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View Code? Open in Web Editor NEWA library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.
License: MIT License
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minthigpen imatiach-msft eedeleon shangodbole tomdyer10 exoblivione gautamtverma janhavi13 amoshua lizarova777 fighting41love rmoin abhaypsingh star1327p mahmoodm2 ssusantachary kingsman1960 bkarski yunsizhang simoneperazzoli nehalecky paolorampazzo christenurs chrinide lycbest1-yicheng cadae techc-ego sohaibe93 rmahdav samiaaskari takaushik joycexu02 brandonliang kenanekici anindya5 thoughtfulmind jb9952 neha-chawla d2as2 minalspatil kc82 thefonseca tetsuoh0103 mohiuddin02 rothsword marcelgardias syu999-github databill86 techthiyanes zsigacsaba pavanyeluri mobilewish ruiwang1994 alienstemple jggunn jacojvr miku-uematsu deepakrajkranti 434622 branden-kang madhuvani5103 alokbpandey sonicplobal jbarsotti jcarlosneto mohsinposts adityadj98interpret-text's Issues
Output individual layer explanation for BERT
Review and cleanup Three Player Introspective Explainer
Refactor devops pipelines
Data serialization
[Classical Explainer] explain_local should support predicted_label as an optional parameter.
The visualization dashboard always defaulted to "fiction" label
repro:
document = "Demonstrating Product Reliability Indicates the Product Is Ready for Production"
local_explanation = explainer.explain_local(document)
local_explanation._predicted_label = None
Solution:
Pass the predicted_label while creating local_explanation in explain_local.
Vocab Error when running 'Interpreting Classical Text Classification models' Notebook
Using a clean Python 3.7 environment on Ubuntu, and installing interpret-text using pip, I am hitting an error when I try to walk through the 'Interpreting Classical Text Classification models' notebook; I have made no changes to the code.
When attempting to fit the model, on the line:
classifier, best_params = explainer.fit(X_train, y_train)
I get the following error:
/anaconda/envs/interpret/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:489: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'
warnings.warn("The parameter 'token_pattern' will not be used"
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-47f4fc43855d> in <module>
----> 1 classifier, best_params = explainer.fit(X_train, y_train)
/anaconda/envs/interpret/lib/python3.7/site-packages/interpret_text/experimental/classical.py in fit(self, X_str, y_train)
92 :rtype: list
93 """
---> 94 X_train = self._encode(X_str)
95 if self.is_trained is False:
96 if self.model is None:
/anaconda/envs/interpret/lib/python3.7/site-packages/interpret_text/experimental/classical.py in _encode(self, X_str)
61 :rtype: array_like (ndarray, pandas dataframe). Same rows as X_str
62 """
---> 63 X_vec, _ = self.preprocessor.encode_features(X_str)
64 return X_vec
65
/anaconda/envs/interpret/lib/python3.7/site-packages/interpret_text/experimental/common/utils_classical.py in encode_features(self, X_str, needs_fit, keep_ids)
129 # needs_fit will be set to true if encoder is not already trained
130 if needs_fit is True:
--> 131 self.vectorizer.fit(X_str)
132 if isinstance(X_str, str):
133 X_str = [X_str]
/anaconda/envs/interpret/lib/python3.7/site-packages/sklearn/feature_extraction/text.py in fit(self, raw_documents, y)
1167 """
1168 self._warn_for_unused_params()
-> 1169 self.fit_transform(raw_documents)
1170 return self
1171
/anaconda/envs/interpret/lib/python3.7/site-packages/sklearn/feature_extraction/text.py in fit_transform(self, raw_documents, y)
1201
1202 vocabulary, X = self._count_vocab(raw_documents,
-> 1203 self.fixed_vocabulary_)
1204
1205 if self.binary:
/anaconda/envs/interpret/lib/python3.7/site-packages/sklearn/feature_extraction/text.py in _count_vocab(self, raw_documents, fixed_vocab)
1131 vocabulary = dict(vocabulary)
1132 if not vocabulary:
-> 1133 raise ValueError("empty vocabulary; perhaps the documents only"
1134 " contain stop words")
1135
ValueError: empty vocabulary; perhaps the documents only contain stop words
Am I missing something obvious here?
Add test to verify setup.py works everytime
Classical text explainer - return tuple instead of multiple returns
Rationale Introspective Explainer Visualization Bugs
[cls] and [sep] should not show up. Also the label doesn't change and the anti-rationales, which would have been the neg feat. importance are not passed into the dashboard
Fix README broken links
We shouldn't autoplot on create global explanation for classical text explainer
Benchmarking framework
Create robust testing framework
- Setup build and test pipeline
- Get tests to run faster
- Restructure tests more like interpret-community
Remove pinned transformer version from generate_conda_files
Remove pinned transfomer version from generate_conda_files
Issue with Introspective Rationale Explainer with RNN
when using explainer.fit(df_train, df_test) i am getting following error
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
API Inconsistencies with interpretml/interpret library
Looking through the API differences between interpret-text-contrib and intpret we found 3 explain_local calls in interpret-text-contrib:
-
def explain_local(self, input_text, abs_sum_to_one=False):
-
def explain_local(self, text: str, **kwargs) -> _create_local_explanation:
-
def explain_local(self, text, classes=None, predicted_label=None, true_label=None, num_iteration=150):
Interpret's spec is more like:
- def explain_local(self, X, y=None, **kwargs)
Will be updating the explain_local calls to the above spec to improve interop with interpret. The spec is still not exact, but a push in the right direction.
We will also explicitly fail for calls of a list instead of the current failure mode. Future support for a list of strings is on the roadmap.
