![]() ![]() Setting the 'html_action' parameter to 'return' will make the function call return a single HTML Python object that can be further processed. Support to retrieve the generated HTML representations has been added to head_view, model_view and neuron_view. To enable this option in the neuron view, simply set the sentence_a and sentence_b parameters in neuron_view.show(). ![]() Head_view( attention, tokens, sentence_b_start) Neuron view tolist() # Batch index 0 tokens = tokenizer. index( 1) # Sentence B starts at first index of token type id 1 token_ids = input_ids. Token_type_ids = inputs # token type id is 0 for Sentence A and 1 for Sentence B attention = model( input_ids, token_type_ids = token_type_ids) encode_plus( sentence_a, sentence_b, return_tensors = 'pt') Sentence_a = "the rabbit quickly hopped" sentence_b = "The turtle slowly crawled" inputs = tokenizer. from_pretrained( model_version, output_attentions = True) set_verbosity_error() # Suppress standard warnings # NOTE: This code is model-specific model_version = 'bert-base-uncased' model = AutoModel. You may also run any of the sample notebooks included with BertViz:įrom bertviz import head_view from transformers import AutoTokenizer, AutoModel, utils utils. See Documentation for additional use cases and examples, e.g., encoder-decoder models. Feel free to experiment with different input texts and The visualization may take a few seconds to load. convert_ids_to_tokens( inputs) # Convert input ids to token strings model_view( attention, tokens) # Display model view encode( input_text, return_tensors = 'pt') # Tokenize input text outputs = model( inputs) # Run model attention = outputs # Retrieve attention from model outputs tokens = tokenizer. from_pretrained( model_name, output_attentions = True) # Configure model to return attention values tokenizer = AutoTokenizer. set_verbosity_error() # Suppress standard warnings model_name = "microsoft/xtremedistil-l12-h384-uncased" # Find popular HuggingFace models here: input_text = "The cat sat on the mat" model = AutoModel. ⚡️ Getting Started Running BertViz in a Jupyter Notebookįrom transformers import AutoTokenizer, AutoModel, utils from bertviz import model_view utils. □ Try out the neuron view in the Interactive Colab Tutorial (all visualizations pre-loaded). The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention. □ Try out the model view in the Interactive Colab Tutorial (all visualizations pre-loaded). The model view shows a bird's-eye view of attention across all layers and heads. □ Try out the head view in the Interactive Colab Tutorial (all visualizations pre-loaded). It is based on the excellent Tensor2Tensor visualization tool by Llion Jones. The head view visualizes attention for one or more attention heads in the same BertViz extends theīy Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. Notebook through a simple Python API that supports most Huggingface models. BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. ![]()
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