Explainability for any 🤗 Transformers models in 2 lines.
Transformers Interpret is a model explainability tool designed to work exclusively with the 🤗 [transformers][transformers] package.
In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files.
Check out the streamlit demo app here
Install
pip install transformers-interpretQuick Start
Text Explainers
Sequence Classification Explainer and Pairwise Sequence Classification
Let's start by initializing a transformers' model and tokenizer, and running it through the `SequenceClassificationExplainer`.
For this example we are using `distilbert-base-uncased-finetuned-sst-2-english`, a distilbert model finetuned on a sentiment analysis task.
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "distilbert-base-uncased-finetuned-sst-2-english"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# With both the model and tokenizer initialized we are now able to get explanations on an example text.
from transformers_interpret import SequenceClassificationExplainer
cls_explainer = SequenceClassificationExplainer(
model,
tokenizer)
word_attributions = cls_explainer("I love you, I like you")
```
Which will return the following list of tuples:
```python
>>> word_attributions
[('[CLS]', 0.0),
('i', 0.2778544699186709),
('love', 0.7792370723380415),
('you', 0.38560088858031094),
(',', -0.01769750505546915),
('i', 0.12071898121557832),
('like', 0.19091105304734457),
('you', 0.33994871536713467),
('[SEP]', 0.0)]
```
Positive attribution numbers indicate a word contributes positively towards the predicted class, while negative numbers indicate a word contributes negatively towards the predicted class. Here we can see that **I love you** gets the most attention.
You can use `predicted_class_index` in case you'd want to know what the predicted class actually is:
```python
>>> cls_explainer.predicted_class_index
array(1)
```
And if the model has label names for each class, we can see these too using `predicted_class_name`:
```python
>>> cls_explainer.predicted_class_name
'POSITIVE'
```
#### Visualize Classification attributions
Sometimes the numeric attributions can be difficult to read particularly in instances where there is a lot of text. To help with that we also provide the `visualize()` method that utilizes Captum's in built viz library to create a HTML file highlighting the attributions.
If you are in a notebook, calls to the `visualize()` method will display the visualization in-line. Alternatively you can pass a filepath in as an argument and an HTML file will be created, allowing you to view the explanation HTML in your browser.
```python
cls_explainer.visualize("distilbert_viz.html")
```
#### Explaining Attributions for Non Predicted Class
Attribution explanations are not limited to the predicted class. Let's test a more complex sentence that contains mixed sentiments.
In the example below we pass `class_name="NEGATIVE"` as an argument indicating we would like the attributions to be explained for the **NEGATIVE** class regardless of what the actual prediction is. Effectively because this is a binary classifier we are getting the inverse attributions.
```python
cls_explainer = SequenceClassificationExplainer(model, tokenizer)
attributions = cls_explainer("I love you, I like you, I also kinda dislike you", class_name="NEGATIVE")
```
In this case, `predicted_class_name` still returns a prediction of the **POSITIVE** class, because the model has generated the same prediction but nonetheless we are interested in looking at the attributions for the negative class regardless of the predicted result.
```python
>>> cls_explainer.predicted_class_name
'POSITIVE'
```
But when we visualize the attributions we can see that the words "**...kinda dislike**" are contributing to a prediction of the "NEGATIVE"
class.
```python
cls_explainer.visualize("distilbert_negative_attr.html")
```
Getting attributions for different classes is particularly insightful for multiclass problems as it allows you to inspect model predictions for a number of different classes and sanity-check that the model is "looking" at the right things.
For a detailed explanation of this example please checkout this [multiclass classification notebook.](notebooks/multiclass_classification_example.ipynb)
### Pairwise Sequence Classification
The `PairwiseSequenceClassificationExplainer` is a variant of the the `SequenceClassificationExplainer` that is designed to work with classification models that expect the input sequence to be two inputs separated by a models' separator token. Common examples of this are [NLI models](https://arxiv.org/abs/1705.02364) and [Cross-Encoders ](https://www.sbert.net/docs/pretrained_cross-encoders.html) which are commonly used to score two inputs similarity to one another.
This explainer calculates pairwise attributions for two passed inputs `text1` and `text2` using the model
and tokenizer given in the constructor.
