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https://github.com/jessevig/bertviz

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
https://github.com/jessevig/bertviz

bert gpt2 machine-learning natural-language-processing neural-network nlp pytorch roberta transformer transformers visualization

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BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

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README 

        


    BertViz





 Visualize Attention in NLP Models





    Quick Tour •

    Getting Started •

    Colab Tutorial •

    Paper




BertViz is an interactive tool for visualizing attention in [Transformer](https://jalammar.github.io/illustrated-transformer/) language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab

 notebook through a simple Python API that supports most [Huggingface models](https://huggingface.co/models). BertViz extends the

   [Tensor2Tensor visualization tool](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/visualization)

    by [Llion Jones](https://medium.com/@llionj), providing multiple views that each offer a unique lens into the attention mechanism.



Get updates for this and related projects on [Twitter

![Twitter logo](images/twitter.svg)](https://twitter.com/jesse_vig).



## 🚀 Quick Tour



### Head View

The *head view* visualizes attention for one or more attention heads in the same 

 layer. It is based on the excellent [Tensor2Tensor visualization tool](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/visualization) by [Llion Jones](https://medium.com/@llionj). 



🕹 Try out the head view in the [Interactive Colab Tutorial](https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ?usp=sharing) (all visualizations pre-loaded).



    




### Model View 



The *model view* shows a bird's-eye view of attention across all layers and heads.



🕹 Try out the model view in the [Interactive Colab Tutorial](https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ?usp=sharing) (all visualizations pre-loaded).



![model view](images/model-view-noscroll.gif)



### Neuron View 

The *neuron view* visualizes individual neurons in the query and key vectors and shows how they are used to compute attention.



🕹 Try out the neuron view in the [Interactive Colab Tutorial](https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ?usp=sharing) (all visualizations pre-loaded).



![neuron view](images/neuron-view-dark.gif)



## ⚡️ Getting Started



### Running BertViz in a Jupyter Notebook



From the command line:



```bash

pip install bertviz

```

You must also have Jupyter Notebook and ipywidgets installed:



```bash

pip install jupyterlab

pip install ipywidgets

```

(If you run into any issues installing Jupyter or ipywidgets, consult the documentation [here](https://jupyter.org/install) and [here](https://ipywidgets.readthedocs.io/en/stable/user_install.html).)



To create a new Jupyter notebook, simply run:



```bash

jupyter notebook

```



Then click `New` and select `Python 3 (ipykernel)` if prompted.



### Running BertViz in Colab



To run in [Colab](https://colab.research.google.com/), simply add the following cell at the beginning of your Colab notebook:



```

!pip install bertviz

```



### Sample code

Run the following code to load the `xtremedistil-l12-h384-uncased` model and display it in the model view: 



```python

from transformers import AutoTokenizer, AutoModel, utils

from bertviz import model_view

utils.logging.set_verbosity_error()  # Suppress standard warnings



model_name = "microsoft/xtremedistil-l12-h384-uncased"  # Find popular HuggingFace models here: https://huggingface.co/models

input_text = "The cat sat on the mat"  

model = AutoModel.from_pretrained(model_name, output_attentions=True)  # Configure model to return attention values

tokenizer = AutoTokenizer.from_pretrained(model_name)

inputs = tokenizer.encode(input_text, return_tensors='pt')  # Tokenize input text

outputs = model(inputs)  # Run model

attention = outputs[-1]  # Retrieve attention from model outputs

tokens = tokenizer.convert_ids_to_tokens(inputs[0])  # Convert input ids to token strings

model_view(attention, tokens)  # Display model view

```



The visualization may take a few seconds to load. Feel free to experiment with different input texts and

 [models](https://huggingface.co/models). 

See [Documentation](#-documentation) for additional use cases and examples, e.g., encoder-decoder models.



#### Running sample notebooks



You may also run any of the sample [notebooks](notebooks/) included with BertViz:



```bash

git clone --depth 1 [email protected]:jessevig/bertviz.git

cd bertviz/notebooks

jupyter notebook

```

## 🕹 Interactive Tutorial



Check out the [Interactive Colab Tutorial](https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ?usp=sharing) 

to learn more about BertViz and try out the tool. Note: all visualizations are pre-loaded, so there is no need to execute any cells.



