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, logging from huggingface_hub import login import os # Retrieve your Hugging Face token from the environment variable HF_TOKEN = os.getenv('HF_TOKEN') if HF_TOKEN: HF_TOKEN = HF_TOKEN.strip() # Remove any leading or trailing whitespace/newlines login(token=HF_TOKEN) # 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") arabert_tokenizer = AutoTokenizer.from_pretrained("aubmindlab/bert-base-arabertv2") # Try to load the gated tokenizer try: meta_llama_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B") except Exception as e: meta_llama_tokenizer = None logging.warning(f"Could not load meta-llama/Meta-Llama-3-8B tokenizer: {e}") cohere_command_r_v01_tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-v01") cohere_command_r_plus_tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-plus") # 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", "FreedomIntelligence/AceGPT-7B", "inception-mbzuai/jais-13b", "aubmindlab/bert-base-arabertv2", "CohereForAI/c4ai-command-r-v01", "CohereForAI/c4ai-command-r-plus" ] if meta_llama_tokenizer: tokenizer_options.append("meta-llama/Meta-Llama-3-8B") 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, "aubmindlab/bert-base-arabertv2": lambda: arabert_tokenizer, "CohereForAI/c4ai-command-r-v01": lambda: cohere_command_r_v01_tokenizer, "CohereForAI/c4ai-command-r-plus": lambda: cohere_command_r_plus_tokenizer } if meta_llama_tokenizer: tokenizers["meta-llama/Meta-Llama-3-8B"] = lambda: meta_llama_tokenizer def compare_tokenizers(tokenizer_index, text): tokenizer_name = tokenizer_options[tokenizer_index] tokenizer = tokenizers[tokenizer_name]() tokens = tokenizer.tokenize(text) encoded_output = tokenizer.encode(text, add_special_tokens=True) decoded_text = tokenizer.decode(encoded_output, skip_special_tokens=True) # Ensure the tokens are properly decoded tokens_display = [token.encode('utf-8').decode('utf-8') if isinstance(token, bytes) else token for token in tokens] # Prepare the results to be displayed in HTML format tokens_html = "".join([ f"{token}" for token in tokens_display ]) encoded_html = "".join([ f"{token}" for token in encoded_output ]) decoded_html = f"
{decoded_text}
" results_html = f"""

Tokenizer: {tokenizer_name}

Tokens: {tokens_html}

Encoded: {encoded_html}

Decoded: {decoded_html}

""" return results_html # Define the Gradio interface components with a dropdown for model selection inputs_component = [ gr.Dropdown(choices=tokenizer_options, label="Select Tokenizer", type="index"), gr.Textbox(lines=2, placeholder="اكتب النص هنا...", label="Input Text") ] outputs_component = gr.HTML(label="Results") # Setting up the interface iface = Interface( fn=compare_tokenizers, inputs=inputs_component, outputs=outputs_component, title="Arabic Tokenizer Arena", live=True ) # Launching the Gradio app iface.launch()