Hi, I'm trying to train a model on my data. The dataset is pretty small, less than 6000 sentences. I used the first tutorial for preprocessing, everything worked just fine. Now when I try model training I get an error:

[2022-11-19 10:42:07,824 WARNING] Corpus corpus_1's weight should be given. We default it to 1 for you.
[2022-11-19 10:42:07,825 INFO] Parsed 2 corpora from -data.
[2022-11-19 10:42:07,826 INFO] Get special vocabs from Transforms: {'src': set(), 'tgt': set()}.
[2022-11-19 10:42:07,887 INFO] Building model...
Traceback (most recent call last):
  File "/usr/local/bin/onmt_train", line 8, in <module>
    sys.exit(main())
  File "/usr/local/lib/python3.7/dist-packages/onmt/bin/train.py", line 65, in main
    train(opt)
  File "/usr/local/lib/python3.7/dist-packages/onmt/bin/train.py", line 50, in train
    train_process(opt, device_id=0)
  File "/usr/local/lib/python3.7/dist-packages/onmt/train_single.py", line 136, in main
    model = build_model(model_opt, opt, vocabs, checkpoint)
  File "/usr/local/lib/python3.7/dist-packages/onmt/model_builder.py", line 327, in build_model
    model = build_base_model(model_opt, vocabs, use_gpu(opt), checkpoint)
  File "/usr/local/lib/python3.7/dist-packages/onmt/model_builder.py", line 242, in build_base_model
    model = build_task_specific_model(model_opt, vocabs)
  File "/usr/local/lib/python3.7/dist-packages/onmt/model_builder.py", line 158, in build_task_specific_model
    encoder, src_emb = build_encoder_with_embeddings(model_opt, vocabs)
  File "/usr/local/lib/python3.7/dist-packages/onmt/model_builder.py", line 131, in build_encoder_with_embeddings
    encoder = build_encoder(model_opt, src_emb)
  File "/usr/local/lib/python3.7/dist-packages/onmt/model_builder.py", line 73, in build_encoder
    return str2enc[enc_type].from_opt(opt, embeddings)
  File "/usr/local/lib/python3.7/dist-packages/onmt/encoders/transformer.py", line 120, in from_opt
    add_qkvbias=opt.add_qkvbias
  File "/usr/local/lib/python3.7/dist-packages/onmt/encoders/transformer.py", line 103, in __init__
    for i in range(num_layers)])
  File "/usr/local/lib/python3.7/dist-packages/onmt/encoders/transformer.py", line 103, in <listcomp>
    for i in range(num_layers)])
  File "/usr/local/lib/python3.7/dist-packages/onmt/encoders/transformer.py", line 38, in __init__
    attn_type="self", add_qkvbias=add_qkvbias)
  File "/usr/local/lib/python3.7/dist-packages/onmt/modules/multi_headed_attn.py", line 118, in __init__
    assert model_dim % head_count == 0
AssertionError

I found this issue with the same error, the solution is supposed to be with hyperparameters, but I checked them and can't find the problem. Could you give a hint how to solve this? Thank you!

I tested this code on my own dataset with 4k parallel sentence. But when I trained the model, it always shows "Weighted corpora loaded so far", how can I solve this problem. Thank u

On Google Colab (free version), the training stops after some time with an error like:

RuntimeError: DataLoader worker (pid 629) is killed by signal: Killed.

As verified by running dmesg -T this is a RAM out of memory error.

Memory cgroup out of memory: Killed process 629 (onmt_train) total-vm:14119556kB, anon-rss:6538204kB, file-rss:80652kB, shmem-rss:16kB, UID:0 pgtables:13432kB oom_score_adj:0

I've followed all instructions with a corpus size of around 300,000 (vocab 25,000) and keep on running into this issues (have tried multiple times, same problem). I've completed all pre-processing, model training etc successfully but the library just errors upon a specific entry in the source.vocab (below)

Do you have any idea how I can resolve my issue?