Royce  Reinger

Royce Reinger

1672521300

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

BertViz

Visualize Attention in NLP Models

BertViz is an interactive tool for visualizing attention in 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. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism.

For updates on BertViz and related projects, feel free to follow me on Twitter.

🚀 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 by Llion Jones.

🕹 Try out the head view in the Interactive Colab Tutorial (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 (all visualizations pre-loaded).

model view

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 (all visualizations pre-loaded).

neuron view

⚡️ Getting Started

Running BertViz in a Jupyter Notebook

From the command line:

pip install bertviz

You must also have Jupyter Notebook and ipywidgets installed:

pip install jupyterlab
pip install ipywidgets

(If you run into any issues installing Jupyter or ipywidgets, consult the documentation here and here.)

To create a new Jupyter notebook, simply run:

jupyter notebook

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

Running BertViz in Colab

To run in Colab, 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:

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. See Documentation for additional use cases and examples, e.g., encoder-decoder models.

Running sample notebooks

You may also run any of the sample notebooks included with BertViz:

git clone --depth 1 git@github.com:jessevig/bertviz.git
cd bertviz/notebooks
jupyter notebook

🕹 Interactive Tutorial

Check out the Interactive Colab Tutorial 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

📖 Documentation

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.

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:

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 or model_view functions:

from bertviz import head_view
head_view(attention, tokens)

Examples: DistilBERT (Model View Notebook, Head View Notebook)

For full API, please refer to the source code for the head view or model view.

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.

# 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, Colab) • GPT-2 (Notebook, Colab) • RoBERTa (Notebook)

For full API, please refer to the source.

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

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

First, load an encoder-decoder model:

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:

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 or model_view.

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) • BART (Notebook)

For full API, please refer to the source code for the head view or model view.

Installing from source

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:

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

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

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 or model_view 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):

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().

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):

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):

# 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 and model_view (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.

⚠️ 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.
  • 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.
  • 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, 2, 3].
  • If you wish to understand how the input text influences output predictions more directly, consider saliency methods provided by tools such as the Language Interpretability Toolkit or Ecco.

🔬 Paper

A Multiscale Visualization of Attention in the Transformer Model (ACL System Demonstrations 2019).

Citation

@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",
}

Download Details:

Author: jessevig
Source Code: https://github.com/jessevig/bertviz 
License: Apache-2.0 license

#machinelearning #visualization #nlp 

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BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, Etc.)
Royce  Reinger

Royce Reinger

1672521300

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

BertViz

Visualize Attention in NLP Models

BertViz is an interactive tool for visualizing attention in 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. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism.

For updates on BertViz and related projects, feel free to follow me on Twitter.

🚀 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 by Llion Jones.

🕹 Try out the head view in the Interactive Colab Tutorial (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 (all visualizations pre-loaded).

model view

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 (all visualizations pre-loaded).

neuron view

⚡️ Getting Started

Running BertViz in a Jupyter Notebook

From the command line:

pip install bertviz

You must also have Jupyter Notebook and ipywidgets installed:

pip install jupyterlab
pip install ipywidgets

(If you run into any issues installing Jupyter or ipywidgets, consult the documentation here and here.)

To create a new Jupyter notebook, simply run:

jupyter notebook

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

Running BertViz in Colab

To run in Colab, 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:

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. See Documentation for additional use cases and examples, e.g., encoder-decoder models.

Running sample notebooks

You may also run any of the sample notebooks included with BertViz:

git clone --depth 1 git@github.com:jessevig/bertviz.git
cd bertviz/notebooks
jupyter notebook

🕹 Interactive Tutorial

Check out the Interactive Colab Tutorial 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

📖 Documentation

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.

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:

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 or model_view functions:

from bertviz import head_view
head_view(attention, tokens)

Examples: DistilBERT (Model View Notebook, Head View Notebook)

For full API, please refer to the source code for the head view or model view.

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.

# 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, Colab) • GPT-2 (Notebook, Colab) • RoBERTa (Notebook)

For full API, please refer to the source.

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

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

First, load an encoder-decoder model:

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:

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 or model_view.

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) • BART (Notebook)

For full API, please refer to the source code for the head view or model view.