Using bert-base-chinese causes CUDA error: device-side assert triggered
I am following the
Text Classification of MultiNLI Sentences using BERT notebook to interpret BERT classification work. When I changed the line LANGUAGE = Language.ENGLISH to LANGUAGE = Language.CHINESE, indicating to use the bert-base-chinese pre-trained model, the following error occured:
interpreter_unified.explain_local('', 'fiction')
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~/Projects/interpret-text/notebooks/text_classification/unified_info_explainer_sample.py in
----> 1 interpreter_unified.explain_local('', 'fiction')
~/miniconda3/envs/interpret_gpu/lib/python3.7/site-packages/interpret_text/experimental/unified_information.py in explain_local(self, X, y, name, num_iteration)
95 X = _validate_X(X)
96
---> 97 embedded_input, parsed_sentence = _get_single_embedding(self.model, X, self.device)
98 self.input_embeddings = embedded_input
99 self.parsed_sentence = parsed_sentence
~/miniconda3/envs/interpret_gpu/lib/python3.7/site-packages/interpret_text/experimental/common/utils_unified.py in _get_single_embedding(model, text, device)
33 words = [BertTokens.CLS] + tokenizer.tokenize(text) + [BertTokens.SEP]
34 tokenized_ids = tokenizer.convert_tokens_to_ids(words)
---> 35 token_tensor = torch.tensor([tokenized_ids], device=device)
36 embedding = model.bert.embeddings(token_tensor)[0]
37 return embedding, words
I tried with the original dataset as used in the example notebook, and also tried with my own dataset with Chinese texts. Both resulted in the above error. There was no issue when using multilingual model.
Unified Information Explainer - Optimize Performance
- Benchmark framework should capture the slowness
Support for n-grams
Explainers: do we add back dropped inputs?
unified_information
- predicted_labels
introspect_rationale
- hard_importances
- preprocessor
Python 3.8 not compatible
I got dependencies conflicts when trying to create interpret_cpu virtual environement. I had to manually set python version to 3.7 on the yaml file to fix the problem.
Error while installing from setup.py
Getting 'interpret_text\widget\static\extension.js' while installing the library for running the notebook.
Error text: "error: can't copy 'interpret_text\widget\static\extension.js': doesn't exist or not a regular file"
Update unified_information model to use transformers
The sample notebook doesn't work on machines with CUDA capability
I got this error when trying to run the Introspective Rationale Explainer Sample Notebook:
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
After some preliminary investigation, the problem seems to be this line. It ignores the self.args.cuda parameter and decides whether to put the z mask purely based on where CUDA is available.
Zero shot classifier with interpret text
Hi
I am using the huggingface transformers zero shot classification pipeline.
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
model="facebook/bart-large-mnli")
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
I would like to use the word importance feature of the interpret-text module to get the important words for the predicted label.
Can you please help on how I could do this ?
Thanks,
Subham
AttributeError: 'LogisticRegression' object has no attribute 'encode_features'
The document mentioned that we can pass any linear or tree model to ClassicTreeExplainer. All code works smoothly if we use the classifier from interpret-text, however, if we train a logistic regression on the outside, and try to pass it to classic explainer, it gives the above error.
Now, the question is, do the explainers actually support any outside model or it has to be fitted from classifiers inside interpret-text package?
My code:
n = X_train.shape[0]
vec = TfidfVectorizer(ngram_range=(1,2), tokenizer=tokenize, min_df=3, max_df=0.9, strip_accents='unicode', use_idf=1, smooth_idf=1, sublinear_tf=1 )
trn_term_doc = vec.fit_transform(X_train)
test_term_doc = vec.transform(X_test)
m = LogisticRegression()
model1=m.fit(x,y_train)
predicted_label = model1.predict(test_x)
explainer = ClassicalTextExplainer(model1)
local_explanation = explainer.explain_local(X_test.iloc[0], predicted_label[0])
The last line results in the error
AttributeError Traceback (most recent call last)
in
----> 1 local_explanation = explainer.explain_local(X_test.iloc[0], predicted_label[0])
~/anaconda3/envs/fairlearn/lib/python3.6/site-packages/interpret_text/experimental/classical.py in explain_local(self, X, y, name)
143 X = _validate_X(X)
144
--> 145 [encoded_text, _] = self.preprocessor.encode_features(
146 X, needs_fit=False
147 )
AttributeError: 'LogisticRegression' object has no attribute 'encode_features'
X_test is the actual texts in the test data, X_test.iloc[0] is actually "Thank you for correcting my typo in Thomas Pooley. I am the world's worst proofreader!"
Just want to find an answer
Dashboard not displayed
I'm running the notebook:
I am expecting a dashboard but I see this :
I tried some suggestions on StackOverflow and also this one : #142 , but no luck.
Observation is same for both Colab and local Jupyter notebook
Remove need for tools/ directory
setup.py depends on most dependencies but the conda install from tools/ resolves an issue taht pip requirements do not.
An example of a failed build:
Base test coverage
Windows builds slow
The Windows builds in the new pipelines are several times slower than the Linux and MacOS ones. This needs to be investigated.
Standardize model parameter passing
Install packages failed with OSError
Hi experts,
Install packages (Jupyter Widget) failed with the following error on Azure ML Compute Instance. Any workaround on this ?
