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from gradio import Interface | |
import gradio as gr | |
import aranizer | |
from aranizer import aranizer_bpe50k, aranizer_bpe64k, aranizer_bpe86k, aranizer_sp32k, aranizer_sp50k, aranizer_sp64k, aranizer_sp86k | |
from transformers import AutoTokenizer | |
# Load additional tokenizers from transformers | |
gpt_13b_tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/AceGPT-13B") | |
gpt_7b_tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/AceGPT-7B") | |
jais_13b_tokenizer = AutoTokenizer.from_pretrained("inception-mbzuai/jais-13b") | |
# List of available tokenizers and a dictionary to load them | |
tokenizer_options = [ | |
"aranizer_bpe50k", "aranizer_bpe64k", "aranizer_bpe86k", | |
"aranizer_sp32k", "aranizer_sp50k", "aranizer_sp64k", "aranizer_sp86k", | |
"FreedomIntelligence/AceGPT-13B", # Previously added GPT tokenizer | |
"FreedomIntelligence/AceGPT-7B", # Another previously added GPT tokenizer | |
"inception-mbzuai/jais-13b" # Adding the new tokenizer to the options | |
] | |
tokenizers = { | |
"aranizer_bpe50k": aranizer_bpe50k.get_tokenizer, | |
"aranizer_bpe64k": aranizer_bpe64k.get_tokenizer, | |
"aranizer_bpe86k": aranizer_bpe86k.get_tokenizer, | |
"aranizer_sp32k": aranizer_sp32k.get_tokenizer, | |
"aranizer_sp50k": aranizer_sp50k.get_tokenizer, | |
"aranizer_sp64k": aranizer_sp64k.get_tokenizer, | |
"aranizer_sp86k": aranizer_sp86k.get_tokenizer, | |
"FreedomIntelligence/AceGPT-13B": lambda: gpt_13b_tokenizer, | |
"FreedomIntelligence/AceGPT-7B": lambda: gpt_7b_tokenizer, | |
"inception-mbzuai/jais-13b": lambda: jais_13b_tokenizer # Adding the new Jais tokenizer | |
} | |
def compare_tokenizers(tokenizer_name, text): | |
# Handle the transformer tokenizers separately due to API differences | |
if tokenizer_name in ["FreedomIntelligence/AceGPT-13B", "FreedomIntelligence/AceGPT-7B", "inception-mbzuai/jais-13b"]: | |
tokenizer = tokenizers[tokenizer_name]() | |
tokens = tokenizer.tokenize(text) | |
encoded_output = tokenizer.encode(text, add_special_tokens=True, return_tensors="pt") | |
decoded_text = tokenizer.decode(encoded_output[0], skip_special_tokens=True) | |
else: | |
# AraNizer tokenizers | |
tokenizer = tokenizers[tokenizer_name]() | |
tokens = tokenizer.tokenize(text) | |
encoded_output = tokenizer.encode(text, add_special_tokens=True) | |
decoded_text = tokenizer.decode(encoded_output) | |
# Prepare the results to be displayed | |
results = [(tokenizer_name, tokens, encoded_output, decoded_text)] | |
return results | |
# Define the Gradio interface components with a dropdown for model selection | |
inputs_component = [ | |
gr.Dropdown(choices=tokenizer_options, label="Select Tokenizer"), | |
gr.Textbox(lines=2, placeholder="Enter text here...", label="Input Text") | |
] | |
outputs_component = gr.Dataframe( | |
headers=["Tokenizer", "Tokens", "Encoded Output", "Decoded Text"], | |
label="Results", | |
type="pandas" | |
) | |
# Setting up the interface | |
iface = Interface( | |
fn=compare_tokenizers, | |
inputs=inputs_component, | |
outputs=outputs_component, | |
title="Tokenizer Comparison", | |
live=True | |
) | |
# Launching the Gradio app | |
iface.launch() |