RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,400574}) do not match parameters to be populated ({100,410050}) about open-sesame HOT 23 OPEN

Hello @eahogue , how much time approximately did it take for you to train the model (targetid)?
I have started training the model but its taking forever to train it. My specs are as follows and I am concerned that it will take a lot of time to train the models:
MacBook Pro (13-inch, 2020, Four Thunderbolt 3 ports)
Processor - 2 GHz Quad-Core Intel Core i5
Graphics - Intel Iris Plus Graphics 1536 MB
RAM - 16 GB 3733 MHz LPDDR4X
Also, if possible, can someone upload the trained models for targetid, frameid and argid, so that maybe others could use them as pre-trained models? Thanks!:)

Hello, I can try loading my trained models for targetid, frameid. But I haven't tested them yet as the model for argid is still learning (4th day). And they will also have less accuracy than stated in this table https://github.com/swabhs/open-sesame#pre-trained-models .
It would be better to share them after checking the work and additional training.

Can you still upload the trained models for targetid and frameid? I dont think there would be much of a difference between the accuracy of your models and the pretrained ones as they are trained on the same fn data. :)

https://drive.google.com/file/d/1BMQJXbjhPItmdH8ZWRlqD3vKU16TAJrr/view?usp=sharing
Yes of course. This archive contains all 3 models.

Best dev f1:

• argid 0.5912
• targetid 0.7892
• frameid 0.8930

I will still try to retrain them later.

from open-sesame.

I struggled to get sesame working due to this issue, so instead trained a T5 transformer model using the open-sesame data/splits. It gets pretty comparable results in trigger ID and frame ID, but does better in arg ID. Sharing here in case it's useful to others: https://github.com/chanind/frame-semantic-transformer

from open-sesame.

I had the same issue ( frameid haltingwith a NaN error), but I got it solved by using Dynet v. 2.1. You could do this with 'pip install dynet==2.1' command line.

from open-sesame.

I am also working on the same problem. I haven't totally solved the problem but I do solve one bug in the preprocessing script which is related.

In "preprocess.py", line 79, remove the encode function.

outf.write(str(token.encode('utf-8')) + "\t")  # The encode shouldn't appear here in the python3 version
outf.write(str(token) + "\t")  # FORM = 1 # The correct one

When using encode function here, the output of the training data would be

1   b'Over'    _   Over    in  IN  1   _   _   _   _   _   _   _   O

, where the prefix b indicates that it is a byte. When parsing training data to build the vocabulary (VOCDICT), these words are treated as a whole (b'Over'). The vocabulary size is then larger than the original one.

After solving this, the vocabulary size doesn't match still. Now it is 400572 but the provided model has 400574 words.

Waiting for someone else to dig further into the issue. Thanks.

from open-sesame.

Thanks for that tip! Still hoping someone has insight into the cause of this problem. I'd be willing to try fixing it.

from open-sesame.

I also faced a completely similar problem.
All the past problems with similar errors did not help.
Thanks @appleternity for that tip.

from open-sesame.

@Brit7777 Wow, that worked! Thank you!

Hopefully with our own trained models, the other problem of vocab size will be solved as well. I will know for sure by tomorrow, probably. :)

from open-sesame.

@eahogue I'm training my model rn as well haha. Training argid takes forever....Please let me know if the trained model works without error!

from open-sesame.

@eahogue @Brit7777 @Vlad116
Hi, Everyone I respect.

Even though I installed Dynet v.2.1, I have the same problem.

root@935fe2bce6cc:~/bert_models/open-sesame# pip show nltk
Name: nltk
Version: 3.5
Summary: Natural Language Toolkit
Home-page: http://nltk.org/
Author: Steven Bird
Author-email: [email protected]
License: Apache License, Version 2.0
Location: /opt/conda/lib/python3.7/site-packages
Requires: tqdm, joblib, regex, click
Required-by:
root@935fe2bce6cc:~/bert_modelsopen-sesame# pip show dynet
Name: dyNET
Version: 2.1
Summary: The Dynamic Neural Network Toolkit
Home-page: https://github.com/clab/dynet
Author: Graham Neubig
Author-email: [email protected]
License: Apache 2.0
Location: /opt/conda/lib/python3.7/site-packages
Requires: numpy, cython
Required-by:

root@935fe2bce6cc:~/bert_models/open-sesame# python -m sesame.targetid --mode predict  --model_name fn1.7-pretrained-targetid
[dynet] random seed: 715363830
[dynet] allocating memory: 512MB
[dynet] memory allocation done.
ARGID_LR:       0.0005
DATA_DIRECTORY: data/
DEBUG_MODE:     False
EMBEDDINGS_FILE:        data/glove.6B.100d.txt
VERSION:        1.7
_____________________
COMMAND: /root/bert_models/open-sesame/sesame/targetid.py --mode predict --model_name fn1.7-pretrained-targetid --raw_input data/input/atomic-emotion.txt
MODEL FOR TEST / PREDICTION:    logs/fn1.7-pretrained-targetid/best-targetid-1.7-model
PARSING MODE:   predict
_____________________