(interpret_gpu) azureuser@interpretml:/mnt/batch/tasks/shared/LS_root/mounts/clusters/interpretml/code/Users/keonabut/interpret-text/python$ jupyter nbextension install interpret_text.experimental.widget --py --sys-prefix
Installing /mnt/batch/tasks/shared/LS_root/mounts/clusters/interpretml/code/Users/keonabut/interpret-text/python/interpret_text/experimental/widget/static -> interpret-text-widget
Up to date: /anaconda/envs/interpret_gpu/share/jupyter/nbextensions/interpret-text-widget/extension.js.map
Copying: /mnt/batch/tasks/shared/LS_root/mounts/clusters/interpretml/code/Users/keonabut/interpret-text/python/interpret_text/experimental/widget/static/index.js -> /anaconda/envs/interpret_gpu/share/jupyter/nbextensions/interpret-text-widget/index.js
Traceback (most recent call last):
File "/anaconda/envs/interpret_gpu/bin/jupyter-nbextension", line 11, in <module>
sys.exit(main())
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/jupyter_core/application.py", line 270, in launch_instance
return super(JupyterApp, cls).launch_instance(argv=argv, **kwargs)
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/traitlets/config/application.py", line 664, in launch_instance
app.start()
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/notebook/nbextensions.py", line 983, in start
super(NBExtensionApp, self).start()
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/jupyter_core/application.py", line 259, in start
self.subapp.start()
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/notebook/nbextensions.py", line 711, in start
self.install_extensions()
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/notebook/nbextensions.py", line 690, in install_extensions
**kwargs
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/notebook/nbextensions.py", line 220, in install_nbextension_python
destination=dest, logger=logger
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/notebook/nbextensions.py", line 191, in install_nbextension
_maybe_copy(src, dest_file, logger=logger)
File "/anaconda/envs/interpret_gpu/lib/python3.6/site-packages/notebook/nbextensions.py", line 1041, in _maybe_copy
shutil.copy2(src, dest)
File "/anaconda/envs/interpret_gpu/lib/python3.6/shutil.py", line 264, in copy2
copystat(src, dst, follow_symlinks=follow_symlinks)
File "/anaconda/envs/interpret_gpu/lib/python3.6/shutil.py", line 229, in copystat
_copyxattr(src, dst, follow_symlinks=follow)
File "/anaconda/envs/interpret_gpu/lib/python3.6/shutil.py", line 157, in _copyxattr
names = os.listxattr(src, follow_symlinks=follow_symlinks)
OSError: [Errno 38] Function not implemented: '/mnt/batch/tasks/shared/LS_root/mounts/clusters/interpretml/code/Users/keonabut/interpret-text/python/interpret_text/experimental/widget/static/index.js'Thanks
Keita
Got an error when running the Rationale Explainer Sample Notebook (with `fixed_classifier` = False)
When trying to run the Introspective Rationale Explainer Sample Notebook (after going around #150 by using specifying CUDA=True), this error came up:
RuntimeError: one of the variables needed for gradient computation has been modified by an in-place operation: [torch.FloatTensor [50, 150]], which is output 0 of TBackward, is at version 9; expected version 8 instead.
This time the problem seems to be from training the classifier used to feed label to the generator (specifically, this block of code). The notebook can run to the end after commenting out this block (line 232 to 235).
(Update: an alternative workaround is to set fixed_classifier=True and pretrain_cls=True in model_config. This should pre-train the classifier and freeze the classifier weights during generator training.)
(I used PyTorch 1.7 to run the notebook.)
Dashboard is not displayed
I'm running the notebook:
And I'm expecting to see a pretty dashboard at the end, but all I see is:
I tried some suggestions from this post on StackOverflow, but it doesn't help.
Any idea how I might fix this?
Is this a bug?
Shouldn't self._ordered_local_importance_values = self.local_importance_values[order] ? Why are the importance values not reordered?
Issue with Introspective Rationale Explainer for BERT on text
explainer.fit(df_train, df_test) is giving error
'RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.'
I'm getting this error on running the example notebook as well as trying on my own data
Extend to NN other than BERT
fp16 compatibility StopIteration on Multiple GPU's: Text Classification of MultiNLI Sentences using BERT
Hello!
I hope you're doing great. On the other hand, I had this issue while running this jupyter notebook Text Classification of MultiNLI Sentences using BERT.
Environment: On-Premises
Computer: Macbook Pro 16"
CPU: intel i9 9980HK
RAM: 64GB
GPU: 2 x TITAN RTX 24GB in RAM
GPU Enclosure: 2 x Razer Core X Chrome Thunderbold 3 (1 connected to the left and one connected to the right)
Conda Version: conda 4.8.3
Python Version: Python 3.7.7
Packages Installed:
Name Version Build Channel
_anaconda_depends 2020.02 py37_0
_pytorch_select 1.1.0 cpu
_r-mutex 1.0.0 anacondar_1
_tflow_select 2.1.0 gpu
absl-py 0.9.0 py37_0
alabaster 0.7.12 py37_0
anaconda custom py37_1
anaconda-client 1.7.2 py37_0
anaconda-project 0.8.4 py_0
argh 0.26.2 py37_0
asn1crypto 1.3.0 py37_0
astor 0.8.0 py37_0
astroid 2.3.3 py37_0
astropy 4.0.1.post1 py37he774522_1
atomicwrites 1.4.0 py_0
attrs 19.3.0 py_0
autopep8 1.4.4 py_0
babel 2.8.0 py_0
backcall 0.1.0 py37_0
backports 1.0 py_2
backports.shutil_get_terminal_size 1.0.0 py37_2
bcrypt 3.1.7 py37he774522_0
beautifulsoup4 4.9.0 py37_0
bitarray 1.2.1 py37he774522_0
bkcharts 0.2 py37_0
blas 1.0 mkl
bleach 3.1.4 py_0
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blis 0.4.1 pypi_0 pypi
blosc 1.16.3 h7bd577a_0
bokeh 2.0.2 py37_0
boto 2.49.0 py37_0
boto3 1.13.13 pypi_0 pypi
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bottleneck 1.3.2 py37h2a96729_0
brotli 1.0.7 pypi_0 pypi
bzip2 1.0.8 he774522_0
ca-certificates 2020.4.5.1 hecc5488_0 conda-forge
cached-property 1.5.1 pypi_0 pypi
cachetools 3.1.1 py_0
catalogue 1.0.0 pypi_0 pypi
certifi 2020.4.5.1 py37hc8dfbb8_0 conda-forge
cffi 1.14.0 py37h7a1dbc1_0
chardet 3.0.4 py37_1003
click 7.1.2 py_0
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cudatoolkit 10.1.243 h74a9793_0
cudnn 7.6.5 cuda10.1_0
curl 7.69.1 h2a8f88b_0
cycler 0.10.0 py37_0
cymem 2.0.3 pypi_0 pypi
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dash 1.12.0 pypi_0 pypi
dash-core-components 1.10.0 pypi_0 pypi
dash-cytoscape 0.1.1 pypi_0 pypi
dash-html-components 1.0.3 pypi_0 pypi
dash-renderer 1.4.1 pypi_0 pypi
dash-table 4.7.0 pypi_0 pypi
dask 2.16.0 py_0
dask-core 2.16.0 py_0
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diff-match-patch 20181111 py_0
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distributed 2.16.0 py37_0
docutils 0.15.2 pypi_0 pypi
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fastcache 1.1.0 py37he774522_0
filelock 3.0.12 py_0
flake8 3.7.9 py37_0
flask 1.1.2 pypi_0 pypi
flask-compress 1.5.0 pypi_0 pypi
freetype 2.9.1 ha9979f8_1
fsspec 0.7.1 py_0
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gast 0.2.2 py37_0
get_terminal_size 1.0.0 h38e98db_0
gevent 20.5.0 pypi_0 pypi
glob2 0.7 py_0
google-auth 1.14.1 py_0
google-auth-oauthlib 0.4.1 py_2
google-pasta 0.2.0 py_0
greenlet 0.4.15 py37hfa6e2cd_0
grpcio 1.27.2 py37h351948d_0
h5py 2.10.0 py37h5e291fa_0
hdf5 1.10.4 h7ebc959_0
heapdict 1.0.1 py_0
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hypothesis 5.11.0 py_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha925a31_3
idna 2.9 py_1
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imageio 2.8.0 py_0
imagesize 1.2.0 py_0
importlib_metadata 1.5.0 py37_0
intel-openmp 2020.1 216
interpret 0.1.22 pypi_0 pypi
interpret-community 0.11.1 pypi_0 pypi
interpret-core 0.1.21 pypi_0 pypi
interpret-text 0.1.1 pypi_0 pypi
intervaltree 3.0.2 py_0
ipykernel 5.1.4 py37h39e3cac_0
ipython 7.13.0 py37h5ca1d4c_0
ipython_genutils 0.2.0 py37_0
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jdcal 1.4.1 py_0
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jsonschema 3.2.0 py37_0
jupyter 1.0.0 py37_7
jupyter_client 6.1.3 py_0
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jupyter_contrib_core 0.3.3 py_2 conda-forge
jupyter_contrib_nbextensions 0.5.1 py37_0 conda-forge
jupyter_core 4.6.3 py37_0
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lz4-c 1.8.1.2 h2fa13f4_0
lzo 2.10 he774522_2