Also, since a common use case for pairwise sequence classification is to compare two inputs similarity - models of this nature typically only have a single output node rather than multiple for each class. The pairwise sequence classification has some useful utility functions to make interpreting single node outputs clearer.
By default for models that output a single node the attributions are with respect to the inputs pushing the scores closer to 1.0, however if you want to see the
attributions with respect to scores closer to 0.0 you can pass `flip_sign=True`. For similarity
based models this is useful, as the model might predict a score closer to 0.0 for the two inputs
and in that case we would flip the attributions sign to explain why the two inputs are dissimilar.
Let's start by initializing a cross-encoder model and tokenizer from the suite of [pre-trained cross-encoders ](https://www.sbert.net/docs/pretrained_cross-encoders.html)provided by [sentence-transformers](https://github.com/UKPLab/sentence-transformers).
For this example we are using `"cross-encoder/ms-marco-MiniLM-L-6-v2"`, a high quality cross-encoder trained on the [MSMarco dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking) a passage ranking dataset for question answering and machine reading comprehension.
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers_interpret import PairwiseSequenceClassificationExplainer
model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/ms-marco-MiniLM-L-6-v2")
tokenizer = AutoTokenizer.from_pretrained("cross-encoder/ms-marco-MiniLM-L-6-v2")
pairwise_explainer = PairwiseSequenceClassificationExplainer(model, tokenizer)
# the pairwise explainer requires two string inputs to be passed, in this case given the nature of the model
# we pass a query string and a context string. The question we are asking of our model is "does this context contain a valid answer to our question"
# the higher the score the better the fit.
query = "How many people live in Berlin?"
context = "Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."
pairwise_attr = pairwise_explainer(query, context)
```
Which returns the following attributions:
```python
>>> pairwise_attr
[('[CLS]', 0.0),
('how', -0.037558652124213034),
('many', -0.40348581975409786),
('people', -0.29756140282349425),
('live', -0.48979015417391764),
('in', -0.17844527885888117),
('berlin', 0.3737346097442739),
('?', -0.2281428913480142),
('[SEP]', 0.0),
('berlin', 0.18282430604641564),
('has', 0.039114659489254834),
('a', 0.0820056652212297),
('population', 0.35712150914643026),
('of', 0.09680870840224687),
('3', 0.04791760029513795),
(',', 0.040330986539774266),
('520', 0.16307677913176166),
(',', -0.005919693904602767),
('03', 0.019431649515841844),
('##1', -0.0243808667024702),
('registered', 0.07748341753369632),
('inhabitants', 0.23904087299731255),
('in', 0.07553221327346359),
('an', 0.033112821611999875),
('area', -0.025378852244447532),
('of', 0.026526373859562906),
('89', 0.0030700151809002147),
('##1', -0.000410387092186983),
('.', -0.0193147139126114),
('82', 0.0073800833347678774),
('square', 0.028988305990861576),
('kilometers', 0.02071182933829008),
('.', -0.025901070914318036),
('[SEP]', 0.0)]
```
#### Visualize Pairwise Classification attributions
Visualizing the pairwise attributions is no different to the sequence classification explaine. We can see that in both the `query` and `context` there is a lot of positive attribution for the word `berlin` as well the words `population` and `inhabitants` in the `context`, good signs that our model understands the textual context of the question asked.
```python
pairwise_explainer.visualize("cross_encoder_attr.html")
```
If we were more interested in highlighting the input attributions that pushed the model away from the positive class of this single node output we could pass:
```python
pairwise_attr = explainer(query, context, flip_sign=True)
```
This simply inverts the sign of the attributions ensuring that they are with respect to the model outputting 0 rather than 1.
MultiLabel Classification Explainer
This explainer is an extension of the `SequenceClassificationExplainer` and is thus compatible with all sequence classification models from the Transformers package. The key change in this explainer is that it caclulates attributions for each label in the model's config and returns a dictionary of word attributions w.r.t to each label. The `visualize()` method also displays a table of attributions with attributions calculated per label.
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers_interpret import MultiLabelClassificationExplainer
model_name = "j-hartmann/emotion-english-distilroberta-base"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
cls_explainer = MultiLabelClassificationExplainer(model, tokenizer)
word_attributions = cls_explainer("There were many aspects of the film I liked, but it was frightening and gross in parts. My parents hated it.")
