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| import os | |
| import gradio as gr | |
| import pandas as pd | |
| from gradio.themes import colors | |
| from transformers import AutoTokenizer | |
| os.environ['TOKENIZERS_PARALLELISM'] = "false" | |
| # Function to map tokenized text to IDs | |
| def inference( | |
| text="", | |
| model_id="openai/clip-vit-large-patch14", | |
| progress=gr.Progress() | |
| ) -> (list[str, str], list[str, str], pd.DataFrame): | |
| if text == "": | |
| return [], [], pd.DataFrame() | |
| progress(0, desc='Loading tokenizer...') | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # Use tokenizer to tokenize the text | |
| progress(0.5, desc='Tokenizing text...') | |
| text_inputs = tokenizer(text, return_tensors='pt') | |
| input_ids = text_inputs['input_ids'].tolist()[0] # Convert tensor to list | |
| # Create pairs of tokens and IDs | |
| tokens = [tokenizer.decode([id_]) for id_ in input_ids] | |
| token_pairs = [] | |
| for token, id_ in zip(tokens, input_ids): | |
| token_pairs.append((token, str(id_))) | |
| # Count the number of characters and tokens | |
| pos_count = pd.DataFrame({ | |
| "Char Count": [len(text)], | |
| "Token Count": [len(token_pairs)] | |
| }) | |
| # Create list of special tokens | |
| special_tokens = [] | |
| for k, v in tokenizer.special_tokens_map.items(): | |
| if k == 'additional_special_tokens': | |
| continue | |
| sp_token_map = [str(k), str(v)] | |
| special_tokens.append(sp_token_map) | |
| return token_pairs, special_tokens, pos_count | |
| if __name__ == '__main__': | |
| iface = gr.Interface( | |
| fn=inference, | |
| inputs=[ | |
| gr.Textbox(label="Text"), | |
| gr.Dropdown( | |
| label="Model", | |
| choices=[ | |
| "openai/clip-vit-large-patch14", | |
| "google/gemma-7b", | |
| "google-bert/bert-base-uncased", | |
| "google/flan-t5-base", | |
| "openai-community/gpt2", | |
| "rinna/japanese-gpt-1b", | |
| "cyberagent/open-calm-7b", | |
| ], | |
| value="openai/clip-vit-large-patch14" | |
| ), | |
| ], | |
| outputs=[ | |
| gr.Highlightedtext(label="Highlighted Text"), | |
| gr.Highlightedtext(label="Special Tokens", combine_adjacent=True, adjacent_separator=' / '), | |
| gr.Dataframe(label="Position Count"), | |
| ], | |
| examples=[ | |
| ["When I told my computer I needed a break, it froze.", "openai/clip-vit-large-patch14"], | |
| ["Yesterday, I thought my cat was studying for her degree in philosophy because she sat on my book, " | |
| "but turns out she was just trying to hatch a plot to steal my dinner.", "openai/clip-vit-large-patch14"], | |
| ["The square root of x is the cube root of y. What is y to the power of 2, if x = 4?", | |
| "google/flan-t5-base"], | |
| ["In my home country, it's a custom to say 'いただきマサチューセッツ' before we start eating a meal.", | |
| "google/gemma-7b"], | |
| ["日本で一番高い山は富士山ですが、二番目に高い山は何ですか？", "rinna/japanese-gpt-1b"], | |
| ], | |
| cache_examples=True, | |
| title="TokenVisor 👀", | |
| description="Visualize how the Tokenizer used in Hugging Face's Transformers library tokenizes text.", | |
| theme=gr.Theme(primary_hue=colors.green, secondary_hue=colors.yellow), | |
| allow_flagging="never", | |
| ) | |
| iface.queue().launch() | |