Add language and license info

README.md

@@ -5,7 +5,7 @@ colorFrom: indigo
 colorTo: blue
 sdk: streamlit
 sdk_version: 1.10.0
-app_file: app.py
+app_file: models.py
 pinned: false
 ---
 

models.py

@@ -0,0 +1,259 @@
+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)
+    ))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

requirements.txt

@@ -0,0 +1 @@
+datasets