Explaining a Question Answering Transformers Model

Here we demonstrate how to explain the output of a question answering model that predicts which range of the context text contains the answer to a given question.

[1]:

import transformers
import shap
import torch

# load the model
pmodel = transformers.pipeline('question-answering')

# define two predictions, one that outputs the logits for the range start,
# and the other for the range end
def f(questions, start):
    outs = []
    for q in questions:
        question, context = q.split("[SEP]")
        d = pmodel.tokenizer(question, context)
        out = pmodel.model.forward(**{k: torch.tensor(d[k]).reshape(1, -1) for k in d})
        logits = out.start_logits if start else out.end_logits
        outs.append(logits.reshape(-1).detach().numpy())
    return outs
def f_start(questions):
    return f(questions, True)
def f_end(questions):
    return f(questions, False)

# attach a dynamic output_names property to the models so we can plot the tokens at each output position
def out_names(inputs):
    question, context = inputs.split("[SEP]")
    d = pmodel.tokenizer(question, context)
    return [pmodel.tokenizer.decode([id]) for id in d["input_ids"]]
f_start.output_names = out_names
f_end.output_names = out_names

Explain the starting positions

Here we explain the starting range predictions of the model. Note that because the model output depends on the length of the model input, is is important that we pass the model’s native tokenizer for masking, so that when we hide portions of the text we can retain the same number of tokens and hence the same meaning for each output position.

[2]:

data = ["What is on the table?[SEP]When I got home today I saw my cat on the table, and my frog on the floor."]

explainer_start = shap.Explainer(f_start, pmodel.tokenizer)
shap_values_start = explainer_start(data)

shap.plots.text(shap_values_start)

[0]

outputs

[CLS]

What

is

on

the

table

?

[SEP]

When

I

got

home

today

I

saw

my

cat

on

the

table

,

and

my

frog

on

the

floor

.

[SEP]

inputs

What

is

on

the

table

?

[SEP]

When I

got home

today I

saw my

cat on

the table

,

and

my frog

on the floor.