```
This produces a dictionary of word attributions mapping labels to a list of tuples for each word and it's attribution score.
Click to see word attribution dictionary
```python
>>> word_attributions
{'anger': [('', 0.0),
('There', 0.09002208622000409),
('were', -0.025129709879675187),
('many', -0.028852677974079328),
('aspects', -0.06341968013631565),
('of', -0.03587626320752477),
('the', -0.014813095892961287),
('film', -0.14087587475098232),
('I', 0.007367876912617766),
('liked', -0.09816592066307557),
(',', -0.014259517291745674),
('but', -0.08087144668471376),
('it', -0.10185214349220136),
('was', -0.07132244710777856),
('frightening', -0.4125361737439814),
('and', -0.021761663818889918),
('gross', -0.10423745223600908),
('in', -0.02383646952201854),
('parts', -0.027137622525091033),
('.', -0.02960415694062459),
('My', 0.05642774605113695),
('parents', 0.11146648216326158),
('hated', 0.8497975489280364),
('it', 0.05358116678115284),
('.', -0.013566277162080632),
('', 0.09293256725788422),
('', 0.0)],
'disgust': [('', 0.0),
('There', -0.035296263203072),
('were', -0.010224922196739717),
('many', -0.03747571761725605),
('aspects', 0.007696321643436715),
('of', 0.0026740873113235107),
('the', 0.0025752851265661335),
('film', -0.040890035285783645),
('I', -0.014710007408208579),
('liked', 0.025696806663391577),
(',', -0.00739107098314569),
('but', 0.007353791868893654),
('it', -0.00821368234753605),
('was', 0.005439709067819798),
('frightening', -0.8135974168445725),
('and', -0.002334953123414774),
('gross', 0.2366024374426269),
('in', 0.04314772995234148),
('parts', 0.05590472194035334),
('.', -0.04362554293972562),
('My', -0.04252694977895808),
('parents', 0.051580790911406944),
('hated', 0.5067406070057585),
('it', 0.0527491071885104),
('.', -0.008280280618652273),
('', 0.07412384603053103),
('', 0.0)],
'fear': [('', 0.0),
('There', -0.019615758046045408),
('were', 0.008033402634196246),
('many', 0.027772367717635423),
('aspects', 0.01334130725685673),
('of', 0.009186049991879768),
('the', 0.005828877177384549),
('film', 0.09882910753644959),
('I', 0.01753565003544039),
('liked', 0.02062597344466885),
(',', -0.004469530636560965),
('but', -0.019660439408176984),
('it', 0.0488084071292538),
('was', 0.03830859527501167),
('frightening', 0.9526443954511705),
('and', 0.02535156284103706),
('gross', -0.10635301961551227),
('in', -0.019190425328209065),
('parts', -0.01713006453323631),
('.', 0.015043169035757302),
('My', 0.017068079071414916),
('parents', -0.0630781275517486),
('hated', -0.23630028921273583),
('it', -0.056057044429020306),
('.', 0.0015102052077844612),
('', -0.010045048665404609),
('', 0.0)],
'joy': [('', 0.0),
('There', 0.04881772670614576),
('were', -0.0379316152427468),
('many', -0.007955371089444285),
('aspects', 0.04437296429416574),
('of', -0.06407011137335743),
('the', -0.07331568926973099),
('film', 0.21588462483311055),
('I', 0.04885724513463952),
('liked', 0.5309510543276107),
(',', 0.1339765195225006),
('but', 0.09394079060730279),
('it', -0.1462792330432028),
('was', -0.1358591558323458),
('frightening', -0.22184169339341142),
('and', -0.07504142930419291),
('gross', -0.005472075984252812),
('in', -0.0942152657437379),
('parts', -0.19345218754215965),
('.', 0.11096247277185402),
('My', 0.06604512262645984),
('parents', 0.026376541098236207),
('hated', -0.4988319510231699),
('it', -0.17532499366236615),
('.', -0.022609976138939034),
('', -0.43417114685294833),
('', 0.0)],
'neutral': [('', 0.0),
('There', 0.045984598036642205),
('were', 0.017142566357474697),
('many', 0.011419348619472542),
('aspects', 0.02558593440287365),
('of', 0.0186162232003498),
('the', 0.015616416841815963),
('film', -0.021190511300570092),
('I', -0.03572427925026324),
('liked', 0.027062554960050455),
(',', 0.02089914209290366),
('but', 0.025872618597570115),
('it', -0.002980407262316265),
('was', -0.022218157611174086),
('frightening', -0.2982516449116045),
('and', -0.01604643529040792),
('gross', -0.04573829263548096),
('in', -0.006511536166676108),
