * Fix multiple bugs including soft dependency and new structure of the library * Improve testing by adding torch_device to inputs and adding dependency on scatter * Use Python 3 inheritance rather than Python 2 * First draft model cards of base sized models * Remove model cards as they are already on the hub * Fix multiple bugs with integration tests * All model integration tests pass * Remove print statement * Add test for convert_logits_to_predictions method of TapasTokenizer * Incorporate suggestions by Google authors * Fix remaining tests * Change position embeddings sizes to 512 instead of 1024 * Comment out positional embedding sizes * Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES * Added more model names * Fix truncation when no max length is specified * Disable torchscript test * Make style & make quality * Quality * Address CI needs * Test the Masked LM model * Fix the masked LM model * Truncate when overflowing * More much needed docs improvements * Fix some URLs * Some more docs improvements * Add add_pooling_layer argument to TapasModel Fix comments by @sgugger and @patrickvonplaten * Fix issue in docs + fix style and quality * Clean up conversion script and add task parameter to TapasConfig * Revert the task parameter of TapasConfig Some minor fixes * Improve conversion script and add test for absolute position embeddings * Improve conversion script and add test for absolute position embeddings * Fix bug with reset_position_index_per_cell arg of the conversion cli * Add notebooks to the examples directory and fix style and quality * Apply suggestions from code review * Move from `nielsr/` to `google/` namespace * Apply Sylvain's comments Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Rogge Niels <niels.rogge@howest.be> Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: sgugger <sylvain.gugger@gmail.com>

[WIP] Tapas v4 (tres) (#9117)
* First commit: adding all files from tapas_v3 * Fix multiple bugs including soft dependency and new structure of the library * Improve testing by adding torch_device to inputs and adding dependency on scatter * Use Python 3 inheritance rather than Python 2 * First draft model cards of base sized models * Remove model cards as they are already on the hub * Fix multiple bugs with integration tests * All model integration tests pass * Remove print statement * Add test for convert_logits_to_predictions method of TapasTokenizer * Incorporate suggestions by Google authors * Fix remaining tests * Change position embeddings sizes to 512 instead of 1024 * Comment out positional embedding sizes * Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES * Added more model names * Fix truncation when no max length is specified * Disable torchscript test * Make style & make quality * Quality * Address CI needs * Test the Masked LM model * Fix the masked LM model * Truncate when overflowing * More much needed docs improvements * Fix some URLs * Some more docs improvements * Test PyTorch scatter * Set to slow + minify * Calm flake8 down * First commit: adding all files from tapas_v3 * Fix multiple bugs including soft dependency and new structure of the library * Improve testing by adding torch_device to inputs and adding dependency on scatter * Use Python 3 inheritance rather than Python 2 * First draft model cards of base sized models * Remove model cards as they are already on the hub * Fix multiple bugs with integration tests * All model integration tests pass * Remove print statement * Add test for convert_logits_to_predictions method of TapasTokenizer * Incorporate suggestions by Google authors * Fix remaining tests * Change position embeddings sizes to 512 instead of 1024 * Comment out positional embedding sizes * Update PRETRAINED_VOCAB_FILES_MAP and PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES * Added more model names * Fix truncation when no max length is specified * Disable torchscript test * Make style & make quality * Quality * Address CI needs * Test the Masked LM model * Fix the masked LM model * Truncate when overflowing * More much needed docs improvements * Fix some URLs * Some more docs improvements * Add add_pooling_layer argument to TapasModel Fix comments by @sgugger and @patrickvonplaten * Fix issue in docs + fix style and quality * Clean up conversion script and add task parameter to TapasConfig * Revert the task parameter of TapasConfig Some minor fixes * Improve conversion script and add test for absolute position embeddings * Improve conversion script and add test for absolute position embeddings * Fix bug with reset_position_index_per_cell arg of the conversion cli * Add notebooks to the examples directory and fix style and quality * Apply suggestions from code review * Move from `nielsr/` to `google/` namespace * Apply Sylvain's comments Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Rogge Niels <niels.rogge@howest.be> Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: sgugger <sylvain.gugger@gmail.com>
1551e2dc · NielsRogge · GitHub · ad895af9 · 1551e2dc · 1551e2dc
Unverified Commit 1551e2dc authored Dec 15, 2020 by NielsRogge Committed by GitHub Dec 15, 2020
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tests/test_tokenization_common.py tests/test_tokenization_common.py +2 -2

tests/test_tokenization_tapas.py tests/test_tokenization_tapas.py +1190 -0

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--- a/tests/test_tokenization_common.py
+++ b/tests/test_tokenization_common.py
@@ -584,7 +584,7 @@ class TokenizerTesterMixin:
                # We want to have sequence 0 and sequence 1 are tagged
                # respectively with 0 and 1 token_ids
-                # (regardeless of weither the model use token type ids)
+                # (regardless of whether the model use token type ids)
                # We use this assumption in the QA pipeline among other place
                output = tokenizer(seq_0, return_token_type_ids=True)
                self.assertIn(0, output["token_type_ids"])
@@ -600,7 +600,7 @@ class TokenizerTesterMixin:
                # We want to have sequence 0 and sequence 1 are tagged
                # respectively with 0 and 1 token_ids
-                # (regardeless of weither the model use token type ids)
+                # (regardless of whether the model use token type ids)
                # We use this assumption in the QA pipeline among other place
                output = tokenizer(seq_0)
                self.assertIn(0, output.sequence_ids())

--- a/tests/test_tokenization_tapas.py
+++ b/tests/test_tokenization_tapas.py