File size: 7,502 Bytes
de86128 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 |
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)
))
|