diff --git a/app.py b/app.py
index 9c035b9dbca074487409c07c8cb8c4dd2dea6777..3c48186ac0452ca2328965daa5ca5ff25a4e6f48 100644
--- a/app.py
+++ b/app.py
@@ -122,6 +122,12 @@ def load_or_prepare(ds_args, show_embeddings, use_cache=False):
dstats.load_or_prepare_zipf()
return dstats
+@st.cache(
+ hash_funcs={
+ dataset_statistics.DatasetStatisticsCacheClass: lambda dstats: dstats.cache_path
+ },
+ allow_output_mutation=True,
+)
def load_or_prepare_widgets(ds_args, show_embeddings, use_cache=False):
"""
Loader specifically for the widgets used in the app.
@@ -144,6 +150,8 @@ def load_or_prepare_widgets(ds_args, show_embeddings, use_cache=False):
dstats = dataset_statistics.DatasetStatisticsCacheClass(CACHE_DIR, **ds_args, use_cache=use_cache)
# Don't recalculate; we're live
dstats.set_deployment(True)
+ # We need to have the text_dset loaded for further load_or_prepare
+ dstats.load_or_prepare_dataset()
# Header widget
dstats.load_or_prepare_dset_peek()
# General stats widget
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+++ b/cache_dir/wikitext_wikitext-2-v1_validation_text/sorted_top_vocab.feather
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a85ea8b3c5d0360eb2b7bd9bba28b8bf91c882424b46d07e4e2fb5c474df7d72
+size 3818
diff --git a/cache_dir/wikitext_wikitext-2-v1_validation_text/zipf_basic_stats.json b/cache_dir/wikitext_wikitext-2-v1_validation_text/zipf_basic_stats.json
new file mode 100644
index 0000000000000000000000000000000000000000..b167ba1875e073126c1f169d51d13baa4522b62a
--- /dev/null
+++ b/cache_dir/wikitext_wikitext-2-v1_validation_text/zipf_basic_stats.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b18bf976d91bfbf662f1c061e9d3f3f1f094c31d4e001c0e8d599c92f3e0606
+size 2260
diff --git a/cache_dir/wikitext_wikitext-2-v1_validation_text/zipf_fig.json b/cache_dir/wikitext_wikitext-2-v1_validation_text/zipf_fig.json
new file mode 100644
index 0000000000000000000000000000000000000000..de883291f3d9be58246c4f744937dfbec78020e5
--- /dev/null
+++ b/cache_dir/wikitext_wikitext-2-v1_validation_text/zipf_fig.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b74bb85c654a73e8878f35ab223dabb74f3645ebcb9905d9b79f6f9e4316d64c
+size 203901
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diff --git a/data_measurements/dataset_statistics.py b/data_measurements/dataset_statistics.py
index 5596d37cac61f99f8f844bb8bde15b8ace38d6a0..ef6ccfe30b64851dacfe0284c601a13571bcb3c8 100644
--- a/data_measurements/dataset_statistics.py
+++ b/data_measurements/dataset_statistics.py
@@ -15,11 +15,12 @@
import json
import logging
import statistics
-import torch
from os import mkdir
from os.path import exists, isdir
from os.path import join as pjoin
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
import nltk
import numpy as np
import pandas as pd
@@ -28,30 +29,17 @@ import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objects as go
import pyarrow.feather as feather
-import matplotlib.pyplot as plt
import seaborn as sns
+import torch
from datasets import load_from_disk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
-from .dataset_utils import (
-TOT_WORDS,
-TOT_OPEN_WORDS,
- CNT,
- DEDUP_TOT,
- EMBEDDING_FIELD,
- LENGTH_FIELD,
- OUR_LABEL_FIELD,
- OUR_TEXT_FIELD,
- PROP,
- TEXT_NAN_CNT,
- TOKENIZED_FIELD,
- TXT_LEN,
- VOCAB,
- WORD,
- extract_field,
- load_truncated_dataset,
-)
+from .dataset_utils import (CNT, DEDUP_TOT, EMBEDDING_FIELD, LENGTH_FIELD,
+ OUR_LABEL_FIELD, OUR_TEXT_FIELD, PROP,
+ TEXT_NAN_CNT, TOKENIZED_FIELD, TOT_OPEN_WORDS,
+ TOT_WORDS, TXT_LEN, VOCAB, WORD, extract_field,
+ load_truncated_dataset)
from .embeddings import Embeddings
from .npmi import nPMI
