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635c44a
1
Parent(s):
1f6107e
Create app.py
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app.py
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import requests
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import json
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import pandas as pd
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from tqdm.auto import tqdm
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import streamlit as st
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from huggingface_hub import HfApi, hf_hub_download
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from huggingface_hub.repocard import metadata_load
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def make_clickable(model_name):
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link = "https://huggingface.co/" + model_name
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return f'<a target="_blank" href="{link}">{model_name}</a>'
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def get_model_ids():
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api = HfApi()
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models = api.list_models(filter="llamaleaderboard")
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model_ids = [x.modelId for x in models]
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return model_ids
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def get_metadata(model_id):
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try:
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readme_path = hf_hub_download(model_id, filename="README.md")
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return metadata_load(readme_path)
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except requests.exceptions.HTTPError:
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# 404 README.md not found
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return None
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def parse_metrics_accuracy(meta):
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if "model-index" not in meta:
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return None
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result = meta["model-index"][0]["results"]
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metrics = result[0]["metrics"]
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accuracy = metrics[0]["value"]
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yield accuracy
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@st.cache(ttl=600)
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def get_data():
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data = []
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model_ids = get_model_ids()
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for model_id in tqdm(model_ids):
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meta = get_metadata(model_id)
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if meta is None:
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continue
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row = {}
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row["Model"] = model_id
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row["accuracy"] = parse_metrics_accuracy(meta)
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data.append(row)
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return pd.DataFrame.from_records(data)
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dataframe = get_data()
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dataframe = dataframe.fillna("")
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st.markdown("# The 🦙 Leaderboard")
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st.markdown(
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f"This is a leaderboard of **{len(dataframe)}** llama classification models.\n\n"
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)
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st.markdown(
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"Please click on the model's name to be redirected to its model card which includes documentation and examples on how to use it."
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)
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dataset_df = dataframe.reset_index(drop=True)
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dataset_df.index += 1
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# turn the model ids into clickable links
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dataset_df["Model"] = dataset_df["Model"].apply(make_clickable)
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table_html = dataset_df.to_html(escape=False)
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table_html = table_html.replace("<th>", '<th align="left">') # left-align the headers
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st.write(table_html, unsafe_allow_html=True)
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