Machine Translation Explanations
This notebook demonstrates model explanations for a text to text scenario using a pretrained transformer model for machine translation. In this demo, we showcase explanations on two different models: English to Spanish (https://huggingface.co/Helsinki-NLP/opus-mt-en-es), and English to French (https://huggingface.co/Helsinki-NLP/opus-mt-en-fr).
[1]:
import numpy as np
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import shap
import torch
English to Spanish model
[2]:
# load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-es")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-es").cuda()
# define the input sentences we want to translate
data = [
"Transformers have rapidly become the model of choice for NLP problems, replacing older recurrent neural network models"
]
Explain the model’s predictions
[3]:
# we build an explainer by passing the model we want to explain and
# the tokenizer we want to use to break up the input strings
explainer = shap.Explainer(model, tokenizer)
# explainers are callable, just like models
shap_values = explainer(data, fixed_context=1)
floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)
Visualize shap explanations
[4]:
shap.plots.text(shap_values)
[0]
outputs
Los
transformador
es
se
han
convertido
rápidamente
en
el
modelo
de
elección
para
problemas
N
LP
,
reemplaza
ndo
modelos
de
red
neuro
nal
recurrente
s
más
antiguos
inputs
▁Transform
ers
▁have
▁rapidly
▁become
▁the
▁model
▁of
▁choice
▁for
▁N
LP
▁problems
,
▁replacing
▁older
▁recurrent
▁neural
▁network
▁models