Reading data/open_sesame_v1_data/fn1.7/fn1.7.fulltext.train.syntaxnet.conll ...
591032it [00:06, 96443.46it/s]
# examples in data/open_sesame_v1_data/fn1.7/fn1.7.fulltext.train.syntaxnet.conll : 19391 in 3413 sents
# examples with missing arguments : 526
Combined 19391 instances in data into 3413 instances.

Reading the lexical unit index file: data/fndata-1.7/luIndex.xml
# unique targets = 9421
# total targets = 13572
# targets with multiple LUs = 4151
# max LUs per target = 5


Reading pretrained embeddings from data/glove.6B.100d.txt ...

PARSER SETTINGS (see logs/fn1.7-pretrained-targetid/configuration.json)
_____________________
DEV_EVAL_EPOCH_FREQUENCY:       3
DROPOUT_RATE:   0.01
EVAL_AFTER_EVERY_EPOCHS:        100
HIDDEN_DIM:     100
LEMMA_DIM:      100
LSTM_DEPTH:     2
LSTM_DIM:       100
LSTM_INPUT_DIM: 100
NUM_EPOCHS:     100
PATIENCE:       25
POS_DIM:        100
PRETRAINED_EMBEDDING_DIM:       100
TOKEN_DIM:      100
TRAIN:  data/neural/fn1.7/fn1.7.fulltext.train.syntaxnet.conll
UNK_PROB:       0.1
USE_DROPOUT:    True

# Tokens = 410050
        Unseen in dev/test = 550
        Unlearnt in dev/test = 400550
# POS tags = 45
        Unseen in dev/test = 0
        Unlearnt in dev/test = 1
# Lemmas = 9349
        Unseen in dev/test = 5495
        Unlearnt in dev/test = 5496

_____________________

Reading model from logs/fn1.7-pretrained-targetid/best-targetid-1.7-model ...
Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/opt/conda/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/root/bert_models/open-sesame/sesame/targetid.py", line 459, in <module>
    model.populate(model_file_name)
  File "_dynet.pyx", line 1461, in _dynet.ParameterCollection.populate
  File "_dynet.pyx", line 1516, in _dynet.ParameterCollection.populate_from_textfile
RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,400574}) do not match parameters to be populated ({100,410050})

Is there anything I miss?

I’d be glad if you could help me.
Thanks.

from open-sesame.

from open-sesame.

I understood the current situation accurately.

Thanks for your kind reply!

from open-sesame.

Hello @eahogue , how much time approximately did it take for you to train the model (targetid)?

I have started training the model but its taking forever to train it. My specs are as follows and I am concerned that it will take a lot of time to train the models:

MacBook Pro (13-inch, 2020, Four Thunderbolt 3 ports)
Processor - 2 GHz Quad-Core Intel Core i5
Graphics - Intel Iris Plus Graphics 1536 MB
RAM - 16 GB 3733 MHz LPDDR4X

Also, if possible, can someone upload the trained models for targetid, frameid and argid, so that maybe others could use them as pre-trained models? Thanks!:)

from open-sesame.

Hello @eahogue , how much time approximately did it take for you to train the model (targetid)?

I have started training the model but its taking forever to train it. My specs are as follows and I am concerned that it will take a lot of time to train the models:

MacBook Pro (13-inch, 2020, Four Thunderbolt 3 ports)
Processor - 2 GHz Quad-Core Intel Core i5
Graphics - Intel Iris Plus Graphics 1536 MB
RAM - 16 GB 3733 MHz LPDDR4X

Also, if possible, can someone upload the trained models for targetid, frameid and argid, so that maybe others could use them as pre-trained models? Thanks!:)

Hello, I can try loading my trained models for targetid, frameid. But I haven't tested them yet as the model for argid is still learning (4th day). And they will also have less accuracy than stated in this table https://github.com/swabhs/open-sesame#pre-trained-models .
It would be better to share them after checking the work and additional training.

from open-sesame.