from .zipf import Zipf
@@ -150,6 +138,7 @@ _NUM_VOCAB_BATCHES = 2000
_TOP_N = 100
_CVEC = CountVectorizer(token_pattern="(?u)\\b\\w+\\b", lowercase=True)
+
class DatasetStatisticsCacheClass:
def __init__(
self,
@@ -248,13 +237,13 @@ class DatasetStatisticsCacheClass:
# path to the directory used for caching
if not isinstance(text_field, str):
text_field = "-".join(text_field)
- #if isinstance(label_field, str):
+ # if isinstance(label_field, str):
# label_field = label_field
- #else:
+ # else:
# label_field = "-".join(label_field)
self.cache_path = pjoin(
self.cache_dir,
- f"{dset_name}_{dset_config}_{split_name}_{text_field}", #{label_field},
+ f"{dset_name}_{dset_config}_{split_name}_{text_field}", # {label_field},
)
if not isdir(self.cache_path):
logs.warning("Creating cache directory %s." % self.cache_path)
@@ -289,8 +278,9 @@ class DatasetStatisticsCacheClass:
# Needed for UI
self.general_stats_json_fid = pjoin(self.cache_path, "general_stats_dict.json")
# Needed for UI
- self.sorted_top_vocab_df_fid = pjoin(self.cache_path,
- "sorted_top_vocab.feather")
+ self.sorted_top_vocab_df_fid = pjoin(
+ self.cache_path, "sorted_top_vocab.feather"
+ )
## Zipf cache files
# Needed for UI
self.zipf_fid = pjoin(self.cache_path, "zipf_basic_stats.json")
@@ -341,18 +331,17 @@ class DatasetStatisticsCacheClass:
and exists(self.dup_counts_df_fid)
and exists(self.sorted_top_vocab_df_fid)
):
- logs.info('Loading cached general stats')
+ logs.info("Loading cached general stats")
self.load_general_stats()
else:
if not self.live:
- logs.info('Preparing general stats')
+ logs.info("Preparing general stats")
self.prepare_general_stats()
if save:
write_df(self.sorted_top_vocab_df, self.sorted_top_vocab_df_fid)
write_df(self.dup_counts_df, self.dup_counts_df_fid)
write_json(self.general_stats_dict, self.general_stats_json_fid)
-
def load_or_prepare_text_lengths(self, save=True):
"""
The text length widget relies on this function, which provides
@@ -364,8 +353,8 @@ class DatasetStatisticsCacheClass:
"""
# Text length figure
- if (self.use_cache and exists(self.fig_tok_length_fid)):
- self.fig_tok_length = plt.imread(self.fig_tok_length_fid)
+ if self.use_cache and exists(self.fig_tok_length_fid):
+ self.fig_tok_length_png = mpimg.imread(self.fig_tok_length_fid)
else:
if not self.live:
self.prepare_fig_text_lengths()
@@ -397,51 +386,48 @@ class DatasetStatisticsCacheClass:
if not self.live:
if self.tokenized_df is None:
self.tokenized_df = self.do_tokenization()
- self.tokenized_df[LENGTH_FIELD] = self.tokenized_df[
- TOKENIZED_FIELD].apply(len)
- self.length_df = self.tokenized_df[
- [LENGTH_FIELD, OUR_TEXT_FIELD]].sort_values(
- by=[LENGTH_FIELD], ascending=True
+ self.tokenized_df[LENGTH_FIELD] = self.tokenized_df[TOKENIZED_FIELD].apply(
+ len
)
+ self.length_df = self.tokenized_df[
+ [LENGTH_FIELD, OUR_TEXT_FIELD]
+ ].sort_values(by=[LENGTH_FIELD], ascending=True)
def prepare_text_length_stats(self):
if not self.live:
- if self.tokenized_df is None or LENGTH_FIELD not in self.tokenized_df.columns or self.length_df is None:
+ if (
+ self.tokenized_df is None
+ or LENGTH_FIELD not in self.tokenized_df.columns
+ or self.length_df is None
+ ):
self.prepare_length_df()
- avg_length = sum(self.tokenized_df[LENGTH_FIELD])/len(self.tokenized_df[LENGTH_FIELD])
+ avg_length = sum(self.tokenized_df[LENGTH_FIELD]) / len(
+ self.tokenized_df[LENGTH_FIELD]
+ )
self.avg_length = round(avg_length, 1)
std_length = statistics.stdev(self.tokenized_df[LENGTH_FIELD])
self.std_length = round(std_length, 1)
self.num_uniq_lengths = len(self.length_df["length"].unique())
- self.length_stats_dict = {"avg length": self.avg_length,
- "std length": self.std_length,
