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

RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,400574}) do not match parameters to be populated ({100,410050})

Open eahogue opened this issue 3 years ago • 23 comments

Hello,

I tried to train new models from scratch, but that wouldn't work. The targetid training would go fine, but then frameid would halt with a NaN error. It would do this at different points in the training, but always eventually did within the first 10% of training or so. None of the issues regarding this were helpful for me.

So, I downloaded the pre-trained models. When I run the pre-trained targetid model over a file of sentences, I see this:

python3 -m sesame.targetid --mode predict                             --model_name fn1.7-pretrained-targetid                             --raw_input yc_essential_startup_advice.txt
[dynet] random seed: 1809054107
[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: /home/alan/git_projects/open-sesame/sesame/targetid.py --mode predict --model_name fn1.7-pretrained-targetid --raw_input yc_essential_startup_advice.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 [00:07, 83907.23it/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 = 17
        Unlearnt in dev/test = 400017
# POS tags = 45
        Unseen in dev/test = 0
        Unlearnt in dev/test = 1
# Lemmas = 9349
        Unseen in dev/test = 114
        Unlearnt in dev/test = 115

_____________________

Reading model from logs/fn1.7-pretrained-targetid/best-targetid-1.7-model ...
Traceback (most recent call last):
  File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/alan/git_projects/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})

I thought perhaps, for some reason, you were supposed to run a test first, since other issues I found similar to this mentioned that it can happen if the training and testing config aren't perfect matches.

$ python3 -m sesame.targetid --mode test --model_name
fn1.7-pretrained-targetid
[dynet] random seed: 3132663994
[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: /home/alan/git_projects/open-sesame/sesame/targetid.py --mode test --model_name fn1.7-pretrained-targetid
MODEL FOR TEST / PREDICTION:    logs/fn1.7-pretrained-targetid/best-targetid-1.7-model
PARSING MODE:   test
_____________________


Reading data/open_sesame_v1_data/fn1.7/fn1.7.fulltext.train.syntaxnet.conll ...
591032it [00:07, 80464.20it/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 ...

Reading data/open_sesame_v1_data/fn1.7/fn1.7.test.syntaxnet.conll ...
189185it [00:02, 74813.60it/s]
# examples in data/open_sesame_v1_data/fn1.7/fn1.7.test.syntaxnet.conll : 6714 in 1354 sents
# examples with missing arguments : 491
Combined 6714 instances in data into 1354 instances.

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 = 2029
        Unlearnt in dev/test = 402029
# POS tags = 45
        Unseen in dev/test = 0
        Unlearnt in dev/test = 1
# Lemmas = 9349
        Unseen in dev/test = 1958
        Unlearnt in dev/test = 1959

_____________________

Reading model from logs/fn1.7-pretrained-targetid/best-targetid-1.7-model ...
Traceback (most recent call last):
  File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/alan/git_projects/open-sesame/sesame/targetid.py", line 433, 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})

So I am at quite a loss now. Details of what I am running below. Note that I am using the current release of DyNet (2.1.2) because the suggested one in the README seems to be unavailable. This seems to be because Eigen is no longer downloadable from the location it used to be. Installing Eigen from scratch is well over my head (as shown by a cascade of bugs which I won't reproduce here), so I am hoping that I can get by with 2.1.2.

$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description:    Ubuntu 20.04.2 LTS
Release:        20.04
Codename:       focal

$ python3 --version
Python 3.8.5

$ 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: /home/alan/.local/lib/python3.8/site-packages
Requires: click, tqdm, joblib, regex
Required-by:

$ pip show dynet
Name: dyNET
Version: 2.1.2
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: /home/alan/.local/lib/python3.8/site-packages
Requires: numpy, cython
Required-by:

Thank you, Alan

Mar 02 '21 01:03 eahogue

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.

Mar 03 '21 00:03 appleternity

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

Mar 03 '21 17:03 eahogue

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

Mar 04 '21 14:03 Vlad116

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.

Mar 16 '21 03:03 Brit7777

@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. :)

Mar 16 '21 22:03 eahogue

@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!

Mar 17 '21 13:03 Brit7777

@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.

Mar 22 '21 05:03 fairy-of-9

There are actually two problems here. One is the vocab number which appears when you try to make predictions from the pre-trained models. I assume this has something to do with one of the models being corrupted or some number not matching up somewhere. No one seems to know.