('parts', -0.011744224307968652),
('.', -0.01817041167875332),
('My', -0.07362312722231429),
('parents', -0.06910711601816408),
('hated', -0.9418903509267312),
('it', 0.022201795222373488),
('.', 0.025694319747309045),
('', 0.04276690822325994),
('', 0.0)],
'sadness': [('', 0.0),
('There', 0.028237893283377526),
('were', -0.04489910545229568),
('many', 0.004996044977269471),
('aspects', -0.1231292680125582),
('of', -0.04552690725956671),
('the', -0.022077819961347042),
('film', -0.14155752357877663),
('I', 0.04135347872193571),
('liked', -0.3097732540526099),
(',', 0.045114660009053134),
('but', 0.0963352125332619),
('it', -0.08120617610094617),
('was', -0.08516150809170213),
('frightening', -0.10386889639962761),
('and', -0.03931986389970189),
('gross', -0.2145059013625132),
('in', -0.03465423285571697),
('parts', -0.08676627134611635),
('.', 0.19025217371906333),
('My', 0.2582092561303794),
('parents', 0.15432351476960307),
('hated', 0.7262186310977987),
('it', -0.029160655114499095),
('.', -0.002758524253450406),
('', -0.33846410359182094),
('', 0.0)],
'surprise': [('', 0.0),
('There', 0.07196110795254315),
('were', 0.1434314520711312),
('many', 0.08812238369489701),
('aspects', 0.013432396769890982),
('of', -0.07127508805657243),
('the', -0.14079766624810955),
('film', -0.16881201614906485),
('I', 0.040595668935112135),
('liked', 0.03239855530171577),
(',', -0.17676382558158257),
('but', -0.03797939330341559),
('it', -0.029191325089641736),
('was', 0.01758013584108571),
('frightening', -0.221738963726823),
('and', -0.05126920277135527),
('gross', -0.33986913466614044),
('in', -0.018180366628697),
('parts', 0.02939418603252064),
('.', 0.018080129971003226),
('My', -0.08060162218059498),
('parents', 0.04351719139081836),
('hated', -0.6919028585285265),
('it', 0.0009574844165327357),
('.', -0.059473118237873344),
('', -0.465690452620123),
('', 0.0)]}
```
Zero Shot Classification Explainer
_Models using this explainer must be previously trained on NLI classification downstream tasks and have a label in the model's config called either "entailment" or "ENTAILMENT"._ This explainer allows for attributions to be calculated for zero shot classification like models. In order to achieve this we use the same methodology employed by Hugging face. For those not familiar method employed by Hugging Face to achieve zero shot classification the way this works is by exploiting the "entailment" label of NLI models. Here is a [link](https://arxiv.org/abs/1909.00161) to a paper explaining more about it. A list of NLI models guaranteed to be compatible with this explainer can be found on the [model hub](https://huggingface.co/models?filter=pytorch&pipeline_tag=zero-shot-classification). Let's start by initializing a transformers' sequence classification model and tokenizer trained specifically on a NLI task, and passing it to the ZeroShotClassificationExplainer. For this example we are using `cross-encoder/nli-deberta-base` which is a checkpoint for a deberta-base model trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [NLI dataset](https://huggingface.co/datasets/multi_nli) Datasets. This model typically predicts whether a sentence pair are an entailment, neutral, or a contradiction, however for zero-shot we only look the entailment label. Notice that we pass our own custom labels `["finance", "technology", "sports"]` to the class instance. Any number of labels can be passed including as little as one. Whichever label scores highest for entailment can be accessed via `predicted_label`, however the attributions themselves are calculated for every label. If you want to see the attributions for a particular label it is recommended just to pass in that one label and then the attributions will be guaranteed to be calculated w.r.t. that label. ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer from transformers_interpret import ZeroShotClassificationExplainer tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-base") model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-base") zero_shot_explainer = ZeroShotClassificationExplainer(model, tokenizer) word_attributions = zero_shot_explainer( "Today apple released the new Macbook showing off a range of new features found in the proprietary silicon chip computer. ", labels = ["finance", "technology", "sports"], ) ``` Which will return the following dict of attribution tuple lists for each label: ```python >>> word_attributions {'finance': [('[CLS]', 0.0), ('Today', 0.144761198095125), ('apple', 0.05008283286211926), ('released', -0.29790757134109724), ('the', -0.09931162582050683), ('new', -0.151252730475885), ('Mac', 0.19431968978659608), ('book', 0.059431761386793486), ('showing', -0.30754747734942633), ('off', 0.0329034397830471), ('a', 0.04198035048519715), ('range', -0.00413947940202566), ('of', 0.7135069733740484), ('new', 0.2294990755900286), ('features', -0.1523457769188503), ('found', -0.016804346228170633), ('in', 0.1185751939327566), ('the', -0.06990875734316043), ('proprietary', 0.16339657649559983), ('silicon', 0.20461302470245252), ('chip', 0.033304742383885574), ('computer', -0.058821677910955064), ('.', -0.19741292299059068)], 'technology': [('[CLS]', 0.0), ('Today', 0.1261355373492264), ('apple', -0.06735584800073911), ('released', -0.37758515332894504), ('the', -0.16300368060788886), ('new', -0.1698884472100767), ('Mac', 0.41505959302727347), ('book', 0.321276307285395), ('showing', -0.2765988420377037), ('off', 0.19388699112601515), ('a', -0.044676708673846766), ('range', 0.05333370699507288), ('of', 0.3654053610507722), ('new', 0.3143976769670845), ('features', 0.2108588137592185), ('found', 0.004676960337191403), ('in', 0.008026783104605233), ('the', -0.09961358108721637), ('proprietary', 0.18816708356062326), ('silicon', 0.13322691438800874), ('chip', 0.015141805082331294), ('computer', -0.1321895049108681), ('.', -0.17152401596638975)], 'sports': [('[CLS]', 0.0), ('Today', 0.11751821789941418), ('apple', -0.024552367058659215), ('released', -0.44706064525430567), ('the', -0.10163968191086448), ('new', -0.18590036257614642), ('Mac', 0.0021649499897370725), ('book', 0.009141161101058446), ('showing', -0.3073791152936541), ('off', 0.0711051596941137), ('a', 0.04153236257439005), ('range', 0.01598478741712663), ('of', 0.6632118834641558), ('new', 0.2684728052423898), ('features', -0.10249856013919137), ('found', -0.032459999377294144), ('in', 0.11078761617308391), ('the', -0.020530085754695244), ('proprietary', 0.17968209761431955), ('silicon', 0.19997909769476027), ('chip', 0.04447720580439545), ('computer', 0.018515748463790047), ('.', -0.1686603393466192)]} ``` We can find out which label was predicted with: ```python >>> zero_shot_explainer.predicted_label 'technology' ``` #### Visualize Zero Shot Classification attributions For the `ZeroShotClassificationExplainer` the visualize() method returns a table similar to the `SequenceClassificationExplainer` but with attributions for every label. ```python zero_shot_explainer.visualize("zero_shot.html") ```
Question Answering Explainer
Let's start by initializing a transformers' Question Answering model and tokenizer, and running it through the `QuestionAnsweringExplainer`. For this example we are using `bert-large-uncased-whole-word-masking-finetuned-squad`, a bert model finetuned on a SQuAD. ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer from transformers_interpret import QuestionAnsweringExplainer tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad") qa_explainer = QuestionAnsweringExplainer( model, tokenizer, ) context = """ In Artificial Intelligence and machine learning, Natural Language Processing relates to the usage of machines to process and understand human language. Many researchers currently work in this space. """ word_attributions = qa_explainer( "What is natural language processing ?", context, ) ``` Which will return the following dict containing word attributions for both the predicted start and end positions for the