Update README.md Zero Shot Code block (#137)

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Changing facebook/bart-large-mnli to cross-encoder/nli-deberta-base works for Zero Shot classification Interpret. @cdpierse

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README.md

@@ -480,18 +480,18 @@ This explainer allows for attributions to be calculated for zero shot classifica
 
 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 `facebook/bart-large-mnli` which is a checkpoint for a bart-large model trained on the
-[MNLI dataset](https://huggingface.co/datasets/multi_nli). 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.
+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("facebook/bart-large-mnli")
+tokenizer = AutoTokenizer.from_pretrained("cross-encoder/nli-deberta-base")
 
-model = AutoModelForSequenceClassification.from_pretrained("facebook/bart-large-mnli")
+model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/nli-deberta-base")
 
 
 zero_shot_explainer = ZeroShotClassificationExplainer(model, tokenizer)
@@ -508,75 +508,75 @@ Which will return the following dict of attribution tuple lists for each label:
 
 ```python
 >>> word_attributions
-{'finance': [('<s>', 0.0),
-  ('Today', 0.0),
-  ('apple', -0.016100065046282107),
-  ('released', 0.3348383988281792),
-  ('the', -0.8932952916127369),
-  ('new', 0.14207183688642497),
-  ('Mac', 0.016309545780430777),
-  ('book', -0.06956802041125129),
-  ('showing', -0.12661404114316252),
-  ('off', -0.11470154900720078),
-  ('a', -0.03299250484912159),
-  ('range', -0.002532332125100561),
-  ('of', -0.022451943898971004),
-  ('new', -0.01859870581213379),
-  ('features', -0.020774327263810944),
-  ('found', -0.007734346326330102),
-  ('in', 0.005100588658589585),
-  ('the', 0.04711084622588314),
-  ('proprietary', 0.046352064964644286),
-  ('silicon', -0.0033502000158946127),
-  ('chip', -0.010419324929115785),
-  ('computer', -0.11507972995022273),
-  ('.', 0.12237840300907425)],
- 'technology': [('<s>', 0.0),
-  ('Today', 0.0),
-  ('apple', 0.22505152647747717),
-  ('released', -0.16164146624851905),
-  ('the', 0.5026975657258089),
-  ('new', 0.052589263167955536),
-  ('Mac', 0.2528325960993759),
-  ('book', -0.06445090203729663),
-  ('showing', -0.21204922293777534),
-  ('off', 0.06319714817612732),
-  ('a', 0.032048012090796815),
-  ('range', 0.08553079346908955),
-  ('of', 0.1409201107994034),
-  ('new', 0.0515261917112576),
-  ('features', -0.09656406466213506),
-  ('found', 0.02336613296843605),
-  ('in', -0.0011649894272190678),
-  ('the', 0.14229640664777807),
-  ('proprietary', -0.23169065661847646),
-  ('silicon', 0.5963924257008087),
-  ('chip', -0.19908474233975806),
-  ('computer', 0.030620295844734646),
-  ('.', 0.1995076958535378)],
- 'sports': [('<s>', 0.0),
-  ('Today', 0.0),
-  ('apple', 0.1776618164760026),
-  ('released', 0.10067773539491479),
-  ('the', 0.4813466937627506),
-  ('new', -0.018555244191949295),
-  ('Mac', 0.016338241133536224),
-  ('book', 0.39311969562943677),
-  ('showing', 0.03579210145504227),
-  ('off', 0.0016710813632476176),
-  ('a', 0.04367940034297261),
-  ('range', 0.06076859006993011),
-  ('of', 0.11039711284328052),
-  ('new', 0.003932416031994724),
-  ('features', -0.009660883377622588),
-  ('found', -0.06507586539836184),
-  ('in', 0.2957812911667922),
-  ('the', 0.1584106228974514),
-  ('proprietary', 0.0005789280604917397),
-  ('silicon', -0.04693795680472678),
-  ('chip', -0.1699508539245465),
-  ('computer', -0.4290823663975582),
-  ('.', 0.469314992542427)]}
+{'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:
@@ -594,8 +594,8 @@ For the `ZeroShotClassificationExplainer` the visualize() method returns a table
 zero_shot_explainer.visualize("zero_shot.html")
 ```
 
-<a href="https://github.com/cdpierse/transformers-interpret/blob/master/images/zero_shot_example.png">
-<img src="https://github.com/cdpierse/transformers-interpret/blob/master/images/zero_shot_example.png" width="100%" height="100%" align="center" />
+<a href="https://github.com/cdpierse/transformers-interpret/blob/master/images/zero_shot_example2.png">
+<img src="https://github.com/cdpierse/transformers-interpret/blob/master/images/zero_shot_example2.png" width="100%" height="100%" align="center" />
 </a>
 
 </details>