sapienzanlp / ewiser Goto Github PK
View Code? Open in Web Editor NEWA Word Sense Disambiguation system integrating implicit and explicit external knowledge.
License: Other
ewiser's People
Contributors
Stargazers
Watchers
Forkers
alvations francescasal sakib1486 huffon myyyvothrr domattee cgravier elsheikh21 jonathanreeve danmincu danielvasic jesper-sk dolores-park songkaoda federicomartelli phi-lineewiser's Issues
Multilingual language mapper is not publicly shared
Hello @mbevila
Thank you for the new update. Unfortunately, the link to the multilingual mapper in README is not accessible. Please take look at it again.
Thanks
Installation issue with torch-scatter
Hello,
I am trying the exact same commands specified in the README but I run into an issue in the step
pip install torch-scatter torch-sparse -f https://pytorch-geometric.com/whl/torch-1.6.0+cu102.html
I am getting the following error:
/anaconda2/envs/ewiser/lib/python3.7/site-packages/torch/include/ATen/core/interned_strings.h:415:1: note: in expansion of macro ‘FORALL_NS_SYMBOLS’
FORALL_NS_SYMBOLS(DEFINE_SYMBOL)
^~~~~~~~~~~~~~~~~
error: command 'gcc' failed with exit status 1
----------------------------------------
ERROR: Command errored out with exit status 1: anaconda2/envs/ewiser/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-kouq5f1x/torch-sparse_cd2910acb4354bf6868ba6699efa7d35/setup.py'"'"'; file='"'"'/tmp/pip-install-kouq5f1x/torch-sparse_cd2910acb4354bf6868ba6699efa7d35/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(file) if os.path.exists(file) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f
I looked online and the solution mentioned was using python binaries (the same command as above) but that doesn't seem to be working, could you please help?
Installation issue
Hi,
I had some problems with
pip install torch-scatter==latest+${CUDA} torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.5.0.html
since "latest" tag seems to be no longer supported cf. this issue.
You can cope with this issue with the following commands in which the torch version is explicitly asserted
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html
code
Hello! Could you please share the EWISER code soon? I'm reading your paper now and it'd be great if I could reference the code as well. Thank you!
Notebooks?
The README says "EWISER can be used as a spacy plugin. Please check annotate.py and notebooks/inspect_wsd.py". However, there doesn't seem to be any notebooks/ directory. It looks like it is in the .gitignore file. Can you either add that directory to the repo or delete the mention of it from the README?
Spacy 3 not compatible
I tried to use spacy 3 but it gives an error.
Loading checkpoints
How does one load the pre-trained checkpoints?
I tried it with embs = torch.load('ewiser.semcor+wngt.pt') but it gives me an error:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-123918dcfdcf> in <module>
----> 1 embs = torch.load('ewiser.semcor+wngt.pt', map_location = 'cpu')
~/miniconda3/lib/python3.8/site-packages/torch/serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
606 return torch.jit.load(opened_file)
607 return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
--> 608 return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
609
610
~/miniconda3/lib/python3.8/site-packages/torch/serialization.py in _legacy_load(f, map_location, pickle_module, **pickle_load_args)
785 unpickler = pickle_module.Unpickler(f, **pickle_load_args)
786 unpickler.persistent_load = persistent_load
--> 787 result = unpickler.load()
788
789 deserialized_storage_keys = pickle_module.load(f, **pickle_load_args)
ModuleNotFoundError: No module named 'qbert'Spacy plugin broken by change in Spacy 3.0
Hi,
When trying to use the spacy plugin, I get:
python disambiguate.py /home/ubuntu/src/ewiser/ewiser.semcor_base.pt
Traceback (most recent call last):
File "disambiguate.py", line 326, in <module>
nlp.add_pipe(wsd, last=True)
File "/home/ubuntu/anaconda3/envs/python3/lib/python3.6/site-packages/spacy/language.py", line 749, in add_pipe
raise ValueError(err)
ValueError: [E966] `nlp.add_pipe` now takes the string name of the registered component factory, not a callable component. Expected string, but got Disambiguator(
(model): LinearTaggerModel(
(embedder): BERTEmbedder(
(bert_model): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(28996, 1024, padding_idx=0)
(position_embeddings): Embedding(512, 1024)
(token_type_embeddings): Embedding(2, 1024)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(1): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(2): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(3): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(4): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(5): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(6): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(7): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(8): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(9): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(10): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(11): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(12): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(13): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(14): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(15): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(16): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(17): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(18): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(19): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(20): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(21): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(22): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(23): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): BertLayerNorm()
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(activation): Tanh()
)
)
)
(decoder): LinearDecoder(
(embed_tokens): FakeInput()
(linears): ModuleList(
(0): Linear(in_features=1024, out_features=512, bias=True)
)
(dropout): Dropout(p=0.2, inplace=False)
(logits): Linear(in_features=512, out_features=117664, bias=False)
(structured_logits): StructuredLogits(
(adjacency_pars): ParameterList(
(0): Parameter containing: [torch.LongTensor of size 2x174717]
(1): Parameter containing: [torch.FloatTensor of size 174717]
(2): Parameter containing: [torch.LongTensor of size 2]
)
)
(norm): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
) (name: 'None').