m2w64-bwidget 1.9.10 2
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m2w64-expat 2.1.1 2
m2w64-fftw 3.3.4 6
m2w64-flac 1.3.1 3
m2w64-gcc-libgfortran 5.3.0 6
m2w64-gcc-libs 5.3.0 7
m2w64-gcc-libs-core 5.3.0 7
m2w64-gettext 0.19.7 2
m2w64-gmp 6.1.0 2
m2w64-gsl 2.1 2
m2w64-libiconv 1.14 6
m2w64-libjpeg-turbo 1.4.2 3
m2w64-libogg 1.3.2 3
m2w64-libpng 1.6.21 2
m2w64-libsndfile 1.0.26 2
m2w64-libsodium 1.0.10 2
m2w64-libtiff 4.0.6 2
m2w64-libvorbis 1.3.5 2
m2w64-libwinpthread-git 5.0.0.4634.697f757 2
m2w64-libxml2 2.9.3 4
m2w64-mpfr 3.1.4 4
m2w64-openblas 0.2.19 1
m2w64-pcre 8.38 2
m2w64-speex 1.2rc2 3
m2w64-speexdsp 1.2rc3 3
m2w64-tcl 8.6.5 3
m2w64-tk 8.6.5 3
m2w64-tktable 2.10 5
m2w64-wineditline 2.101 5
m2w64-xz 5.2.2 2
m2w64-zeromq 4.1.4 2
m2w64-zlib 1.2.8 10
markdown 3.1.1 py37_0
markupsafe 1.1.1 py37he774522_0
matplotlib 3.1.3 py37_0
matplotlib-base 3.1.3 py37h64f37c6_0
mccabe 0.6.1 py37_1
menuinst 1.4.16 py37he774522_0
mistune 0.8.4 py37he774522_0
mkl 2020.1 216
mkl-service 2.3.0 py37hb782905_0
mkl_fft 1.0.15 py37h14836fe_0
mkl_random 1.1.0 py37h675688f_0
mock 4.0.2 py_0
more-itertools 8.2.0 py_0
mpmath 1.1.0 py37_0
msgpack-python 1.0.0 py37h74a9793_1
msys2-conda-epoch 20160418 1
multipledispatch 0.6.0 py37_0
murmurhash 1.0.2 pypi_0 pypi
nbconvert 5.6.1 py37_0
nbformat 5.0.6 py_0
networkx 2.4 py_0
ninja 1.9.0 py37h74a9793_0
nltk 3.4.5 py37_0
nose 1.3.7 py37_2
notebook 6.0.3 py37_0
numba 0.49.1 py37h47e9c7a_0
numexpr 2.7.1 py37h25d0782_0
numpy 1.18.1 py37h93ca92e_0
numpy-base 1.18.1 py37hc3f5095_1
numpydoc 0.9.2 py_0
oauthlib 3.1.0 py_0
olefile 0.46 py37_0
openpyxl 3.0.3 py_0
openssl 1.1.1g he774522_0 conda-forge
opt_einsum 3.1.0 py_0
packaging 20.4 pypi_0 pypi
pandas 1.0.3 py37h47e9c7a_0
pandoc 2.2.3.2 0
pandocfilters 1.4.2 py37_1
paramiko 2.7.1 py_0
parsel 1.6.0 pypi_0 pypi
parso 0.5.2 py_0
partd 1.1.0 py_0
path 13.1.0 py37_0
path.py 12.4.0 0
pathlib2 2.3.5 py37_0
pathtools 0.1.2 py_1
patsy 0.5.1 py37_0
pep8 1.7.1 py37_0
pexpect 4.8.0 py37_0
pickleshare 0.7.5 py37_0
pillow 5.4.1 pypi_0 pypi
pip 20.0.2 py37_3
pkginfo 1.5.0.1 py37_0
plac 1.1.3 pypi_0 pypi
plotly 4.7.1 pypi_0 pypi
pluggy 0.13.1 py37_0
ply 3.11 py37_0
powershell_shortcut 0.0.1 3
preshed 3.0.2 pypi_0 pypi
prometheus_client 0.7.1 py_0
prompt-toolkit 3.0.4 py_0
prompt_toolkit 3.0.4 0
protobuf 3.11.4 py37h33f27b4_0
psutil 5.7.0 py37he774522_0
py 1.8.1 py_0
py-lief 0.10.1 py37ha925a31_0
pyasn1 0.4.8 py_0
pyasn1-modules 0.2.7 py_0
pycodestyle 2.5.0 py37_0
pycosat 0.6.3 py37he774522_0
pycparser 2.20 py_0
pycrypto 2.6.1 py37hfa6e2cd_9
pycurl 7.43.0.5 py37h7a1dbc1_0
pydantic 1.5.1 pypi_0 pypi
pydocstyle 4.0.1 py_0
pyflakes 2.1.1 py37_0
pygments 2.6.1 py_0
pyjwt 1.7.1 py37_0
pylint 2.4.4 py37_0
pynacl 1.3.0 py37h62dcd97_0
pyodbc 4.0.30 py37ha925a31_0
pyopenssl 19.1.0 py37_0
pyparsing 2.4.7 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
pyrsistent 0.16.0 py37he774522_0
pysocks 1.7.1 py37_0
pytables 3.6.1 py37h1da0976_0
pytest 5.4.2 py37_0
pytest-arraydiff 0.3 py37h39e3cac_0
pytest-astropy 0.8.0 py_0
pytest-astropy-header 0.1.2 py_0
pytest-doctestplus 0.5.0 py_0
pytest-openfiles 0.5.0 py_0
pytest-remotedata 0.3.2 py37_0
python 3.7.7 h81c818b_4
python-dateutil 2.8.1 py_0
python-jsonrpc-server 0.3.4 py_0
python-language-server 0.31.10 py37_0
python-libarchive-c 2.9 py_0
python_abi 3.7 1_cp37m conda-forge
pytorch 1.5.0 py3.7_cuda101_cudnn7_0 pytorch
pytorch-pretrained-bert 0.6.2 pypi_0 pypi
pytz 2020.1 py_0
pywavelets 1.1.1 py37he774522_0
pywin32 227 py37he774522_1
pywin32-ctypes 0.2.0 py37_1000
pywinpty 0.5.7 py37_0
pyyaml 5.3.1 py37he774522_0
pyzmq 18.1.1 py37ha925a31_0
qdarkstyle 2.8.1 py_0
qt 5.9.7 vc14h73c81de_0
qtawesome 0.7.0 py_0
qtconsole 4.7.4 py_0
qtpy 1.9.0 py_0
r-askpass 1.0 r36_0
r-assertthat 0.2.1 r36h6115d3f_0
r-backports 1.1.4 r36h6115d3f_0
r-base 3.6.1 hf18239d_1
r-base64enc 0.1_3 r36h6115d3f_4
r-bh 1.69.0_1 r36h6115d3f_0
r-boot 1.3_20 r36h6115d3f_0
r-broom 0.5.2 r36h6115d3f_0
r-callr 3.2.0 r36h6115d3f_0
r-caret 6.0_83 r36h6115d3f_0
r-cellranger 1.1.0 r36h6115d3f_0
r-class 7.3_15 r36h6115d3f_0
r-cli 1.1.0 r36h6115d3f_0
r-clipr 0.6.0 r36h6115d3f_0
r-cluster 2.0.8 r36h6115d3f_0
r-codetools 0.2_16 r36h6115d3f_0
r-colorspace 1.4_1 r36h6115d3f_0
