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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
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
* add for alpha_fold2 * add some extra setting * fix some bugs * fix some changes * fix some bugs 2nd * Add another initition of Gmem_tile_qkv and Gmem_tile_o * add some compensation for try..catch * fix mistake in flash_attn_fwd * commit for code style and bug check * fix some bugs for flash_attn_with_bias-mask * add more print for pointer debug * add some bug test cases. * backward function * fix bugs * make some changes for backward * Fix compiling error. * quote all printf debug * quote all printf debug and fix interface error * quote all printf debug and fix interface error, fix typo * remove all printf * split files * remove useless debug code * split fwd and bwd execution function * split fwd and bwd execution function * remove useless codes * remove useless codes * remove useless codes 3rd times * remove useless codes 4th times * Fix compiling error. * Remove const.
remove cudaMallocAsync
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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