Installing from source

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:

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

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

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 or model_view 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):

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().

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):

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):

# 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 and model_view (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.

⚠️ 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.
  • 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.
  • 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, 2, 3].
  • If you wish to understand how the input text influences output predictions more directly, consider saliency methods provided by tools such as the Language Interpretability Toolkit or Ecco.

🔬 Paper

A Multiscale Visualization of Attention in the Transformer Model (ACL System Demonstrations 2019).

Citation

@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",
}

Download Details:

Author: jessevig
Source Code: https://github.com/jessevig/bertviz 
License: Apache-2.0 license

#machinelearning #visualization #nlp 

Murray  Beatty

Murray Beatty

1597773960

Is Common Sense Common In NLP Models?

NLP Models have shown tremendous advancements in syntactic, semantic and linguistic knowledge for downstream tasks. However, that raises an interesting research question — is it possible for them to go beyond pattern recognition and apply common sense for word-sense disambiguation?

Thus, to identify if BERT, a large pre-trained NLP model developed by Google, can solve common sense tasks, researchers took a closer look. The researchers from Westlake University and Fudan University, in collaboration with Microsoft Research Asia, discovered how the model computes the structured, common sense knowledge for downstream NLP tasks.

According to the researchers, it has been a long-standing debate as to whether pre-trained language models can solve tasks leveraging only a few shallow clues and their common sense of knowledge. To figure that out, researchers used a CommonsenseQA dataset for BERT to solve multiple-choice problems.

#opinions #ai common sense #bert #bert model #common sense #nlp model #nlp models

8 Open-Source Tools To Start Your NLP Journey

Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.

To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.

Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.


Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text

1| Natural Language Toolkit (NLTK)

About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.


#developers corner #learning nlp #natural language processing #natural language processing tools #nlp #nlp career #nlp tools #open source nlp tools #opensource nlp tools

Justyn  Ortiz

Justyn Ortiz

1597791600

Building and Visualizing Machine Language Translation from Scratch using TF2

This article requires some basic knowledge of Recurrent neural networks and GRU’s. I’ll give a brief intro to this concept in this article.

In Deep Learning sequence to sequence, models have achieved a lot of success for the tasks of machine language translation. In this article, we will be building a Spanish to English translator.

**Remember any bump in the animations refers to a mathematical operation being computed behind the scenes. **Also if you come across any french words in the animations consider them as Spanish words and continue(I tried to collect the best animations I could from the web ( ͡° ͜ʖ ͡°))

seq 2 seq visulizattion

seq 2 seq visualization

A brief intro to RNN’s:

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Each RNN unit takes in an input vector(input #1) and previous hidden state vector(hidden state #0) to compute current hidden state vector(hidden state #1) and the output vector(output #1) of that particular unit. we stack many of these units to make build our model(if we are specifically talking about the first unit, logically there would be no previous state so initialize that with zeros). In the coming animations, you would encounter encoder and decoder parts, each of those encoder-decoder parts is stacked with these RNN’s(GRU’s in our current example, they can be LSTM’s as well but GRU’s would suffice).

Let’s begin with seq2seq model:

This seq2seq model takes input as a sequence of items and outputs another sequence of items. For example, in this model, it would take in a Spanish sentence as input “Me gusta el arte” and outputs translated English sentence “I like art”.

Image for post

Image for post

A high-level seq2seq attention model

The attention mechanism was born to help memorize long source sentences in neural machine translation (NMT). Rather than building a single context vector out of the encoder’s last hidden state, the secret sauce invented by attention is to create shortcuts between the context vector and the entire source input.

#attention-model #deep-learning #visualize-nlp #sequence-to-sequence #nlp

Arvel  Parker

Arvel Parker

1591177440

Visual Analytics and Advanced Data Visualization

Visual Analytics is the scientific visualization to emerge an idea to present data in such a way so that it could be easily determined by anyone.

It gives an idea to the human mind to directly interact with interactive visuals which could help in making decisions easy and fast.

Visual Analytics basically breaks the complex data in a simple way.

The human brain is fast and is built to process things faster. So Data visualization provides its way to make things easy for students, researchers, mathematicians, scientists e

#blogs #data visualization #business analytics #data visualization techniques #visual analytics #visualizing ml models