can't load the model

[countback]

model = BertModel.from_pretrained('bert-large-cased')
Model name 'bert-large-cased' was not found in model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese). We assumed 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz' was a path or url but couldn't find any file associated to this path or url.

[thomwolf]

Strange error.

Can you try:

import pytorch_pretrained_bert as ppb
assert 'bert-large-cased' in ppb.modeling.PRETRAINED_MODEL_ARCHIVE_MAP

Do you have an open internet connection on the server that run the script?

[erip]

@thomwolf Is there a way to point to a model on disk? This question seems related enough to daisychain with this issue. :-)

[martiansideofthemoon]

I noticed that this error happens when you exceed the disk space in the temporary directory while downloading BERT.

[AmazingGzZc]

I ran into the same problem. When I used the Chinese pre-training model, it was sometimes good and sometimes bad.

[DuncanCam-Stein]

@thomwolf I've been having the same error, and I received an AssertionError when I try

assert 'bert-based-uncased' in bert.modeling.PRETRAINED_MODEL_ARCHIVE_MAP

I've tried using both conda install and Pip install to get the package but in both cases I am not able to load any models

[thomwolf]

Hi @DuncanCam-Stein,
Which version of python do you have?
Can you try to install from source?

[DuncanCam-Stein]

@thomwolf @countback
I finally fixed the problem by downloading the tf checkpoints directly from here, and then using the 'convert_tf_checkpoint_to_pytorch.py' function to create a pytorch_model.bin file.
I then used the path to pytorch_model.bin and bert_config.json file in BertModel.from_pretrained('path/to/bin/and/json') instead of 'bert-base-uncased'.
👍
Helpful info was found here.

[thomwolf]

The network connection check has been relaxed in the now merged #500.
Serialization of the model have also been simplified a lot with #489.

These improvements will be included in the next PyPI release (probably next week).

In the meantime you can install from master and already use the serialization best-practices described in the README here

[astariul]

As @martiansideofthemoon said, I met this error because I didn't have enough space on disk.

Check if you can download the file with :

wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz

[fabrahman]

@martiansideofthemoon What does that mean if we can download it via wget but not when we use from_pretrained? is it a disk space problem?

[astariul]

@Hannabrahman
If you can download it via wget, it means you have enough disk space, so the issue is from somewhere else.

[fabrahman]

@colanim Thanks. I figured out it was memory issue on the cache directory.

[raj5287]

@Hannabrahman

@colanim Thanks. I figured out it was memory issue on the cache directory.

how did you solve this issue?

[astariul]

@raj5287
Free some disk space on the cache directory or specify another cache directory

[raj5287]

@colanim i have enough disk space since i have downloaded the file using
wget https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz but i am not sure how to specify another cache directory or use the downloaded file (i am new to pytorch and ubuntu :| )

[raj5287]

@thomwolf @countback
I finally fixed the problem by downloading the tf checkpoints directly from here, and then using the 'convert_tf_checkpoint_to_pytorch.py' function to create a pytorch_model.bin file.
I then used the path to pytorch_model.bin and bert_config.json file in BertModel.from_pretrained('path/to/bin/and/json') instead of 'bert-base-uncased'.
+1
Helpful info was found here.

@DuncanCam-Stein i have downloaded and placed pytorch_model.bin and bert_config.json in bert_tagger folder but when i am doing tokenizer = BertModel.from_pretrained('home/user/Download/bert_pos_tagger/bert_tagger/') i am still getting the error : Model name 'home/user/Downloads/bert_pos_tagger/bert_tagger/' was not found in model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese). We assumed 'home/user/Downloads/bert_pos_tagger/bert_tagger/' was a path or url but couldn't find any file associated to this path or url.

[sherjy]

try to delete cahe file and rerun the command

[predictedblog]

I noticed that the error appears when I execute my script in debug mode (in Visual Studio Code). I fixed it by executing the script over the terminal python myscriptname.py once. Afterwards Debug mode works fine.