[![Tutorial](images/tutorial-screenshots.jpg)](https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ?usp=sharing)



## 📖 Documentation



### Table of contents



- [Self-attention models (BERT, GPT-2, etc.)](#self-attention-models-bert-gpt-2-etc)

  * [Head and Model Views](#head-and-model-views)

  * [Neuron View](#neuron-view-1)

- [Encoder-decoder models (BART, T5, etc.)](#encoder-decoder-models-bart-t5-etc)

- [Installing from source](#installing-from-source)

- [Additional options](#additional-options)

  * [Dark / light mode](#dark--light-mode)

  * [Filtering layers](#filtering-layers)

  * [Setting default layer/head(s)](#setting-default-layerheads)

  * [Visualizing sentence pairs](#visualizing-sentence-pairs)

  * [Obtain HTML representations](#obtain-HTML-representations)

  * [Non-Huggingface models](#non-huggingface-models)

 - [Limitations](#%EF%B8%8F-limitations)



### Self-attention models (BERT, GPT-2, etc.)



#### Head and Model Views

First load a Huggingface model, either a pre-trained model as shown below, or your own fine-tuned model.

 Be sure to set `output_attentions=True`.

```python

from transformers import AutoTokenizer, AutoModel, utils

utils.logging.set_verbosity_error()  # Suppress standard warnings

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

model = AutoModel.from_pretrained("bert-base-uncased", output_attentions=True)

```



Then prepare inputs and compute attention:



```python

inputs = tokenizer.encode("The cat sat on the mat", return_tensors='pt')

outputs = model(inputs)

attention = outputs[-1]  # Output includes attention weights when output_attentions=True

tokens = tokenizer.convert_ids_to_tokens(inputs[0]) 

```



Finally, display the attention weights using the [`head_view`](bertviz/head_view.py) or [`model_view`](bertviz/model_view.py)

 functions:



```python

from bertviz import head_view

head_view(attention, tokens)

```



Examples: DistilBERT ([Model View Notebook](notebooks/model_view_distilbert.ipynb), [Head View Notebook](notebooks/head_view_distilbert.ipynb))



For full API, please refer to the source code for the [head view](bertviz/head_view.py) or [model view](bertviz/model_view.py).



#### Neuron View



The neuron view is invoked differently than the head view or model view, due to requiring access to the model's

query/key vectors, which are not returned through the Huggingface API. It is currently limited to custom versions of BERT, GPT-2, and

RoBERTa included with BertViz.



```python

# Import specialized versions of models (that return query/key vectors)

from bertviz.transformers_neuron_view import BertModel, BertTokenizer

from bertviz.neuron_view import show



model_type = 'bert'

model_version = 'bert-base-uncased'

do_lower_case = True

sentence_a = "The cat sat on the mat"

sentence_b = "The cat lay on the rug"

model = BertModel.from_pretrained(model_version, output_attentions=True)

tokenizer = BertTokenizer.from_pretrained(model_version, do_lower_case=do_lower_case)

show(model, model_type, tokenizer, sentence_a, sentence_b, layer=2, head=0)

```



Examples:

BERT ([Notebook](notebooks/neuron_view_bert.ipynb),

[Colab](https://colab.research.google.com/drive/1m37iotFeubMrp9qIf9yscXEL1zhxTN2b)) •

GPT-2 ([Notebook](notebooks/neuron_view_gpt2.ipynb),

[Colab](https://colab.research.google.com/drive/1s8XCCyxsKvNRWNzjWi5Nl8ZAYZ5YkLm_)) •

RoBERTa

([Notebook](notebooks/neuron_view_roberta.ipynb))  



For full API, please refer to the [source](bertviz/neuron_view.py).



### Encoder-decoder models (BART, T5, etc.)



The head view and model view both support encoder-decoder models.



First, load an encoder-decoder model:



```python

from transformers import AutoTokenizer, AutoModel



tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")

model = AutoModel.from_pretrained("Helsinki-NLP/opus-mt-en-de", output_attentions=True)

```



Then prepare the inputs and compute attention:

```python

encoder_input_ids = tokenizer("She sees the small elephant.", return_tensors="pt", add_special_tokens=True).input_ids

with tokenizer.as_target_tokenizer():

    decoder_input_ids = tokenizer("Sie sieht den kleinen Elefanten.", return_tensors="pt", add_special_tokens=True).input_ids



outputs = model(input_ids=encoder_input_ids, decoder_input_ids=decoder_input_ids)



encoder_text = tokenizer.convert_ids_to_tokens(encoder_input_ids[0])

decoder_text = tokenizer.convert_ids_to_tokens(decoder_input_ids[0])

```



Finally, display the visualization using either [`head_view`](bertviz/head_view.py) or [`model_view`](bertviz/model_view.py).

```python

from bertviz import model_view

model_view(

    encoder_attention=outputs.encoder_attentions,

    decoder_attention=outputs.decoder_attentions,

    cross_attention=outputs.cross_attentions,

    encoder_tokens= encoder_text,

    decoder_tokens = decoder_text

)

```



You may select `Encoder`, `Decoder`, or `Cross` attention from the drop-down in the upper left corner of the visualization.



