From 7c2f938451b37459533bed2f637020bb06000a5e Mon Sep 17 00:00:00 2001 From: Owais Ahmad Date: Thu, 31 Aug 2023 04:22:58 +0530 Subject: [PATCH] Update README.md Zero Shot Code block (#137) * Update README.md Changing facebook/bart-large-mnli to cross-encoder/nli-deberta-base works for Zero Shot classification Interpret. @cdpierse * Add files via upload * Update README.md --- README.md | 150 +++++++++++++++++----------------- images/zero_shot_example2.png | Bin 0 -> 91125 bytes 2 files changed, 75 insertions(+), 75 deletions(-) create mode 100644 images/zero_shot_example2.png diff --git a/README.md b/README.md index 61a9eb1..8f34e33 100644 --- a/README.md +++ b/README.md @@ -480,8 +480,8 @@ 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. @@ -489,9 +489,9 @@ Notice that we pass our own custom labels `["finance", "technology", "sports"]` 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': [('', 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': [('', 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': [('', 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") ``` - - + + diff --git a/images/zero_shot_example2.png b/images/zero_shot_example2.png new file mode 100644 index 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