r-crayon 1.3.4 r36h6115d3f_0
r-curl 3.3 r36h6115d3f_0
r-data.table 1.12.2 r36h6115d3f_0
r-dbi 1.0.0 r36h6115d3f_0
r-dbplyr 1.4.0 r36h6115d3f_0
r-dichromat 2.0_0 r36h6115d3f_4
r-digest 0.6.18 r36h6115d3f_0
r-dplyr 0.8.0.1 r36h6115d3f_0
r-ellipsis 0.1.0 r36h6115d3f_0
r-essentials 3.6.0 r36_0
r-evaluate 0.13 r36h6115d3f_0
r-fansi 0.4.0 r36h6115d3f_0
r-forcats 0.4.0 r36h6115d3f_0
r-foreach 1.4.4 r36h6115d3f_0
r-foreign 0.8_71 r36h6115d3f_0
r-formatr 1.6 r36h6115d3f_0
r-fs 1.2.7 r36h6115d3f_0
r-generics 0.0.2 r36h6115d3f_0
r-ggplot2 3.1.1 r36h6115d3f_0
r-glmnet 2.0_16 r36h6115d3f_0
r-glue 1.3.1 r36h6115d3f_0
r-gower 0.2.0 r36h6115d3f_0
r-gtable 0.3.0 r36h6115d3f_0
r-haven 2.1.0 r36h6115d3f_0
r-hexbin 1.27.2 r36h6115d3f_0
r-highr 0.8 r36h6115d3f_0
r-hms 0.4.2 r36h6115d3f_0
r-htmltools 0.3.6 r36h6115d3f_0
r-htmlwidgets 1.3 r36h6115d3f_0
r-httpuv 1.5.1 r36h6115d3f_0
r-httr 1.4.0 r36h6115d3f_0
r-ipred 0.9_8 r36h6115d3f_0
r-irdisplay 0.7.0 r36h6115d3f_0
r-irkernel 0.8.15 r36_0
r-iterators 1.0.10 r36h6115d3f_0
r-jsonlite 1.6 r36h6115d3f_0
r-kernsmooth 2.23_15 r36h6115d3f_4
r-knitr 1.22 r36h6115d3f_0
r-labeling 0.3 r36h6115d3f_4
r-later 0.8.0 r36h6115d3f_0
r-lattice 0.20_38 r36h6115d3f_0
r-lava 1.6.5 r36h6115d3f_0
r-lazyeval 0.2.2 r36h6115d3f_0
r-lubridate 1.7.4 r36h6115d3f_0
r-magrittr 1.5 r36h6115d3f_4
r-maps 3.3.0 r36h6115d3f_0
r-markdown 0.9 r36h6115d3f_0
r-mass 7.3_51.3 r36h6115d3f_0
r-matrix 1.2_17 r36h6115d3f_0
r-mgcv 1.8_28 r36h6115d3f_0
r-mime 0.6 r36h6115d3f_0
r-modelmetrics 1.2.2 r36h6115d3f_0
r-modelr 0.1.4 r36h6115d3f_0
r-munsell 0.5.0 r36h6115d3f_0
r-nlme 3.1_139 r36h6115d3f_0
r-nnet 7.3_12 r36h6115d3f_0
r-numderiv 2016.8_1 r36h6115d3f_0
r-openssl 1.3 r36h6115d3f_0
r-pbdzmq 0.3_3 r36h6115d3f_0
r-pillar 1.3.1 r36h6115d3f_0
r-pkgconfig 2.0.2 r36h6115d3f_0
r-plogr 0.2.0 r36h6115d3f_0
r-plyr 1.8.4 r36h6115d3f_0
r-prettyunits 1.0.2 r36h6115d3f_0
r-processx 3.3.0 r36h6115d3f_0
r-prodlim 2018.04.18 r36h6115d3f_0
r-progress 1.2.0 r36h6115d3f_0
r-promises 1.0.1 r36h6115d3f_0
r-ps 1.3.0 r36h6115d3f_0
r-purrr 0.3.2 r36h6115d3f_0
r-quantmod 0.4_14 r36h6115d3f_0
r-r6 2.4.0 r36h6115d3f_0
r-randomforest 4.6_14 r36h6115d3f_0
r-rbokeh 0.6.3 r36_0
r-rcolorbrewer 1.1_2 r36h6115d3f_0
r-rcpp 1.0.1 r36h6115d3f_0
r-rcpproll 0.3.0 r36h6115d3f_0
r-readr 1.3.1 r36h6115d3f_0
r-readxl 1.3.1 r36h6115d3f_0
r-recipes 0.1.5 r36h6115d3f_0
r-recommended 3.6.0 r36_0
r-rematch 1.0.1 r36h6115d3f_0
r-repr 0.19.2 r36h6115d3f_0
r-reprex 0.2.1 r36h6115d3f_0
r-reshape2 1.4.3 r36h6115d3f_0
r-rlang 0.3.4 r36h6115d3f_0
r-rmarkdown 1.12 r36h6115d3f_0
r-rpart 4.1_15 r36h6115d3f_0
r-rstudioapi 0.10 r36h6115d3f_0
r-rvest 0.3.3 r36h6115d3f_0
r-scales 1.0.0 r36h6115d3f_0
r-selectr 0.4_1 r36h6115d3f_0
r-shiny 1.3.2 r36h6115d3f_0
r-sourcetools 0.1.7 r36h6115d3f_0
r-spatial 7.3_11 r36h6115d3f_4
r-squarem 2017.10_1 r36h6115d3f_0
r-stringi 1.4.3 r36h6115d3f_0
r-stringr 1.4.0 r36h6115d3f_0
r-survival 2.44_1.1 r36h6115d3f_0
r-sys 3.2 r36h6115d3f_0
r-tibble 2.1.1 r36h6115d3f_0
r-tidyr 0.8.3 r36h6115d3f_0
r-tidyselect 0.2.5 r36h6115d3f_0
r-tidyverse 1.2.1 r36h6115d3f_0
r-timedate 3043.102 r36h6115d3f_0
r-tinytex 0.12 r36h6115d3f_0
r-ttr 0.23_4 r36h6115d3f_0
r-utf8 1.1.4 r36h6115d3f_0
r-uuid 0.1_2 r36h6115d3f_4
r-viridislite 0.3.0 r36h6115d3f_0
r-whisker 0.3_2 r36h6115d3f_4
r-withr 2.1.2 r36h6115d3f_0
r-xfun 0.6 r36h6115d3f_0
r-xml2 1.2.0 r36h6115d3f_0
r-xtable 1.8_4 r36h6115d3f_0
r-xts 0.11_2 r36h6115d3f_0
r-yaml 2.2.0 r36h6115d3f_0
r-zoo 1.8_5 r36h6115d3f_0
regex 2020.5.14 pypi_0 pypi
requests 2.23.0 py37_0
requests-oauthlib 1.3.0 py_0
retrying 1.3.3 pypi_0 pypi
rope 0.17.0 py_0
rsa 4.0 py_0
rtree 0.9.4 py37h21ff451_1
ruamel_yaml 0.15.87 py37he774522_0
s3transfer 0.3.3 pypi_0 pypi
sacremoses 0.0.43 pypi_0 pypi
salib 1.3.11 pypi_0 pypi
scikit-image 0.17.2 pypi_0 pypi
scikit-learn 0.22.1 py37h6288b17_0
scipy 1.4.1 py37h9439919_0
scrapbook 0.2.0 pypi_0 pypi
seaborn 0.10.1 py_0
send2trash 1.5.0 py37_0
sentencepiece 0.1.90 pypi_0 pypi
setuptools 46.4.0 py37_0
shap 0.29.3 pypi_0 pypi
simplegeneric 0.8.1 py37_2
singledispatch 3.4.0.3 py37_0
sip 4.19.8 py37h6538335_0
six 1.14.0 py37_0
snappy 1.1.7 h777316e_3
snowballstemmer 2.0.0 py_0
sortedcollections 1.1.2 py37_0
sortedcontainers 2.1.0 py37_0
soupsieve 2.0 py_0
