Visualise attention for translation

Hi, first of all, thank you for a great tool.

My question is how to visualize attention for translation? I would like to see how much particular input word attended the choice of output word. I am using Seq2Seq model and its output has three types od attention values: encoder, decoder and cross attentions. https://huggingface.co/transformers/main_classes/output.html#seq2seqlmoutput

So to plot it, It would mean that words on the right side of head_view come from the translated sequence.

Is it how seq2seq attention can be visualised?

Thank you even for directing me in advance. Below is a code I used to get Seq2SeqLMOutput

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from bertviz import model_view

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-pl-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-pl-en")

text = "Kot nie przekroczył drogi bo była za szeroka"

inputs = tokenizer.encode(text, return_tensors="pt")
outputs = model.generate(inputs)

tokens = tokenizer.convert_ids_to_tokens(inputs[0])
output = model(inputs, decoder_input_ids=inputs, output_attentions=True)

[jessevig]

Encoder-decoder models now supported in Bertviz 1.1.0: https://github.com/jessevig/bertviz/blob/master/notebooks/head_view_encoder_decoder.ipynb

[jessevig]

You would just take the output of the generation and plug it into the aforementioned script. Because this is an autoregressive (left-to-right generting) model, this will show you the complete history of attention that would have been used to generate the output. Let me know if that doesn’t make sense.