Move tokenization logic to central JiantModelTransformers method #1290
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jeswan
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Codecov Report
@@ Coverage Diff @@
## js/feature/easy_add_model #1290 +/- ##
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+ Coverage 49.75% 49.82% +0.06%
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Files 162 163 +1
Lines 11112 11166 +54
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+ Hits 5529 5563 +34
- Misses 5583 5603 +20
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jeswan
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Mar 11, 2021
zphang
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- I noticed that ELECTRA doesn't have a normalize tokenizations implementation. Can we add an abstractmethod to
JiantTransformersModeland araise NotImplementedfor ELECTRA? (It should be possible to add the implementation for ELECTRA, but I'm okay with leaving it as a future to-do) - I was wondering if it might be a better idea to pull the normalize tokenization logic out into individual functions (in some cases, similar models have the same logic), but I can also see how that might be excessive delegation. I don't feel strongly either way, what do you think?
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…tion normalization
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Comments implemented! For point 2 above, discussed offline to refactor if there is additional duplicate code in the future. |
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* Update to Transformers v4.3.3 (#1266) * use default return_dict in taskmodels and remove hidden state context manager in models. * return hidden states in output of model wrapper * Switch to task model/head factories instead of embedded if-else statements (#1268) * Use jiant transformers model wrapper instead of if-else. Use taskmodel and head factory instead of if-else. * switch to ModelArchitectures enum instead of strings * Refactor get_output_from_encoder() to be member of JiantTaskModel (#1283) * refactor getting output from encoder to be member function of jiant model * switch to explicit encode() in jiant transformers model * fix simple runscript test * update to tokenizer 0.10.1 * Add tests for flat_strip() (#1289) * add flat_strip test * add list to test cases flat_strip * mlm_weights(), feat_spec(), flat_strip() if-else refactors (#1288) * moves remaining if-else statments to jiant model or replaces with model agnostic method * switch from jiant_transformers_model to encoder * fix bug in flat_strip() * Move tokenization logic to central JiantModelTransformers method (#1290) * move model specific tokenization logic to JiantTransformerModels * implement abstract methods for JiantTransformerModels * fix tasks circular import (#1296) * Add DeBERTa (#1295) * Add DeBERTa with sanity test * fix tasks circular import * [WIP] add deberta tests * Revert "fix tasks circular import" This reverts commit f924640. * deberta tests passing with transformers 6472d8 * switch to deberta-v2 * fix get_mlm_weights_dict() for deberta-v2 * update to transformers 4.5.0 * mark deberta test_export as slow * Update test_tokenization_normalization.py * add guide to add a model * fix test_expor_model tests * minor pytest fixes (add num_labels for rte, overnight flag fix) * bugfix for simple api notebook * bugfix for #1310 * bugfix for #1306: simple api notebook path name * squad running * 2nd bugfix for #1310: not all tasks have num_labels property * simple api notebook back to roberta-base * run test matrix for more steps to compare to master * save last/best model test fix Co-authored-by: Jesse Swanson <js11133Wnyu.edu>
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This PR refactors
normalize_tokenizations()by moving the functions toJiantTransformerModels. Although the location of this logic is not ideal, it was the easiest way to have a central location for editing new models/tokenizers. This PR also addsresolve_model_arch_tokenizer(tokenizer)to map the tokenizers to model_archs.