spacy 2.2.4 pypi_0 pypi
sphinx 3.0.3 py_0
sphinxcontrib 1.0 py37_1
sphinxcontrib-applehelp 1.0.2 py_0
sphinxcontrib-devhelp 1.0.2 py_0
sphinxcontrib-htmlhelp 1.0.3 py_0
sphinxcontrib-jsmath 1.0.1 py_0
sphinxcontrib-qthelp 1.0.3 py_0
sphinxcontrib-serializinghtml 1.1.4 py_0
sphinxcontrib-websupport 1.2.1 py_0
spyder 4.1.3 py37_0
spyder-kernels 1.9.1 py37_0
sqlalchemy 1.3.16 py37he774522_0
sqlite 3.31.1 h2a8f88b_1
srsly 1.0.2 pypi_0 pypi
statsmodels 0.11.0 py37he774522_0
sympy 1.5.1 py37_0
tbb 2020.0 h74a9793_0
tblib 1.6.0 py_0
tensorboard 2.1.0 py3_0
tensorflow 2.1.0 gpu_py37h7db9008_0
tensorflow-base 2.1.0 gpu_py37h55f5790_0
tensorflow-estimator 2.1.0 pyhd54b08b_0
tensorflow-gpu 2.1.0 h0d30ee6_0
termcolor 1.1.0 py37_1
terminado 0.8.3 py37_0
testpath 0.4.4 py_0
thinc 7.4.0 pypi_0 pypi
tifffile 2020.5.11 pypi_0 pypi
tk 8.6.8 hfa6e2cd_0
tokenizers 0.0.11 pypi_0 pypi
toolz 0.10.0 py_0
torchvision 0.6.0 py37_cu101 pytorch
tornado 6.0.4 py37he774522_1
tqdm 4.46.0 py_0
traitlets 4.3.3 py37_0
transformers 2.4.1 pypi_0 pypi
treeinterpreter 0.2.2 pypi_0 pypi
typing_extensions 3.7.4.1 py37_0
ujson 1.35 py37hfa6e2cd_0
unicodecsv 0.14.1 py37_0
urllib3 1.25.8 py37_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_1
w3lib 1.22.0 pypi_0 pypi
wasabi 0.6.0 pypi_0 pypi
watchdog 0.10.2 py37_0
wcwidth 0.1.9 py_0
webencodings 0.5.1 py37_1
werkzeug 1.0.1 pypi_0 pypi
wheel 0.34.2 py37_0
widgetsnbextension 3.5.1 py37_0
win_inet_pton 1.1.0 py37_0
win_unicode_console 0.5 py37_0
wincertstore 0.2 py37_0
winpty 0.4.3 4
wrapt 1.12.1 py37he774522_1
xgboost 1.1.0 pypi_0 pypi
xlrd 1.2.0 py37_0
xlsxwriter 1.2.8 py_0
xlwings 0.19.0 py37_0
xlwt 1.3.0 py37_0
xz 5.2.5 h62dcd97_0
yaml 0.1.7 hc54c509_2
yapf 0.28.0 py_0
zeromq 4.3.1 h33f27b4_3
zict 2.0.0 py_0
zipp 3.1.0 py_0
zlib 1.2.11 h62dcd97_4
zstd 1.3.7 h508b16e_0
When running this line:
with Timer() as t:
classifier.fit(token_ids=tokens_train,
input_mask=mask_train,
labels=labels_train,
num_epochs=NUM_EPOCHS,
batch_size=BATCH_SIZE,
verbose=True)
print("[Training time: {:.3f} hrs]".format(t.interval / 3600))
I got the following stack trace:
t_total value of -1 results in schedule not being applied
Iteration: 0%| | 0/79 [00:00<?, ?it/s]
---------------------------------------------------------------------------
StopIteration Traceback (most recent call last)
<ipython-input-34-18e84990dbbe> in <module>
5 num_epochs=NUM_EPOCHS,
6 batch_size=BATCH_SIZE,
----> 7 verbose=True)
8
9 print("[Training time: {:.3f} hrs]".format(t.interval / 3600))
C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\interpret_text\experimental\common\utils_bert.py in fit(self, token_ids, input_mask, labels, token_type_ids, num_gpus, num_epochs, batch_size, lr, warmup_proportion, verbose)
550 token_type_ids=token_type_ids_batch,
551 attention_mask=mask_batch,
--> 552 labels=None,
553 )
554 loss = loss_func(y_h, y_batch).mean()
C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
548 result = self._slow_forward(*input, **kwargs)
549 else:
--> 550 result = self.forward(*input, **kwargs)
551 for hook in self._forward_hooks.values():
552 hook_result = hook(self, input, result)
C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\nn\parallel\data_parallel.py in forward(self, *inputs, **kwargs)
153 return self.module(*inputs[0], **kwargs[0])
154 replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
--> 155 outputs = self.parallel_apply(replicas, inputs, kwargs)
156 return self.gather(outputs, self.output_device)
157
C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\nn\parallel\data_parallel.py in parallel_apply(self, replicas, inputs, kwargs)
163
164 def parallel_apply(self, replicas, inputs, kwargs):
--> 165 return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
166
167 def gather(self, outputs, output_device):
C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\nn\parallel\parallel_apply.py in parallel_apply(modules, inputs, kwargs_tup, devices)
83 output = results[i]
84 if isinstance(output, ExceptionWrapper):
---> 85 output.reraise()
86 outputs.append(output)
87 return outputs
C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\_utils.py in reraise(self)
393 # (https://bugs.python.org/issue2651), so we work around it.
