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Semantic role labeling demo #4
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Thanks for your attention, the network architecture in demo is consistent with that paper, while the parameters setup and features are not the optimal. Currently, it's provided to users for study. The experiment code of paper was implemented on the past interface of PaddlePaddle, which is different from this demo. We will update the setup later, after verified experiment on new code. |
Thank you so much for your timely reply! I'm wondering if you happen to have test results from training the current SRL code on the CoNLL2005 training set? And if so, are they close to the numbers reported in the ACL 2015 paper? |
@luheng We update SRL demo recently. Now, it is same as 'End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networks' |
Feature/dynamic net doc
Update distributed lookup table design doc
Fuse broadcast
merge colourful-tree's change
…rd run (#41306) * [Refactor] refactored eager_gen.py PR #2 * [DoubleGrad PR #1] Decoupled code generation logics for Dygraph ForwardFunctions and GradNodes * Fixed minor issue * Adjusted logics of GenerateNodeCreationCodes and GenerateForwardDefinition * Fixed issues * Supported higher-order grad node generation * [DoubleGrad PR #4] Supported higher-order GradNode generation * [DoubleGrad #4] Bug Fixes to Double Grad Node Generation * Fixed yaml typo * Fixed yaml typo * fixed minor issues * [DoubleGrad PR #5] Enabled gradient computations for grad_tensors passed to paddle.grad() * Fixed minor issue * Fixed CI-Inference issue * Fixed CI-inference issues * [DoubleGrad PR #7] paddle.grad() to copy backward graph before backward run * Fixed minor issues * Fixed issue with backward graph construction logic * Fixed implementation issues with backward graph reconstruction * Fixed unittest issue * Fixed issues
* [Refactor] refactored eager_gen.py PR #2 * [DoubleGrad PR #1] Decoupled code generation logics for Dygraph ForwardFunctions and GradNodes * Fixed minor issue * Adjusted logics of GenerateNodeCreationCodes and GenerateForwardDefinition * Fixed issues * Supported higher-order grad node generation * [DoubleGrad PR #4] Supported higher-order GradNode generation * [DoubleGrad #4] Bug Fixes to Double Grad Node Generation * Fixed yaml typo * Fixed yaml typo * fixed minor issues * [DoubleGrad PR #5] Enabled gradient computations for grad_tensors passed to paddle.grad() * Fixed minor issue * Fixed CI-Inference issue * Fixed CI-inference issues * [DoubleGrad PR #7] paddle.grad() to copy backward graph before backward run * Fixed minor issues * Fixed issue with backward graph construction logic * Fixed implementation issues with backward graph reconstruction * Fixed unittest issue * Fixed issues * [DoubleGrad PR #8] Enabled triple grads for sigmoid and matmul * Fixed issues with phi kernel * Added triple grad test case * Fixed minor issue
fix the document format of enable_prim/disable_prim/prim2orig/prim_enabled
Add the doc link to main page.
Fix bugs and Optimize fuse_seqpool_cvm_op
* add support for mixed precision training
…blomm add bf16 o2
update test_cache_program
fix grid dim.y should less than 65535 bug
* Add mkdocs.yml and rename some docs * Update hooks
[MTAI] build(system): enable build system in paddle for MUSA
add log space clarification
fix cublaslt search
Is the setup in demo/semantic_role_labeling/train.sh a full replication of the ACL 2015 paper End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networks? Thanks!
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