Switching to Dynet 2.1 doesn't affect that. What it does is make it possible to train your own models. If you try training with the recommended version of dynet, you get NaN errors. Using the newer version does fix that problem.

The upshot being, if you want to use open-sesame, you have to train your own models first.

Alan

On Sun, Mar 21, 2021 at 10:11 PM fairy-of-9 @.***> wrote:

@eahogue https://github.com/eahogue @Brit7777 https://github.com/Brit7777 @Vlad116 https://github.com/Vlad116 Hi, Everyone I respect.

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

@.***:~/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: @.***

License: Apache License, Version 2.0

Location: /opt/conda/lib/python3.7/site-packages

Requires: tqdm, joblib, regex, click

Required-by:

@.***:~/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: @.***

License: Apache 2.0

Location: /opt/conda/lib/python3.7/site-packages

Requires: numpy, cython

Required-by:

@.***:~/bert_models/minimal-BERT/open-sesame# python -m sesame.targetid --mode predict --model_name fn1.7-pretrained-targetid --raw_input data/input/atomic-emotion.txt

[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/minimal-BERT/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/minimal-BERT/open-sesame/sesame/targetid.py", line 459, in
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.

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/swabhs/open-sesame/issues/61#issuecomment-803764959, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABJ2L3GZNWDW2HEM2WUVLULTE3GQJANCNFSM4YNWTE6Q .

Mar 22 '21 17:03 eahogue

I understood the current situation accurately.

Thanks for your kind reply!

...

> There are actually two problems here. One is the vocab number which appears when you try to make predictions from the pre-trained models. I assume this has something to do with one of the models being corrupted or some number not matching up somewhere. No one seems to know. Switching to Dynet 2.1 doesn't affect that. What it does is make it possible to train your own models. If you try training with the recommended version of dynet, you get NaN errors. Using the newer version does fix that problem. The upshot being, if you want to use open-sesame, you have to train your own models first. Alan > […](#) > On Sun, Mar 21, 2021 at 10:11 PM fairy-of-9 ***@***.***> wrote: @eahogue @Brit7777 @Vlad116 Hi, Everyone I respect. Even though I installed Dynet v.2.1, I have the same problem. ***@***.***:~/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: ***@***.*** License: Apache License, Version 2.0 Location: /opt/conda/lib/python3.7/site-packages Requires: tqdm, joblib, regex, click Required-by: ***@***.***:~/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: ***@***.*** License: Apache 2.0 Location: /opt/conda/lib/python3.7/site-packages Requires: numpy, cython Required-by: ***@***.***:~/bert_models/minimal-BERT/open-sesame# python -m sesame.targetid --mode predict --model_name fn1.7-pretrained-targetid --raw_input data/input/atomic-emotion.txt [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/minimal-BERT/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/minimal-BERT/open-sesame/sesame/targetid.py", line 459, in 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. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub , or unsubscribe .

Mar 23 '21 00:03 fairy-of-9

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!:)

Mar 25 '21 11:03 CompileZero

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.

Mar 25 '21 13:03 Vlad116

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. :)

Mar 26 '21 03:03 CompileZero

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.

Mar 26 '21 20:03 Vlad116

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?

Mar 27 '21 08:03 CompileZero

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

Mar 27 '21 09:03 Vlad116

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...

Mar 29 '21 04:03 CompileZero

@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?

Apr 29 '21 03:04 SAI990323

@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.

May 23 '21 15:05 Alexia1994

@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

Jun 01 '21 21:06 Vlad116

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

Mar 19 '22 17:03 YuxiaWu

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

Apr 18 '22 21:04 chingachleung

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

May 25 '22 16:05 chanind

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

Jun 07 '22 06:06 shakib-root

open-sesame open-sesame copied to clipboard

RuntimeError: Dimensions of lookup parameter /_0 lookup up from file ({100,400574}) do not match parameters to be populated ({100,410050})

examples in data/open_sesame_v1_data/fn1.7/fn1.7.fulltext.train.syntaxnet.conll : 19391 in 3413 sents

examples with missing arguments : 526

unique targets = 9421

total targets = 13572

targets with multiple LUs = 4151

max LUs per target = 5

Tokens = 410050

POS tags = 45

Lemmas = 9349

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})

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})

open-sesame
open-sesame copied to clipboard

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})`

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})`