Btw. I got the same problem with the tokenizer and this also fixed it.

[DTW1004]

model = BertModel.from_pretrained('bert-large-cased')
Model name 'bert-large-cased' was not found in model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese). We assumed 'https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz' was a path or url but couldn't find any file associated to this path or url.

hello，I meet the problem when run the torch bert code 👍

OSError: Can't load weights for 'bert-base-uncased'. Make sure that:

• 'bert-base-uncased' is a correct model identifier listed on 'https://huggingface.co/models'

• or 'bert-base-uncased' is the correct path to a directory containing a file named one of pytorch_model.bin, tf_model.h5, model.ckpt.
  if I can download the bert-base-uncased weight, where I should put the file in ? hope your reply~

[erip]

@DTW1004 check your network connection. This happens when I'm behind a proxy and SSL/proxy isn't configured appropriately.

[1172100222]

bro，I've been having the same error. and then I try to debug the specific code
BertTokenizer.from_pretrained(MODEL_NAME)
step in to the origin code, I find that I could step every line of the transformer in the debug mode. when step out origin code, the tokenizer tool could be used. what‘s more , the code could be run normaly in the next time I run the code.

[andiShan11]

I met the issue and I found the reason is that my server connecting was offline.

[dmr07]

Running into the same issue on AWS Lambda. Neither relative and absolute paths will allow the model to load from pre-trained.

[sayakpaul]

Here's what I am doing:

!wget -q https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz
!tar xf bert-base-multilingual-cased.tar.gz

Now, if I do:

encoder = TFBertModel.from_pretrained("bert-base-multilingual-cased")

I still get:

OSError: Can't load config for 'bert-base-multilingual-cased'. Make sure that:

- 'bert-base-multilingual-cased' is a correct model identifier listed on 'https://huggingface.co/models'

- or 'bert-base-multilingual-cased' is the correct path to a directory containing a config.json file

[caijie12138]

Here's what I am doing:

from transformers import pipeline

def corret_sentence(sentence,unmasker):
res = unmasker(sentence)
return res

if name=='main':
sentence = "关小彤"
new_sentence = ""
unmasker = pipeline('fill-mask', model='uer/chinese_roberta_L-2_H-512')
for idx,ch in enumerate(sentence):
new_sentence = sentence[:idx] + "[MASK]" + sentence[idx+1:]
print(corret_sentence(new_sentence,unmasker))

I get:

ValueError: Could not load model uer/chinese_roberta_L-2_H-512 with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForMaskedLM'>, <class 'transformers.models.bert.modeling_bert.BertForMaskedLM'>).

[apkbala107]

Here's what I am doing:

from transformers import pipeline

def corret_sentence(sentence,unmasker): res = unmasker(sentence) return res

if name=='main': sentence = "关小彤" new_sentence = "" unmasker = pipeline('fill-mask', model='uer/chinese_roberta_L-2_H-512') for idx,ch in enumerate(sentence): new_sentence = sentence[:idx] + "[MASK]" + sentence[idx+1:] print(corret_sentence(new_sentence,unmasker))

I get:

ValueError: Could not load model uer/chinese_roberta_L-2_H-512 with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForMaskedLM'>, <class 'transformers.models.bert.modeling_bert.BertForMaskedLM'>).

how could solve this?

did you solve this problem?

i am also having sample

[zhanghaok]

Free some disk space

how can I free some disk space.
which shell command should i use？

[Sharique-stack]

@thomwolf @countback I finally fixed the problem by downloading the tf checkpoints directly from here, and then using the 'convert_tf_checkpoint_to_pytorch.py' function to create a pytorch_model.bin file. I then used the path to pytorch_model.bin and bert_config.json file in BertModel.from_pretrained('path/to/bin/and/json') instead of 'bert-base-uncased'. 👍 Helpful info was found here.

Can you please specify which model exactly you downloaded and how you ran the function? Thanks