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| import streamlit as st | |
| import pandas as pd | |
| from datasets import load_dataset | |
| from ast import literal_eval | |
| import altair as alt | |
| nlp_tasks = ["text-classification", "text-generation", "text2text-generation", "token-classification", "fill-mask", "question-answering" | |
| "translation", "conversational", "sentence-similarity", "summarization", "multiple-choice", "zero-shot-classification", "table-question-answering" | |
| ] | |
| audio_tasks = ["automatic-speech-recognition", "audio-classification", "text-to-speech", "audio-to-audio", "voice-activity-detection"] | |
| cv_tasks = ["image-classification", "image-segmentation", "zero-shot-image-classification", "image-to-image", "unconditional-image-generation", "object-detection"] | |
| multimodal = ["feature-extraction", "text-to-image", "visual-question-answering", "image-to-text", "document-question-answering"] | |
| tabular = ["tabular-clasification", "tabular-regression"] | |
| modalities = { | |
| "nlp": nlp_tasks, | |
| "audio": audio_tasks, | |
| "cv": cv_tasks, | |
| "multimodal": multimodal, | |
| "tabular": tabular, | |
| "rl": ["reinforcement-learning"] | |
| } | |
| def modality(row): | |
| pipeline = row["pipeline"] | |
| for modality, tasks in modalities.items(): | |
| if pipeline in tasks: | |
| return modality | |
| if type(pipeline) == "str": | |
| return "unk_modality" | |
| return None | |
| supported_revisions = ["27_09_22"] | |
| def process_dataset(version): | |
| # Load dataset at specified revision | |
| dataset = load_dataset("open-source-metrics/model-repos-stats", revision=version) | |
| # Convert to pandas dataframe | |
| data = dataset["train"].to_pandas() | |
| # Add modality column | |
| data["modality"] = data.apply(modality, axis=1) | |
| # Bin the model card length into some bins | |
| data["length_bins"] = pd.cut(data["text_length"], [0, 200, 1000, 2000, 3000, 4000, 5000, 7500, 10000, 20000, 50000]) | |
| return data | |
| base = st.selectbox( | |
| 'What revision do you want to use', | |
| supported_revisions) | |
| data = process_dataset(base) | |
| total_samples = data.shape[0] | |
| st.metric(label="Total models", value=total_samples) | |
| tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Language", "License", "Pipeline", "Discussion Features", "Libraries", "Model Cards", "Super users"]) | |
| with tab1: | |
| st.header("Languages info") | |
| data.loc[data.languages == "False", 'languages'] = None | |
| data.loc[data.languages == {}, 'languages'] = None | |
| no_lang_count = data["languages"].isna().sum() | |
| data["languages"] = data["languages"].fillna('none') | |
| def make_list(row): | |
| languages = row["languages"] | |
| if languages == "none": | |
| return [] | |
| return literal_eval(languages) | |
| def language_count(row): | |
| languages = row["languages"] | |
| leng = len(languages) | |
| return leng | |
| data["languages"] = data.apply(make_list, axis=1) | |
| data["repos_count"] = data.apply(language_count, axis=1) | |
| models_with_langs = data[data["repos_count"] > 0] | |
| langs = models_with_langs["languages"].explode() | |
| langs = langs[langs != {}] | |
| total_langs = len(langs.unique()) | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric(label="Language Specified", value=total_samples-no_lang_count) | |
| with col2: | |
| st.metric(label="No Language Specified", value=no_lang_count) | |
| with col3: | |
| st.metric(label="Total Unique Languages", value=total_langs) | |
| st.subheader("Distribution of languages per model repo") | |
| linguality = st.selectbox( | |
| 'All or just Multilingual', | |
| ["All", "Just Multilingual", "Three or more languages"]) | |
| filter = 0 | |
| if linguality == "Just Multilingual": | |
| filter = 1 | |
| elif linguality == "Three or more languages": | |