answer. ```python >>> word_attributions {'start': [('[CLS]', 0.0), ('what', 0.9177170660377296), ('is', 0.13382234898765258), ('natural', 0.08061747350142005), ('language', 0.013138062762511409), ('processing', 0.11135923869816286), ('?', 0.00858057388924361), ('[SEP]', -0.09646373141894966), ('in', 0.01545633993975799), ('artificial', 0.0472082598707737), ('intelligence', 0.026687249355110867), ('and', 0.01675371260058537), ('machine', -0.08429502436554961), ('learning', 0.0044827685126163355), (',', -0.02401013152520878), ('natural', -0.0016756080249823537), ('language', 0.0026815068421401885), ('processing', 0.06773157580722854), ('relates', 0.03884601576992908), ('to', 0.009783797821526368), ('the', -0.026650922910540952), ('usage', -0.010675019721821147), ('of', 0.015346787885898537), ('machines', -0.08278008270160107), ('to', 0.12861387892768839), ('process', 0.19540146386642743), ('and', 0.009942879959615826), ('understand', 0.006836894853320319), ('human', 0.05020451122579102), ('language', -0.012980795199301), ('.', 0.00804358248127772), ('many', 0.02259009321498161), ('researchers', -0.02351650942555469), ('currently', 0.04484573078852946), ('work', 0.00990399948294476), ('in', 0.01806961211334615), ('this', 0.13075899776164499), ('space', 0.004298315347838973), ('.', -0.003767904539347979), ('[SEP]', -0.08891544093454595)], 'end': [('[CLS]', 0.0), ('what', 0.8227231947501547), ('is', 0.0586864942952253), ('natural', 0.0938903563379123), ('language', 0.058596976016400674), ('processing', 0.1632374290269829), ('?', 0.09695686057123237), ('[SEP]', -0.11644447033554006), ('in', -0.03769172371919206), ('artificial', 0.06736158404049886), ('intelligence', 0.02496399001288386), ('and', -0.03526028847762427), ('machine', -0.20846431491771975), ('learning', 0.00904892847529654), (',', -0.02949905488474854), ('natural', 0.011024507784743872), ('language', 0.0870741751282507), ('processing', 0.11482449622317169), ('relates', 0.05008962090922852), ('to', 0.04079118393166258), ('the', -0.005069048880616451), ('usage', -0.011992752445836278), ('of', 0.01715183316135495), ('machines', -0.29823535624026265), ('to', -0.0043760160855057925), ('process', 0.10503217484645223), ('and', 0.06840313586976698), ('understand', 0.057184000619403944), ('human', 0.0976805947708315), ('language', 0.07031163646606695), ('.', 0.10494566513897102), ('many', 0.019227154676079487), ('researchers', -0.038173913797800885), ('currently', 0.03916641120002003), ('work', 0.03705371672439422), ('in', -0.0003155975107591203), ('this', 0.17254932354022232), ('space', 0.0014311439625599323), ('.', 0.060637932829867736), ('[SEP]', -0.09186286505530596)]} ``` We can get the text span for the predicted answer with: ```python >>> qa_explainer.predicted_answer 'usage of machines to process and understand human language' ``` #### Visualize Question Answering attributions For the `QuestionAnsweringExplainer` the visualize() method returns a table with two rows. The first row represents the attributions for the answers' start position and the second row represents the attributions for the answers' end position. ```python qa_explainer.visualize("bert_qa_viz.html") ```
Token Classification (NER) explainer
_This is currently an experimental explainer under active development and is not yet fully tested. The explainers' API is subject to change as are the attribution methods, if you find any bugs please let me know._ Let's start by initializing a transformers' Token Classfication model and tokenizer, and running it through the `TokenClassificationExplainer`. For this example we are using `dslim/bert-base-NER`, a bert model finetuned on the CoNLL-2003 Named Entity Recognition dataset. ```python from transformers import AutoModelForTokenClassification, AutoTokenizer from transformers_interpret import TokenClassificationExplainer model = AutoModelForTokenClassification.from_pretrained('dslim/bert-base-NER') tokenizer = AutoTokenizer.from_pretrained('dslim/bert-base-NER') ner_explainer = TokenClassificationExplainer( model, tokenizer, ) sample_text = "We visited Paris last weekend, where Emmanuel Macron lives." word_attributions = ner_explainer(sample_text, ignored_labels=['O']) ``` In order to reduce the number of attributions that are calculated, we tell the explainer to ignore the tokens that whose predicted label is `'O'`. We could also tell the explainer to ignore certain indexes providing a list as argument of the parameter `ignored_indexes`. Which will return the following dict of including the predicted label and the attributions for each of token, except those which were predicted as 'O': ```python >>> word_attributions {'paris': {'label': 'B-LOC', 'attribution_scores': [('[CLS]', 0.0), ('we', -0.014352325471387907), ('visited', 0.32915222186559123), ('paris', 0.9086791784795596), ('last', 0.15181203147624034), ('weekend', 0.14400210630677038), (',', 0.01899744327012935), ('where', -0.039402005463239465), ('emmanuel', 0.061095284002642025), ('macro', 0.004192922551105228), ('##n', 0.09446355513057757), ('lives', -0.028724312616455003), ('.', 0.08099007392937585), ('[SEP]', 0.0)]}, 'emmanuel': {'label': 'B-PER', 'attribution_scores': [('[CLS]', 0.0), ('we', -0.006933030636686712), ('visited', 0.10396962390436904), ('paris', 0.14540758744233165), ('last', 0.08024018944451371), ('weekend', 0.10687970996804418), (',', 0.1793198466387937), ('where', 0.3436407835483767), ('emmanuel', 0.8774892642652167), ('macro', 0.03559399361048316), ('##n', 0.1516315604785551), ('lives', 0.07056441327498127), ('.', -0.025820924624605487), ('[SEP]', 0.0)]}, 'macro': {'label': 'I-PER', 'attribution_scores': [('[CLS]', 0.0), ('we', 0.05578067326280157), ('visited', 0.00857021283406586), ('paris', 0.16559056506114297), ('last', 0.08285256685903823), ('weekend', 0.10468727443796395), (',', 0.09949509071515888), ('where', 0.3642458274356929), ('emmanuel', 0.7449335213978788), ('macro', 0.3794625659183485), ('##n', -0.2599031433800762), ('lives', 0.20563450682196147), ('.', -0.015607017319486929), ('[SEP]', 0.0)]}, '##n': {'label': 'I-PER', 'attribution_scores': [('[CLS]', 0.0), ('we', 0.025194121717285252), ('visited', -0.007415022865239864), ('paris', 0.09478357303107598), ('last', 0.06927939834474463), ('weekend', 0.0672008033510708), (',', 0.08316907214363504), ('where', 0.3784915854680165), ('emmanuel', 0.7729352621546081), ('macro', 0.4148652759139777), ('##n', -0.20853534512145033), ('lives', 0.09445057087678274), ('.', -0.094274985907366), ('[SEP]', 0.0)]}, '[SEP]': {'label': 'B-LOC', 'attribution_scores': [('[CLS]', 0.0), ('we', -0.3694351403796742), ('visited', 0.1699038407402483), ('paris', 0.5461587414992369), ('last', 0.0037948102770307517), ('weekend', 0.1628100955702496), (',', 0.4513093410909263), ('where', -0.09577409464161038), ('emmanuel', 0.48499459835388914), ('macro', -0.13528905587653023), ('##n', 0.14362969934754344), ('lives', -0.05758007024257254), ('.', -0.13970977266152554), ('[SEP]', 0.0)]}} ``` #### Visualize NER attributions For the `TokenClassificationExplainer` the visualize() method returns a table with as many rows as tokens. ```python ner_explainer.visualize("bert_ner_viz.html") ```
For more details about how the `TokenClassificationExplainer` works, you can check the notebook [notebooks/ner_example.ipynb](notebooks/ner_example.ipynb).
Vision Explainers
Image Classification Explainer
The `ImageClassificationExplainer` is designed to work with all models from the Transformers library that are trained for image classification (Swin, ViT etc). It provides attributions for every pixel in that image that can be easily visualized using the explainers built in `visualize` method.
Initialising an image classification is very simple, all you need a is a image classification model finetuned or trained to work with Huggingface and its feature extractor.
For this example we are using `google/vit-base-patch16-224`, a Vision Transformer (ViT) model pre-trained on ImageNet-21k that predicts from 1000 possible classes.