Examples: MarianMT ([Notebook](notebooks/model_view_encoder_decoder.ipynb)) • BART ([Notebook](notebooks/model_view_bart.ipynb))



For full API, please refer to the source code for the [head view](bertviz/head_view.py) or [model view](bertviz/model_view.py).



### Installing from source

```bash

git clone https://github.com/jessevig/bertviz.git

cd bertviz

python setup.py develop

```



### Additional options



#### Dark / light mode



The model view and neuron view support dark (default) and light modes. You may set the mode using

the `display_mode` parameter:

```python

model_view(attention, tokens, display_mode="light")

```



#### Filtering layers



To improve the responsiveness of the tool when visualizing larger models or inputs, you may set the `include_layers`

 parameter to restrict the visualization to a subset of layers (zero-indexed). This option is available in the head view and model

view.



**Example:** Render model view with only layers 5 and 6 displayed

```python

model_view(attention, tokens, include_layers=[5, 6])

```



For the model view, you may also restrict the visualization to a subset of attention heads (zero-indexed) by setting the 

`include_heads` parameter. 



#### Setting default layer/head(s)



In the head view, you may choose a specific `layer` and collection of `heads` as the default selection when the

 visualization first renders. Note: this is different from the `include_heads`/`include_layers` parameter (above), which 

 removes layers and heads from the visualization completely.



**Example:** Render head view with layer 2 and heads 3 and 5 pre-selected

```python

head_view(attention, tokens, layer=2, heads=[3,5])

```



You may also pre-select a specific `layer` and single `head` for the neuron view. 



#### Visualizing sentence pairs



Some models, e.g. BERT, accept a pair of sentences as input. BertViz optionally supports a drop-down menu that allows 

user to filter attention based on which sentence the tokens are in, e.g. only show attention between tokens in first

 sentence and tokens in second sentence.

 

 

##### Head and model views

To enable this feature when invoking the [`head_view`](bertviz/head_view.py) or [`model_view`](bertviz/model_view.py) functions, set

 the `sentence_b_start` parameter to the start index of the second sentence. Note that the method for computing this

 index will depend on the model.

 

Example (BERT):



```python

from bertviz import head_view

from transformers import AutoTokenizer, AutoModel, utils

utils.logging.set_verbosity_error()  # Suppress standard warnings



# NOTE: This code is model-specific

model_version = 'bert-base-uncased'

model = AutoModel.from_pretrained(model_version, output_attentions=True)

tokenizer = AutoTokenizer.from_pretrained(model_version)

sentence_a = "the rabbit quickly hopped"

sentence_b = "The turtle slowly crawled"

inputs = tokenizer.encode_plus(sentence_a, sentence_b, return_tensors='pt')

input_ids = inputs['input_ids']

token_type_ids = inputs['token_type_ids'] # token type id is 0 for Sentence A and 1 for Sentence B

attention = model(input_ids, token_type_ids=token_type_ids)[-1]

sentence_b_start = token_type_ids[0].tolist().index(1) # Sentence B starts at first index of token type id 1

token_ids = input_ids[0].tolist() # Batch index 0

tokens = tokenizer.convert_ids_to_tokens(token_ids)    

head_view(attention, tokens, sentence_b_start)

``` 



##### Neuron view



To enable this option in the neuron view, simply set the `sentence_a` and `sentence_b` parameters in [`neuron_view.show()`](bertviz/neuron_view.py).



#### Obtain HTML representations



Support to retrieve the generated HTML representations has been added to head_view, model_view and neuron_view.



Setting the 'html_action' parameter to 'return' will make the function call return a single HTML Python object that can be further processed. Remember you can access the HTML source using the data attribute of a Python HTML object.



The default behavior for 'html_action' is 'view', which will display the visualization but won't return the HTML object.



