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import gradio as gr |
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import pandas as pd |
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from cachetools import TTLCache, cached |
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from huggingface_hub import list_models |
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from toolz import groupby |
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from tqdm.auto import tqdm |
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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( |
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tqdm( |
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iter(list_models(cardData=True, limit=None, sort="downloads", direction=-1)) |
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) |
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) |
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models = [model for model in models if model is not None] |
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return [ |
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model for model in models if model.downloads > 1 |
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] |
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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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def produce_summary(): |
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return 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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pipeline_tags = [x.pipeline_tag for x in models_with_base_model_info] |
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pipeline_tags = sorted(pipeline_tags) |
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unique_pipeline_tags = list( |
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{x.pipeline_tag for x in models_with_base_model_info if x.pipeline_tag is not None} |
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) |
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def parse_org(hub_id): |
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parts = hub_id.split("/") |
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if len(parts) == 2: |
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return parts[0] if parts[0] != "." else None |
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else: |
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return "huggingface" |
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def render_model_hub_link(hub_id): |
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link = f"https://huggingface.co/{hub_id}" |
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return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{hub_id}</a>' |
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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 get_grandchildren(base_model): |
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grandchildren = [] |
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for model in tqdm(grouped_by_base_model[base_model]): |
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model_id = model.modelId |
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grandchildren.extend(grouped_by_base_model.get(model_id, [])) |
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return grandchildren |
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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 += ( |
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"## Models fine-tuned from" |
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f" [`{base_model}`](https://huggingface.co/{base_model}) \n\n" |
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) |
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results += f"`{base_model}` has {len(models)} children\n\n" |
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total_download_number = sum(model.downloads for model in models) |
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results += ( |
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f"`{base_model}`'s children have been" |
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f" downloaded {total_download_number:,} times\n\n" |
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) |
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grandchildren = get_grandchildren(base_model) |
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number_of_grandchildren = len(grandchildren) |
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results += f"`{base_model}` has {number_of_grandchildren} grandchildren\n\n" |
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grandchildren_download_count = sum(model.downloads for model in grandchildren) |
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results += ( |
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f"`{base_model}`'s grandchildren have been" |
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f" downloaded {grandchildren_download_count:,} times\n\n" |
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) |
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results += f"Including grandchildren, `{base_model}` has {number_of_grandchildren + len(models):,} descendants\n\n" |
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results += f"Including grandchildren, `{base_model}`'s descendants have been downloaded {grandchildren_download_count + total_download_number:,} times\n\n" |
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results += "### Children models \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:,}" |
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+ "\n\n" |
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) |
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return results |
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def return_base_model_popularity(pipeline=None): |
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df_with_pipeline_info = ( |
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pd.DataFrame({"base_model": base_models, "pipeline": pipeline_tags}) |
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.value_counts() |
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.reset_index() |
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) |
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if pipeline is not None: |
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df_with_pipeline_info = df_with_pipeline_info[ |
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df_with_pipeline_info["pipeline"] == pipeline |
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] |
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keep_columns = ["base_model", "count"] |
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df_with_pipeline_info["base_model"] = df_with_pipeline_info["base_model"].apply( |
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render_model_hub_link |
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) |
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return df_with_pipeline_info[keep_columns].head(50) |
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def return_base_model_popularity_by_org(pipeline=None): |
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referenced_base_models = [ |
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f"[`{model}`](https://huggingface.co/{model})" for model in base_models |
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] |
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df_with_pipeline_info = pd.DataFrame( |
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{"base_model": base_models, "pipeline": pipeline_tags} |
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) |
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df_with_pipeline_info["org"] = df_with_pipeline_info["base_model"].apply(parse_org) |
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df_with_pipeline_info["org"] = df_with_pipeline_info["org"].apply( |
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render_model_hub_link |
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) |
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df_with_pipeline_info = df_with_pipeline_info.dropna(subset=["org"]) |
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df_with_org = df_with_pipeline_info.copy(deep=True) |
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if pipeline is not None: |
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df_with_org = df_with_pipeline_info[df_with_org["pipeline"] == pipeline] |
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df_with_org = df_with_org.drop(columns=["pipeline"]) |
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df_with_org = pd.DataFrame(df_with_org.value_counts()) |
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return 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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.reset_index() |
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.head(50) |
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) |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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"# Base model explorer: explore the lineage of models on the 🤗 Hub" |
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) |
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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](https://huggingface.co/docs/hub/model-cards#specifying-a-base-model), 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's models for a given base model and view the popularity of models for fine-tuning. |
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You can also optionally filter by the task to see rankings for a particular machine learning task. |
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Don't forget to ❤ if you like this space 🤗""" |
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) |
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gr.Markdown(produce_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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gr.Markdown("## Base model rankings ") |
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dropdown = gr.Dropdown( |
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choices=unique_pipeline_tags, |
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value=None, |
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label="Filter rankings by task pipeline", |
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) |
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with gr.Accordion("Base model popularity ranking", open=False): |
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df_popularity = gr.DataFrame( |
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return_base_model_popularity(None), datatype="markdown" |
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) |
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dropdown.change(return_base_model_popularity, dropdown, df_popularity) |
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with gr.Accordion("Base model popularity ranking by organization", open=False): |
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df_popularity_org = gr.DataFrame( |
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return_base_model_popularity_by_org(None), datatype="markdown" |
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) |
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dropdown.change( |
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return_base_model_popularity_by_org, dropdown, df_popularity_org |
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) |
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demo.launch() |
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