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from huggingface_hub import list_models |
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from cachetools import cached, TTLCache |
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from toolz import groupby, valmap |
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import gradio as gr |
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from tqdm.auto import tqdm |
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import pandas as pd |
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@cached(TTLCache(maxsize=10, ttl=60 * 60 * 3)) |
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def get_all_models(): |
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models = list(tqdm(iter(list_models(cardData=True)))) |
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return [model for model in models if model is not None] |
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def has_base_model_info(model): |
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try: |
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if card_data := model.cardData: |
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if base_model := card_data.get("base_model"): |
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if isinstance(base_model, str): |
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return True |
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except AttributeError: |
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return False |
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return False |
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grouped_by_has_base_model_info = groupby(has_base_model_info, get_all_models()) |
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print(valmap(len, grouped_by_has_base_model_info)) |
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summary = f"""{len(grouped_by_has_base_model_info.get(True)):,} models have base model info. |
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{len(grouped_by_has_base_model_info.get(False)):,} models don't have base model info. |
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Currently {round(len(grouped_by_has_base_model_info.get(True))/len(get_all_models())*100,2)}% of models have base model info.""" |
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models_with_base_model_info = grouped_by_has_base_model_info.get(True) |
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base_models = [ |
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model.cardData.get("base_model") for model in models_with_base_model_info |
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] |
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df = pd.DataFrame( |
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pd.DataFrame({"base_model": base_models}).value_counts() |
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).reset_index() |
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df_with_org = df.copy(deep=True) |
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def parse_org(hub_id): |
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parts = hub_id.split("/") |
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return parts[0] if len(parts) == 2 else None |
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df_with_org["org"] = df_with_org["base_model"].apply(parse_org) |
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df_with_org = df_with_org.dropna(subset=["org"]) |
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grouped_by_base_model = groupby( |
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lambda x: x.cardData.get("base_model"), models_with_base_model_info |
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) |
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all_base_models = df["base_model"].to_list() |
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def return_models_for_base_model(base_model): |
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models = grouped_by_base_model.get(base_model) |
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models = sorted(models, key=lambda x: x.downloads, reverse=True) |
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results = "" |
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results += f"## {base_model} children\n\n" |
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results += f"{base_model} has {len(models)} children\n\n" |
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for model in models: |
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url = f"https://huggingface.co/{model.modelId}" |
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results += ( |
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f"[{model.modelId}]({url}) | number of downloads {model.downloads}" + "\n\n" |
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) |
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return results |
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with gr.Blocks() as demo: |
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gr.Markdown("# Base model explorer") |
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gr.Markdown( |
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"""When sharing models to the Hub it is possible to specify a base model in the model card i.e. that your model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased). |
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This Space allows you to find children models for a given base model and view the popularity of models for fine-tuning.""" |
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) |
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gr.Markdown(summary) |
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gr.Markdown("### Find all models trained from a base model") |
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base_model = gr.Dropdown(all_base_models, label="Base Model") |
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results = gr.Markdown() |
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base_model.change(return_models_for_base_model, base_model, results) |
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with gr.Accordion("Base model popularity ranking", open=False): |
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gr.DataFrame(df.head(50)) |
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with gr.Accordion("Base model popularity ranking by organization", open=False): |
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gr.DataFrame( |
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pd.DataFrame( |
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df_with_org.groupby("org")["count"] |
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.sum() |
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.sort_values(ascending=False) |
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.head(50) |
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) |
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.reset_index() |
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.sort_values("count", ascending=False) |
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) |
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demo.launch() |
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