iwangjian / color4dial Goto Github PK
View Code? Open in Web Editor NEWCode and data for "Dialogue Planning via Brownian Bridge Stochastic Process for Goal-directed Proactive Dialogue" (ACL Findings 2023).
License: MIT License
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jrangelgcolor4dial's Issues
Generating Dialogue Issues
Thanks for sharing your codes!
But when I run bash scripts/durecdial_dialog_train.sh. The terminal told me that
It seems that you add another variable that can not be accepted by the handler.
NaN losses during durecdial_planning_train_planner
Hello,
I am going through the pipeline, and have trained the Brownian Bridge. I am however encountering NaN values at the next step of training the planner, as can be seen below.
2023-10-16 10:03:53,449 [INFO] Total parameters: 319843609 Trainable parameters: 178636808
2023-10-16 10:03:53,449 [INFO] Total batches per epoch : 2151
2023-10-16 10:03:53,449 [INFO]
Epoch 1:
2023-10-16 10:04:42,779 [INFO] Train Step: 100 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:05:30,786 [INFO] Train Step: 200 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:06:18,475 [INFO] Train Step: 300 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:07:06,691 [INFO] Train Step: 400 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:07:56,195 [INFO] Train Step: 500 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:08:44,817 [INFO] Train Step: 600 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:09:33,643 [INFO] Train Step: 700 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:10:21,695 [INFO] Train Step: 800 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:11:10,581 [INFO] Train Step: 900 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:11:58,976 [INFO] Train Step: 1000 total_loss: nan lm_loss: nan trans_loss: nan kl_loss: nan
2023-10-16 10:11:58,976 [INFO] Evaluating...
As you can see, this is happening from the very start, and so I'm not sure where this is coming from. As this step uses the model made in the bridge creation step, I thought that might be the issue, but training there seemed fine. Below is the end of the training for the bridge:
Epoch 10:
2023-10-16 09:37:10,892 [INFO] Batch Step: 100 Avg loss: 11.296
2023-10-16 09:38:02,177 [INFO] Batch Step: 200 Avg loss: 10.941
2023-10-16 09:38:53,537 [INFO] Batch Step: 300 Avg loss: 11.168
2023-10-16 09:39:44,677 [INFO] Batch Step: 400 Avg loss: 10.457
2023-10-16 09:40:36,015 [INFO] Batch Step: 500 Avg loss: 9.152
2023-10-16 09:42:24,550 [INFO] Evaluation Average Similarity: 0.998
2023-10-16 09:42:24,551 [INFO] Epoch 10 training done.
2023-10-16 09:42:26,615 [INFO] Saved to [logs/DuRecDial2/checkpoints_bridge/bridge_model_epoch_10.bin]
2023-10-16 09:42:26,617 [INFO] Loading raw data from data/DuRecDial2/sample_test_seen.jsonl
2023-10-16 09:42:28,640 [INFO] Creating cache instances durecdial_plan_test_seen.pkl
/project/6000784/habashyk/Color4Dial/lib/python3.8/site-packages/transformers/models/bart/modeling_bart.py:549: FutureWarning: The class `PretrainedBartModel` has been depreciated, please use `BartPreTrainedModel` instead.
warnings.warn(
100%|██████████| 6152/6152 [00:32<00:00, 191.96it/s]2023-10-16 09:43:01,608 [INFO] Total of 6152 instances were cached.
2023-10-16 09:43:01,645 [INFO] Loading raw data from data/DuRecDial2/sample_test_unseen.jsonl
2023-10-16 09:43:02,734 [INFO] Creating cache instances durecdial_plan_test_unseen.pkl
100%|██████████| 3983/3983 [00:21<00:00, 185.63it/s]2023-10-16 09:43:24,659 [INFO] Total of 3983 instances were cached.
2023-10-16 09:43:24,681 [INFO] Evaluate on test-seen ...
2023-10-16 09:45:15,136 [INFO] Saved to logs/DuRecDial2/brownian_bridge_sim/test_seen_2023-10-16-09-42-26.txt
2023-10-16 09:45:15,139 [INFO] Average similarity on test-seen: 0.9974333125222197
2023-10-16 09:45:15,139 [INFO] Evaluate on test-unseen ...
2023-10-16 09:46:39,721 [INFO] Saved to logs/DuRecDial2/brownian_bridge_sim/test_unseen_2023-10-16-09-42-26.txt
2023-10-16 09:46:39,724 [INFO] Average similarity on test-unseen: 0.9966462435209125
Testing on user data
Would it be possible to add some instructions to the readme about how to try out the model on general utterances? I want to see the model in action myself.
Thank you!
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