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.
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?
@thomwolf Is there a way to point to a model on disk? This question seems related enough to daisychain with this issue. :-)
I noticed that this error happens when you exceed the disk space in the temporary directory while downloading BERT.
I ran into the same problem. When I used the Chinese pre-training model, it was sometimes good and sometimes bad.
@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
Hi @DuncanCam-Stein,
Which version of python do you have?
Can you try to install from source?
@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'.
馃憤
Helpful info was found here.
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
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
@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?
@Hannabrahman
If you can download it via wget, it means you have enough disk space, so the issue is from somewhere else.
@Colanim Thanks. I figured out it was memory issue on the cache directory.
@Hannabrahman
@Colanim Thanks. I figured out it was memory issue on the cache directory.
how did you solve this issue?
@raj5287
Free some disk space on the cache directory or specify another cache directory
@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 :| )
@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 apytorch_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.
try to delete cahe file and rerun the command
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.
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锛孖 meet the problem when run the torch bert code 馃憤
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~
@DTW1004 check your network connection. This happens when I'm behind a proxy and SSL/proxy isn't configured appropriately.
Most helpful comment
@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'.
馃憤
Helpful info was found here.