- If you created your component with `nlp.create_pipe('name')`: remove nlp.create_pipe and call `nlp.add_pipe('name')` instead.
- If you passed in a component like `TextCategorizer()`: call `nlp.add_pipe` with the string name instead, e.g. `nlp.add_pipe('textcat')`.
- If you're using a custom component: Add the decorator `@Language.component` (for function components) or `@Language.factory` (for class components / factories) to your custom component and assign it a name, e.g. `@Language.component('your_name')`. You can then run `nlp.add_pipe('your_name')` to add it to the pipeline.
Which is similar to an issue I've recently seen with another system. Spacy seems to have changed how these custom pipelines work.
Any chance of a fix in the near future? I can try to fix it myself but it would be nice to have a solution from the authors. Presumably, if my diagnosis is correct, you will have a lot of people asking about this soon.
Thanks,
Alan
Terminate called after throwing an instance of 'std::bad_alloc'
I followed the installation instructions to the dot - including the torch problem described in the first issue but I get the following error when running regardless of the checkpoint issue
python /home/dan/ewiser/bin/annotate.py -c /home/dan/ewiser/res/downloaded/ewiser.semcor_base.pt test.txt
......
2022-07-13 11:58:22 | INFO | pytorch_pretrained_bert.modeling | Model config {
"attention_probs_dropout_prob": 0.1,
"directionality": "bidi",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"max_position_embeddings": 512,
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pooler_fc_size": 768,
"pooler_num_attention_heads": 12,
"pooler_num_fc_layers": 3,
"pooler_size_per_head": 128,
"pooler_type": "first_token_transform",
"type_vocab_size": 2,
"vocab_size": 28996
}
terminate called after throwing an instance of 'std::bad_alloc'
what(): std::bad_alloc
Aborted (core dumped)
Any ideas?
ModuleNotFoundError: No module named 'ewiser.spacy'
from ewiser.spacy.disambiguate import Disambiguator
ModuleNotFoundError: No module named 'ewiser.spacy'
Memory Issue while running model as REST service
I'm using a REST service to run WSD model. But it's RAM keeps on increasing as I the number of hits increase. I was testing how much RAM will be enough for this, so at last I used a 64GB server, and it consumed all of it.
This is the code for rest service:-
import requests
import re
import os
import time
import json
from flask import jsonify
from time import sleep
from json import dumps
from flask import Flask, request
from ewiser.spacy.disambiguate import Disambiguator
import spacy
nlp = spacy.load("en_core_web_sm", disable=['parser', 'ner'])
wsd = Disambiguator("/content/ewiser.semcor+wngt.pt", lang="en")
nlp.add_pipe(wsd, last=True)
app = Flask(name)
@app.route("/wsd", methods=['POST'])
def wsd():
print('service ---------------(( + _ + ))----------------- started')
item = {}
x = request.get_json()
print('+++++++++++++++++++++++++++++++++', x)
sent = x['text']
doc = nlp(sent)
List = []
for w in doc:
if w._.offset:
print(w.text, w.lemma_, w.pos_, w._.offset, w._.synset.definition())
itm= {}
itm['lemma']=w.lemma_
itm['synset']=w._.offset
itm['gloss']=w._.synset.definition()
List.append(itm)
print(List)
return jsonify(List)
if name == "main":
#app.debug = True
app.run(host='localhost', port=7777)
Spacy plugin
Is the spaCy plugin working? The link in the README directs to spaCy homepage.
I am asking because I wanted to use your code in my project for WSD.
multilingual datasets and model
Hi, nice work there. Could you please detail the multilingual dataset version ('all' or 'wn') and also the multilingual BERT version (base or large)?
What do you mean by WordNet offset?
Why not simply use a sense key? Why is there a sensekeys2offsets conversion table?
running issue with fairseq criterion
I got a criterion issue with the class named WeightedCrossEntropyCriterionWithSoftmaxMask, It is implemented in the ewiser\fairseq_ext\criterions, but is not infered. I tried to figure out why it happen.
Babelnet 4.0.1 Dependency
Hello,
It seems that disambiguation for languages other than English requires Babelnet 4.0.1, which is not available anymore. We tried just replacing the BabelNet 4 references in the bash scripts with those to Babelnet 5.0, but this does not work and producing the offset/lemmapos dictionary files from a newer version seems likely to introduce errors from altered offsets.
Download links for the old API from other locations are all dead.
Do you have plans to update this to BabelNet 5.0, or to make 4.0.1 available for this project?
How else would you suggest using the pretrained Multilingual model for other languages?
Thanks
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.