394 msg = KeyErrorMessage(msg)
--> 395 raise self.exc_type(msg)
StopIteration: Caught StopIteration in replica 0 on device 0.
Original Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\nn\parallel\parallel_apply.py", line 60, in _worker
output = module(*input, **kwargs)
File "C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\pytorch_pretrained_bert\modeling.py", line 989, in forward
_, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)
File "C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\torch\nn\modules\module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "C:\ProgramData\Anaconda3\envs\EmilioDL\lib\site-packages\pytorch_pretrained_bert\modeling.py", line 727, in forward
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
StopIteration
With one GPU the code runs flawlessly, but with 2 GPU's it doesn't run.
Please let me know if you need additional information.
Change the dashboard to use flask
Remove duplicate nlp-recipes dataset utils code
- Migrate to latest version of pytorch-pretrained-bert (tranformers)
- once nlp-recipes/utils_nlp can be pip installed : remove interpret_text/common/dataset
Should we support global explanation
ModuleNotFoundError: No module named 'interpret_text'
New to interpret text. Failed to run the first cell. Where can I get the module interpret_text?
TypeError: 'bool' object is not subscriptable
Greetings! I am facing the following issue while trying to set up the explainer:
TypeError: 'bool' object is not subscriptable
Here is my BERT model:
BertForSequenceClassification(
(bert): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(30522, 768, padding_idx=0)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(1): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(2): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(3): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(4): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(5): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(6): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(7): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(8): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(9): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(10): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(11): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
(dropout): Dropout(p=0.1, inplace=False)
(classifier): Linear(in_features=768, out_features=6, bias=True)
)
Here is the code:
interpreter_unified = UnifiedInformationExplainer(model=model,
train_dataset=df.loc[X_train]['Sentence'],
device=device,
target_layer=14,
classes=possible_labels)
text = df.loc[X_val]['Sentence'][90]
true_label = df.loc[X_val]['Category'][90]
predicted_label = list(label_dict.keys())[list(label_dict.values()).index(np.argmax(predictions[0]))]
print(text)
print(true_label, predicted_label)
And this is the line that fails:
explanation_unified = interpreter_unified.explain_local(text, true_label)
TypeError Traceback (most recent call last)
in
----> 1 explanation_unified = interpreter_unified.explain_local(text, true_label)
/anaconda/envs/azureml_py36/lib/python3.6/site-packages/interpret_text/experimental/unified_information.py in explain_local(self, X, y, name, num_iteration)
117 )
118 regularization = self._calculate_regularization(
--> 119 training_embeddings, self.model
120 ).tolist()
121 self.regular = nn.Parameter(
/anaconda/envs/azureml_py36/lib/python3.6/site-packages/interpret_text/experimental/unified_information.py in _calculate_regularization(self, sampled_x, model, reduced_axes)
169 x = x.to(self.device)
170
--> 171 s = self.Phi(x)
172 if reduced_axes is not None:
173 for axis in reduced_axes:
/anaconda/envs/azureml_py36/lib/python3.6/site-packages/interpret_text/experimental/unified_information.py in Phi(x)
272 classifier = self.model.classifier
273 return classifier(
--> 274 pooler(encoder(hidden_states, attention_mask, False)[-1])
275 )[0]
276 else:
/anaconda/envs/azureml_py36/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
/anaconda/envs/azureml_py36/lib/python3.6/site-packages/transformers/modeling_bert.py in forward(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states)
434 outputs = (hidden_states,)
435 if self.output_hidden_states:
--> 436 outputs = outputs + (all_hidden_states,)
437 if self.output_attentions:
438 outputs = outputs + (all_attentions,)
TypeError: 'bool' object is not subscriptable
Add dependency on nlp_utils from nlp-recipes
This issue will remove duplicated "utils_bert" and other model code that is copied over from utils_nlp.
Tasks:
- identify duplicate utils_nlp/models code
- add pip install nlp_recipes to setup
- remove duplicated utils related to bert model
[Classical Explainer] Importing Classical Text Explainer throws an OS Error
I set up the environment according to the steps outlined in the Readme and got the following error when I tried to run the Classical Text Explainer. I'm on a mac, perhaps that's causing the issue?
Link for example data is broken
The link for downloading data for the examples seems to be broken.
This link now redirects to Sam Bowman's homepage.
Add true_y as an optional paramater to explain_local
review the usage for all true_y for all explainers in explain_local()
Set up telemetry dashboard
Found conflicts! - when creating the conda environment
At the step which prepares the environment to include all the necessary packages and conda files (Python 3.8.5 and Anaconda 3), and specifically at this command:
conda env create -n interpret_cpu --file=interpret_cpu.yaml
it fails to solve the environment and returns with the following error:
Found conflicts! Looking for incompatible packages.
Then it looks for these packages, and then it throws a list of incompatibilities.
When last time I worked with this repo, I worked with Python 3.7, but now I couldn't make it work with that version either. I've attached the yaml file as .txt, and another file with the error message I received.
Thank you in advance!
Issue with text_classification_classical_text_explainer.ipynb
When executing the following cell:
classifier, best_params = explainer.fit(X_train, y_train)
It results with the following error:
ValueError: empty vocabulary; perhaps the documents only contain stop words
Using the same data set as in the example notebook - haven't changed anything in the code.
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