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AndreasMadsen
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Description

Create interactive textual heat maps for Jupiter notebooks

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textualheatmap

Create interactive textual heatmaps for Jupiter notebooks.

I originally published this visualization method in my distill paper https://distill.pub/2019/memorization-in-rnns/. In this context, it is used as a saliency map for showing which parts of a sentence are used to predict the next word. However, the visualization method is more general-purpose than that and can be used for any kind of textual heatmap purposes.

textualheatmap
works with python 3.6 or newer and is distributed under the MIT license.

Gif of saliency in RNN models

An end-to-end example of how to use the HuggingFace 🤗 Transformers python module to create a textual saliency map for how each masked token is predicted.

Open In Colab

Gif of saliency in BERT models

Install

pip install -U textualheatmap

API

Examples

Example of sequential-charecter model with metadata visible

Open In Colab

from textualheatmap import TextualHeatmap

data = [[ # GRU data {"token":" ", "meta":["the","one","of"], "heat":[1,0,0,0,0,0,0,0,0]}, {"token":"c", "meta":["can","called","century"], "heat":[1,0.22,0,0,0,0,0,0,0]}, {"token":"o", "meta":["country","could","company"], "heat":[0.57,0.059,1,0,0,0,0,0,0]}, {"token":"n", "meta":["control","considered","construction"], "heat":[1,0.20,0.11,0.84,0,0,0,0,0]}, {"token":"t", "meta":["control","continued","continental"], "heat":[0.27,0.17,0.052,0.44,1,0,0,0,0]}, {"token":"e", "meta":["context","content","contested"], "heat":[0.17,0.039,0.034,0.22,1,0.53,0,0,0]}, {"token":"x", "meta":["context","contexts","contemporary"], "heat":[0.17,0.0044,0.021,0.17,1,0.90,0.48,0,0]}, {"token":"t", "meta":["context","contexts","contentious"], "heat":[0.14,0.011,0.034,0.14,0.68,1,0.80,0.86,0]}, {"token":" ", "meta":["of","and","the"], "heat":[0.014,0.0063,0.0044,0.011,0.034,0.10,0.32,0.28,1]}, # ... ],[ # LSTM data # ... ]]

heatmap = TextualHeatmap( width = 600, show_meta = True, facet_titles = ['GRU', 'LSTM'] )

Set data and render plot, this can be called again to replace

the data.

heatmap.set_data(data)

Focus on the token with the given index. Especially useful when

interactive=False is used in TextualHeatmap.

heatmap.highlight(159)

Shows saliency with predicted words at metadata

Example of sequential-charecter model without metadata

Open In Colab

When

show_meta
is not
True
, the
meta
part of the
data
object has no effect.
heatmap = TextualHeatmap(
    facet_titles = ['LSTM', 'GRU'],
    rotate_facet_titles = True
)
heatmap.set_data(data)
heatmap.highlight(159)

Shows saliency without metadata

Example of non-sequential-word model

Open In Colab

format = True
can be set in the
data
object to inducate tokens that are not directly used by the model. This is useful if word or sub-word tokenization is used.
data = [[
{'token': '[CLR]',
 'meta': ['', '', ''],
 'heat': [1, 0, 0, 0, 0, ...]},
{'token': ' ',
 'format': True},
{'token': 'context',
 'meta': ['today', 'and', 'thus'],
 'heat': [0.13, 0.40, 0.23, 1.0, 0.56, ...]},
{'token': ' ',
 'format': True},
{'token': 'the',
 'meta': ['##ual', 'the', '##ually'],
 'heat': [0.11, 1.0, 0.34, 0.58, 0.59, ...]},
{'token': ' ',
 'format': True},
{'token': 'formal',
 'meta': ['formal', 'academic', 'systematic'],
 'heat': [0.13, 0.74, 0.26, 0.35, 1.0, ...]},
{'token': ' ',
 'format': True},
{'token': 'study',
 'meta': ['##ization', 'study', '##ity'],
 'heat': [0.09, 0.27, 0.19, 1.0, 0.26, ...]}
]]

heatmap = TextualHeatmap(facet_titles = ['BERT'], show_meta=True) heatmap.set_data(data)

Shows saliency in a BERT model, using sub-word tokenization

Citation

If you use this in a publication, please cite my Distill publication where I first demonstrated this visualization method.

@article{madsen2019visualizing,
  author = {Madsen, Andreas},
  title = {Visualizing memorization in RNNs},
  journal = {Distill},
  year = {2019},
  note = {https://distill.pub/2019/memorization-in-rnns},
  doi = {10.23915/distill.00016}
}

Sponsor

Sponsored by NearForm Research.

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