Hello @eahogue , how much time approximately did it take for you to train the model (targetid)?
I have started training the model but its taking forever to train it. My specs are as follows and I am concerned that it will take a lot of time to train the models:
MacBook Pro (13-inch, 2020, Four Thunderbolt 3 ports)
Processor - 2 GHz Quad-Core Intel Core i5
Graphics - Intel Iris Plus Graphics 1536 MB
RAM - 16 GB 3733 MHz LPDDR4X
Also, if possible, can someone upload the trained models for targetid, frameid and argid, so that maybe others could use them as pre-trained models? Thanks!:)

Hello, I can try loading my trained models for targetid, frameid. But I haven't tested them yet as the model for argid is still learning (4th day). And they will also have less accuracy than stated in this table https://github.com/swabhs/open-sesame#pre-trained-models .
It would be better to share them after checking the work and additional training.

Can you still upload the trained models for targetid and frameid? I dont think there would be much of a difference between the accuracy of your models and the pretrained ones as they are trained on the same fn data. :)

from open-sesame.

https://drive.google.com/file/d/1BMQJXbjhPItmdH8ZWRlqD3vKU16TAJrr/view?usp=sharing
Yes of course. This archive contains all 3 models.

Best dev f1:

• argid 0.5912
• targetid 0.7892
• frameid 0.8930

I will still try to retrain them later.

Thanks a lot for this. But I am still getting the same error RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,410050}) do not match parameters to be populated ({100,400573})

My configuration is:

• Python 3.8.5
• DyNet 2.1.2
• nltk 3.5
• glove embeddings glove.6B.100d.txt
• fn1.7

Are these same as yours or have you used some other configuration to train the models?

from open-sesame.

https://drive.google.com/file/d/1BMQJXbjhPItmdH8ZWRlqD3vKU16TAJrr/view?usp=sharing
Yes of course. This archive contains all 3 models.
Best dev f1:

• argid 0.5912
• targetid 0.7892
• frameid 0.8930

I will still try to retrain them later.

Thanks a lot for this. But I am still getting the same error RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,410050}) do not match parameters to be populated ({100,400573})

My configuration is:

• Python 3.8.5
• DyNet 2.1.2
• nltk 3.5
• glove embeddings glove.6B.100d.txt
• fn1.7

Are these same as yours or have you used some other configuration to train the models?

Try dyNET == 2.1 as mentioned earlier.
My configuration is like this:

• Python 3.7.9
• nltk == 3.5
• dyNET == 2.1
• glove embeddings glove.6B.100d.txt
• fn1.7

from open-sesame.

Try dyNET == 2.1 as mentioned earlier.
My configuration is like this:

• Python 3.7.9
• nltk == 3.5
• dyNET == 2.1
• glove embeddings glove.6B.100d.txt
• fn1.7

Thanks for this. Surprisingly, it was still not working on my Mac configuration. Tested on Windows with the same configuration and it works! Will deep dive into it and see why it's not working on MacOS...

from open-sesame.

@Brit7777 Wow, that worked! Thank you!

Hopefully with our own trained models, the other problem of vocab size will be solved as well. I will know for sure by tomorrow, probably. :)

Hi, did you make any changes to the code you successfully ran except for the version of dynet?

from open-sesame.

@Vlad116 hi, sorry to bother. I am trying to use your trained model by running following command:
python -m sesame.targetid --mode predict --model_name fn1.7-targetid --raw_input input.txt
but I met the following problem:
RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,410050}) do not match parameters to be populated ({100,400000})
Is there anything I miss?

My configuration is like:
python 3.6.12
nltk==3.5
dyNET == 2.1
glove embeddings glove.6B.100d.txt downloaded from here

Could you please upload your glove.6B.100d.txt?
I’d be glad if you could help me.
Thanks.

from open-sesame.

@Vlad116 hi, sorry to bother. I am trying to use your trained model by running following command:
python -m sesame.targetid --mode predict --model_name fn1.7-targetid --raw_input input.txt
but I met the following problem:
RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,410050}) do not match parameters to be populated ({100,400000})
Is there anything I miss?

My configuration is like:
python 3.6.12
nltk==3.5
dyNET == 2.1
glove embeddings glove.6B.100d.txt downloaded from here

Could you please upload your glove.6B.100d.txt?
I’d be glad if you could help me.
Thanks.

@Alexia1994 Hi, could there be a problem in the Python version? It seems there was this file
https://drive.google.com/file/d/1V__v9Lt4Fb_rwZD9ogYXFjaoWrLNrPb9/view?usp=sharing

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@Alexia1994 hi, I also have the same problem. Have you solved it yet?

from open-sesame.

+up same problem here. Trying to avoid retraining since it is taking a lonnng time

from open-sesame.

@chanind very nice work! Thanks for sharing. I will try with your repo.

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