| filter = 2 | |
| models_with_langs = data[data["repos_count"] > filter] | |
| df1 = models_with_langs['repos_count'].value_counts() | |
| st.bar_chart(df1) | |
| st.subheader("Distribution of repos per language") | |
| linguality_2 = st.selectbox( | |
| 'All or filtered', | |
| ["All", "No English", "Remove top 10"]) | |
| filter = 0 | |
| if linguality_2 == "All": | |
| filter = 0 | |
| elif linguality_2 == "No English": | |
| filter = 1 | |
| else: | |
| filter = 2 | |
| models_with_langs = data[data["repos_count"] > 0] | |
| langs = models_with_langs["languages"].explode() | |
| langs = langs[langs != {}] | |
| d = langs.value_counts().rename_axis("language").to_frame('counts').reset_index() | |
| if filter == 1: | |
| d = d.iloc[1:] | |
| elif filter == 2: | |
| d = d.iloc[10:] | |
| # Just keep top 25 to avoid vertical scroll | |
| d = d.iloc[:25] | |
| st.write(alt.Chart(d).mark_bar().encode( | |
| x='counts', | |
| y=alt.X('language', sort=None) | |
| )) | |
| st.subheader("Raw Data") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.dataframe(df1) | |
| with col2: | |
| d = langs.value_counts().rename_axis("language").to_frame('counts').reset_index() | |
| st.dataframe(d) | |
| with tab2: | |
| st.header("License info") | |
| no_license_count = data["license"].isna().sum() | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric(label="License Specified", value=total_samples-no_license_count) | |
| with col2: | |
| st.metric(label="No license Specified", value=no_license_count) | |
| with col3: | |
| st.metric(label="Total Unique Licenses", value=len(data["license"].unique())) | |
| st.subheader("Distribution of licenses per model repo") | |
| license_filter = st.selectbox( | |
| 'All or filtered', | |
| ["All", "No Apache 2.0", "Remove top 10"]) | |
| filter = 0 | |
| if license_filter == "All": | |
| filter = 0 | |
| elif license_filter == "No Apache 2.0": | |
| filter = 1 | |
| else: | |
| filter = 2 | |
| d = data["license"].value_counts().rename_axis("license").to_frame('counts').reset_index() | |
| if filter == 1: | |
| d = d.iloc[1:] | |
| elif filter == 2: | |
| d = d.iloc[10:] | |
| # Just keep top 25 to avoid vertical scroll | |
| d = d.iloc[:25] | |
| st.write(alt.Chart(d).mark_bar().encode( | |
| x='counts', | |
| y=alt.X('license', sort=None) | |
| )) | |
| st.text("There are some edge cases, as old repos using lists of licenses. We are working on fixing this.") | |
| st.subheader("Raw Data") | |
| d = data["license"].value_counts().rename_axis("license").to_frame('counts').reset_index() | |
| st.dataframe(d) | |
| with tab3: | |
| st.header("Pipeline info") | |
| no_pipeline_count = data["pipeline"].isna().sum() | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric(label="Pipeline Specified", value=total_samples-no_pipeline_count) | |
| with col2: | |
| st.metric(label="No pipeline Specified", value=no_pipeline_count) | |
| with col3: | |
| st.metric(label="Total Unique Pipelines", value=len(data["pipeline"].unique())) | |
| st.subheader("Distribution of pipelines per model repo") | |
| pipeline_filter = st.selectbox( | |
| 'All or filtered', | |
| ["All", "NLP", "CV", "Audio", "RL", "Multimodal", "Tabular"]) | |
| filter = 0 | |
| if pipeline_filter == "All": | |
| filter = 0 | |
| elif pipeline_filter == "NLP": | |
| filter = 1 | |
| elif pipeline_filter == "CV": | |
| filter = 2 | |
| elif pipeline_filter == "Audio": | |
| filter = 3 | |
| elif pipeline_filter == "RL": | |
| filter = 4 | |
| elif pipeline_filter == "Multimodal": | |
| filter = 5 | |
| elif pipeline_filter == "Tabular": | |
| filter = 6 | |
| d = data["pipeline"].value_counts().rename_axis("pipeline").to_frame('counts').reset_index() | |
| st.write(alt.Chart(d).mark_bar().encode( | |
| x='counts', | |
| y=alt.X('pipeline', sort=None) | |
| )) | |