This functionality is useful if you need to:

- Save the representation as an independent HTML file that can be accessed via web browser

- Use custom display methods as the ones needed in Databricks to visualize HTML objects



Example (head and model views):



```python

from transformers import AutoTokenizer, AutoModel, utils

from bertviz import head_view



utils.logging.set_verbosity_error()  # Suppress standard warnings

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

model = AutoModel.from_pretrained("bert-base-uncased", output_attentions=True)



inputs = tokenizer.encode("The cat sat on the mat", return_tensors='pt')

outputs = model(inputs)

attention = outputs[-1]  # Output includes attention weights when output_attentions=True

tokens = tokenizer.convert_ids_to_tokens(inputs[0]) 



html_head_view = head_view(attention, tokens, html_action='return')



with open("PATH_TO_YOUR_FILE/head_view.html", 'w') as file:

    file.write(html_head_view.data)



```



Example (neuron view):



```python

# Import specialized versions of models (that return query/key vectors)

from bertviz.transformers_neuron_view import BertModel, BertTokenizer

from bertviz.neuron_view import show



model_type = 'bert'

model_version = 'bert-base-uncased'

do_lower_case = True

sentence_a = "The cat sat on the mat"

sentence_b = "The cat lay on the rug"

model = BertModel.from_pretrained(model_version, output_attentions=True)

tokenizer = BertTokenizer.from_pretrained(model_version, do_lower_case=do_lower_case)

html_neuron_view = show(model, model_type, tokenizer, sentence_a, sentence_b, layer=2, head=0, html_action='return')



with open("PATH_TO_YOUR_FILE/neuron_view.html", 'w') as file:

    file.write(html_neuron_view.data)

```



#### Non-Huggingface models



The head view and model view may be used to

 visualize self-attention for any standard Transformer model,

as long as the attention weights are available and follow the format specified in [`head_view`](bertviz/head_view.py) and

 [`model_view`](bertviz/model_view.py) (which is the format 

returned from Huggingface models). In some case, Tensorflow checkpoints may be loaded as Huggingface models as described

 in the

 [Huggingface docs](https://huggingface.co/transformers/). 

 

 

### ⚠️ Limitations



#### Tool

* This tool is designed for shorter inputs and may run slowly if the input text is very long and/or the model is very large.

 To mitigate this, you may wish to filter the layers displayed by setting the **`include_layers`** parameter, as described [above](#filtering-layers).

* When running on Colab, some of the visualizations will fail (runtime disconnection) when the input text is long.  To mitigate this, you may wish to filter the layers displayed by setting the **`include_layers`** parameter, as described [above](#filtering-layers).

* The *neuron view* only supports the custom BERT, GPT-2, and RoBERTa models included with the tool. This view needs access to the query and key vectors, 

which required modifying the model code (see `transformers_neuron_view` directory), which has only been done for these three models.



#### Attention as "explanation"?

* Visualizing attention weights illuminates one type of architecture within the model but does not

necessarily provide a direct *explanation* for predictions [[1](https://arxiv.org/pdf/1909.11218.pdf), [2](https://arxiv.org/abs/1902.10186), [3](https://arxiv.org/pdf/1908.04626.pdf)].

* If you wish to understand how the input text influences output predictions more directly, consider [saliency methods](https://arxiv.org/pdf/2010.05607.pdf) provided 

by tools such as the [Language Interpretability Toolkit](https://github.com/PAIR-code/lit) or [Ecco](https://github.com/jalammar/ecco).



## 🔬 Paper



[A Multiscale Visualization of Attention in the Transformer Model](https://www.aclweb.org/anthology/P19-3007.pdf) (ACL System Demonstrations 2019).



### Citation

```bibtex

@inproceedings{vig-2019-multiscale,

    title = "A Multiscale Visualization of Attention in the Transformer Model",

    author = "Vig, Jesse",

    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",

    month = jul,

    year = "2019",

    address = "Florence, Italy",

    publisher = "Association for Computational Linguistics",

    url = "https://www.aclweb.org/anthology/P19-3007",

    doi = "10.18653/v1/P19-3007",

    pages = "37--42",

}

```

## Authors

[Jesse Vig](https://twitter.com/jesse_vig)



## 🙏 Acknowledgments

We are grateful to the authors of the following projects, which are incorporated into this repo:

* https://github.com/tensorflow/tensor2tensor

* https://github.com/huggingface/pytorch-pretrained-BERT



## License



This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details