```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
from transformers_interpret import ImageClassificationExplainer
from PIL import Image
import requests
model_name = "google/vit-base-patch16-224"
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
# With both the model and feature extractor initialized we are now able to get explanations on an image, we will use a simple image of a golden retriever.
image_link = "https://imagesvc.meredithcorp.io/v3/mm/image?url=https%3A%2F%2Fstatic.onecms.io%2Fwp-content%2Fuploads%2Fsites%2F47%2F2020%2F08%2F16%2Fgolden-retriever-177213599-2000.jpg"
image = Image.open(requests.get(image_link, stream=True).raw)
image_classification_explainer = ImageClassificationExplainer(model=model, feature_extractor=feature_extractor)
image_attributions = image_classification_explainer(
image
)
print(image_attributions.shape)
```
Which will return the following list of tuples:
```python
>>> torch.Size([1, 3, 224, 224])
```
#### Visualizing Image Attributions
Because we are dealing with images visualization is even more straightforward than in text models.
Attrbutions can be easily visualized using the `visualize` method of the explainer. There are currently 4 supported visualization methods.
- `heatmap` - a heatmap of positive and negative attributions is drawn in using the dimensions of the image.
- `overlay` - the heatmap is overlayed over a grayscaled version of the original image
- `masked_image` - the absolute value of attrbutions is used to create a mask over original image
- `alpha_scaling` - Sets alpha channel (transparency) of each pixel to be equal to normalized attribution value.
#### Heatmap
```python
image_classification_explainer.visualize(
method="heatmap",
side_by_side=True,
outlier_threshold=0.03
)
```
#### Overlay
```python
image_classification_explainer.visualize(
method="overlay",
side_by_side=True,
outlier_threshold=0.03
)
```
#### Masked Image
```python
image_classification_explainer.visualize(
method="masked_image",
side_by_side=True,
outlier_threshold=0.03
)
```
#### Alpha Scaling
```python
image_classification_explainer.visualize(
method="alpha_scaling",
side_by_side=True,
outlier_threshold=0.03
)
```
Future Development
This package is still in active development and there is much more planned. For a 1.0.0 release we're aiming to have:
- Clean and thorough documentation website
Support for Question Answering modelsSupport for NER modelsSupport for Zero Shot Classification models.Ability to show attributions for multiple embedding type, rather than just the word embeddings.- Support for SentenceTransformer embedding models and other image embeddings
- Additional attribution methods
Support for vision transformer models- In depth examples
A nice logo(thanks @Voyz)- and more... feel free to submit your suggestions!
Contributing
If you would like to make a contribution please checkout our contribution guidelines
Questions / Get In Touch
The maintainer of this repository is @cdpierse.
If you have any questions, suggestions, or would like to make a contribution (please do 😁), feel free to get in touch at charlespierse@gmail.com
I'd also highly suggest checking out Captum if you find model explainability and interpretability interesting.
This package stands on the shoulders of the the incredible work being done by the teams at Pytorch Captum and Hugging Face and would not exist if not for the amazing job they are both doing in the fields of ML and model interpretability respectively.
Reading and Resources
Captum
All of the attributions within this package are calculated using PyTorch's explainability package [Captum](https://captum.ai/). See below for some useful links related to Captum. - [Captum Algorithm Overview](https://captum.ai/docs/algorithms) - [Bert QA Example](https://captum.ai/tutorials/Bert_SQUAD_Interpret) this an implementation acheived purely using Captum. - [API Reference](https://captum.ai/api/) - [Model Interpretability with Captum - Narine Kokhilkyan (Video)](https://www.youtube.com/watch?v=iVSIFm0UN9I)
Attribution
Integrated Gradients (IG) and a variation of it Layer Integrated Gradients (LIG) are the core attribution methods on which Transformers Interpret is currently built. Below are some useful resources including the original paper and some video links explaining the inner mechanics. If you are curious about what is going on inside of Transformers Interpret I highly recommend checking out at least one of these resources. - [Axiomatic Attributions for Deep Networks](https://arxiv.org/abs/1703.01365) the original paper [2017] where Integrated Gradients was specified. - [Fiddler AI YouTube video on IG](https://www.youtube.com/watch?v=9AaDc35JYiI) - [Henry AI Labs YouTube Primer on IG](https://www.youtube.com/watch?v=MB8KYX5UzKw) - [Explaining Explanations: Axiomatic Feature Interactions for Deep Networks](http://export.arxiv.org/abs/2002.04138) more recent paper [2020] extending the work of the original paper.