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An error about trainning the SLIM
Thank you for releasing the code of SLIM!
It is really a cool job.
I really want to train the SLIM model on msmarco dataset again, so I follow the README.
After creating the environment and splitting the training data into train and dev, I run the command provided by the README.
PYTHONPATH=.:$PYTHONPATH python dpr_scale/main.py -m \ --config-name msmarco_aws.yaml \ task=multiterm task/model=mtsplade_model \ task.model.sparse_mode=True \ task.in_batch_eval=True datamodule.num_test_negative=10 trainer.max_epochs=6 \ task.shared_model=True +task.cross_batch=False +task.in_batch=True \ +task.query_topk=20 +task.context_topk=20 \ +task.teacher_coef=0 +task.tau=1 \ +task.query_router_marg_load_loss_coef=0 +task.context_router_marg_load_loss_coef=0 \ +task.query_expert_load_loss_coef=1e-5 +task.context_expert_load_loss_coef=1e-5 \ datamodule.batch_size=8 datamodule.num_negative=7 \ trainer=gpu_1_host trainer.num_nodes=4 trainer.gpus=8
However, I encountered the following error :
Error executing job with overrides: ['task=multiterm', 'task/model=mtsplade_model', 'task.model.sparse_mode=True', 'task.in_batch_eval=True', 'datamodule.num_test_negative=10', 'trainer.max_epochs=6', 'task.shared_model=True', '+task.cross_batch=False', '+task.in_batch=True', '+task.query_topk=20', '+task.context_topk=20', '+task.teacher_coef=0', '+task.tau=1', '+task.query_router_marg_load_loss_coef=0', '+task.context_router_marg_load_loss_coef=0', '+task.query_expert_load_loss_coef=1e-05', '+task.context_expert_load_loss_coef=1e-05', 'datamodule.batch_size=8', 'datamodule.num_negative=7', 'trainer=gpu_1_host', 'trainer.num_nodes=4', 'trainer.gpus=8'] Error locating target 'dpr_scale.task.mtsplade_task.MultiTermRetrieverTask', set env var HYDRA_FULL_ERROR=1 to see chained exception. full_key: task Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Could you please tell me how to solve it ๏ผ
Thanks very much !!!
There is a problem with formula 8 in this paper
Hello, I recently read your article, and there is a question that has been bothering me. Formula 8 in the article calculates the lower bound of similarity. Both sides of the equation are not equal and cannot be converted into a maximum pooling representation of the paragraph.
Can you answer my confusion? Thank you very much.
How to building the index for the generated embeddings?
Thanks for publishing the code of this important work.
I just follow the README to train the SLIM model on msmarco dataset again.
After generating the embeddings, I want to build an index on them but I don't no the details.
I try to find the instructions in pyserini, but the step about building index is omitted.
I really want to know how to build the index. I would appreciate your help.
I look forward to hearing from you.
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