- "num lengths": self.num_uniq_lengths}
+ self.length_stats_dict = {
+ "avg length": self.avg_length,
+ "std length": self.std_length,
+ "num lengths": self.num_uniq_lengths,
+ }
def prepare_fig_text_lengths(self):
if not self.live:
- if self.tokenized_df is None or LENGTH_FIELD not in self.tokenized_df.columns:
+ if (
+ self.tokenized_df is None
+ or LENGTH_FIELD not in self.tokenized_df.columns
+ ):
self.prepare_length_df()
self.fig_tok_length = make_fig_lengths(self.tokenized_df, LENGTH_FIELD)
- def load_or_prepare_embeddings(self, save=True):
- if self.use_cache and exists(self.node_list_fid) and exists(self.fig_tree_json_fid):
- self.node_list = torch.load(self.node_list_fid)
- self.fig_tree = read_plotly(self.fig_tree_json_fid)
- elif self.use_cache and exists(self.node_list_fid):
- self.node_list = torch.load(self.node_list_fid)
- self.fig_tree = make_tree_plot(self.node_list,
- self.text_dset)
- if save:
- write_plotly(self.fig_tree, self.fig_tree_json_fid)
- else:
- self.embeddings = Embeddings(self, use_cache=self.use_cache)
- self.embeddings.make_hierarchical_clustering()
- self.node_list = self.embeddings.node_list
- self.fig_tree = make_tree_plot(self.node_list,
- self.text_dset)
- if save:
- torch.save(self.node_list, self.node_list_fid)
- write_plotly(self.fig_tree, self.fig_tree_json_fid)
+ def load_or_prepare_embeddings(self):
+ self.embeddings = Embeddings(self, use_cache=self.use_cache)
+ self.embeddings.make_hierarchical_clustering()
+ self.node_list = self.embeddings.node_list
+ self.fig_tree = self.embeddings.fig_tree
# get vocab with word counts
def load_or_prepare_vocab(self, save=True):
@@ -451,10 +437,7 @@ class DatasetStatisticsCacheClass:
:param
:return:
"""
- if (
- self.use_cache
- and exists(self.vocab_counts_df_fid)
- ):
+ if self.use_cache and exists(self.vocab_counts_df_fid):
logs.info("Reading vocab from cache")
self.load_vocab()
self.vocab_counts_filtered_df = filter_vocab(self.vocab_counts_df)
@@ -501,7 +484,9 @@ class DatasetStatisticsCacheClass:
write_df(self.dup_counts_df, self.dup_counts_df_fid)
def load_general_stats(self):
- self.general_stats_dict = json.load(open(self.general_stats_json_fid, encoding="utf-8"))
+ self.general_stats_dict = json.load(
+ open(self.general_stats_json_fid, encoding="utf-8")
+ )
with open(self.sorted_top_vocab_df_fid, "rb") as f:
self.sorted_top_vocab_df = feather.read_feather(f)
self.text_nan_count = self.general_stats_dict[TEXT_NAN_CNT]
@@ -536,8 +521,7 @@ class DatasetStatisticsCacheClass:
if not self.live:
if self.tokenized_df is None:
self.load_or_prepare_tokenized_df()
- dup_df = self.tokenized_df[
- self.tokenized_df.duplicated([OUR_TEXT_FIELD])]
+ dup_df = self.tokenized_df[self.tokenized_df.duplicated([OUR_TEXT_FIELD])]
self.dup_counts_df = pd.DataFrame(
dup_df.pivot_table(
columns=[OUR_TEXT_FIELD], aggfunc="size"
@@ -577,7 +561,7 @@ class DatasetStatisticsCacheClass:
write_json({"dset peek": self.dset_peek}, self.dset_peek_json_fid)
def load_or_prepare_tokenized_df(self, save=True):
- if (self.use_cache and exists(self.tokenized_df_fid)):
+ if self.use_cache and exists(self.tokenized_df_fid):
self.tokenized_df = feather.read_feather(self.tokenized_df_fid)
else:
if not self.live:
@@ -589,7 +573,7 @@ class DatasetStatisticsCacheClass:
write_df(self.tokenized_df, self.tokenized_df_fid)
def load_or_prepare_text_dset(self, save=True):
- if (self.use_cache and exists(self.text_dset_fid)):
+ if self.use_cache and exists(self.text_dset_fid):
# load extracted text
self.text_dset = load_from_disk(self.text_dset_fid)
logs.warning("Loaded dataset from disk")
@@ -813,7 +797,13 @@ class nPMIStatisticsCacheClass:
# When everything is already computed for the selected subgroups.
logs.info("Loading cached joint npmi")
joint_npmi_df = self.load_joint_npmi_df(joint_npmi_fid)
- npmi_display_cols = ['npmi-bias', subgroup1 + '-npmi', subgroup2 + '-npmi', subgroup1 + '-count', subgroup2 + '-count']
+ npmi_display_cols = [
+ "npmi-bias",
+ subgroup1 + "-npmi",
+ subgroup2 + "-npmi",
+ subgroup1 + "-count",
+ subgroup2 + "-count",
+ ]
joint_npmi_df = joint_npmi_df[npmi_display_cols]
# When maybe some things have been computed for the selected subgroups.
else:
@@ -972,9 +962,11 @@ class nPMIStatisticsCacheClass:
def get_available_terms(self):
return self.load_or_prepare_npmi_terms()
+
def dummy(doc):
return doc
+
def count_vocab_frequencies(tokenized_df):
"""
Based on an input pandas DataFrame with a 'text' column,
@@ -989,7 +981,9 @@ def count_vocab_frequencies(tokenized_df):
)
# We do this to calculate per-word statistics
# Fast calculation of single word counts
- logs.info("Fitting dummy tokenization to make matrix using the previous tokenization")
+ logs.info(
+ "Fitting dummy tokenization to make matrix using the previous tokenization"
+ )
cvec.fit(tokenized_df[TOKENIZED_FIELD])
document_matrix = cvec.transform(tokenized_df[TOKENIZED_FIELD])
batches = np.linspace(0, tokenized_df.shape[0], _NUM_VOCAB_BATCHES).astype(int)
@@ -1010,6 +1004,7 @@ def count_vocab_frequencies(tokenized_df):
word_count_df.index.name = WORD
return word_count_df
+
def calc_p_word(word_count_df):
# p(word)
word_count_df[PROP] = word_count_df[CNT] / float(sum(word_count_df[CNT]))
@@ -1020,8 +1015,7 @@ def calc_p_word(word_count_df):
def filter_vocab(vocab_counts_df):
# TODO: Add warnings (which words are missing) to log file?
- filtered_vocab_counts_df = vocab_counts_df.drop(_CLOSED_CLASS,
- errors="ignore")
+ filtered_vocab_counts_df = vocab_counts_df.drop(_CLOSED_CLASS, errors="ignore")
filtered_count = filtered_vocab_counts_df[CNT]
filtered_count_denom = float(sum(filtered_vocab_counts_df[CNT]))
filtered_vocab_counts_df[PROP] = filtered_count / filtered_count_denom
@@ -1030,19 +1024,23 @@ def filter_vocab(vocab_counts_df):
## Figures ##
+
def write_plotly(fig, fid):
write_json(plotly.io.to_json(fig), fid)
+
def read_plotly(fid):
fig = plotly.io.from_json(json.load(open(fid, encoding="utf-8")))
return fig
+
def make_fig_lengths(tokenized_df, length_field):
fig_tok_length, axs = plt.subplots(figsize=(15, 6), dpi=150)
sns.histplot(data=tokenized_df[length_field], kde=True, bins=100, ax=axs)
sns.rugplot(data=tokenized_df[length_field], ax=axs)
return fig_tok_length
+
def make_fig_labels(label_df, label_names, label_field):
labels = label_df[label_field].unique()
label_sums = [len(label_df[label_df[label_field] == label]) for label in labels]
@@ -1123,89 +1121,6 @@ def make_zipf_fig(vocab_counts_df, z):
return fig
-def make_tree_plot(node_list, text_dset):
- nid_map = dict([(node["nid"], nid) for nid, node in enumerate(node_list)])
-
- for nid, node in enumerate(node_list):
- node["label"] = node.get(
- "label",
- f"{nid:2d} - {node['weight']:5d} items
"
- + "
".join(
- [
- "> " + txt[:64] + ("..." if len(txt) >= 63 else "")
- for txt in list(
- set(text_dset.select(node["example_ids"])[OUR_TEXT_FIELD])
- )[:5]
- ]
- ),
- )
-
- # make plot nodes
- # TODO: something more efficient than set to remove duplicates
- labels = [node["label"] for node in node_list]
-
- root = node_list[0]
- root["X"] = 0
- root["Y"] = 0
-
- def rec_make_coordinates(node):
- total_weight = 0
- add_weight = len(node["example_ids"]) - sum(
- [child["weight"] for child in node["children"]]
- )
- for child in node["children"]:
- child["X"] = node["X"] + total_weight
- child["Y"] = node["Y"] - 1
- total_weight += child["weight"] + add_weight / len(node["children"])
- rec_make_coordinates(child)
-
- rec_make_coordinates(root)
-
- E = [] # list of edges
- Xn = []
- Yn = []
- Xe = []
- Ye = []
- for nid, node in enumerate(node_list):
- Xn += [node["X"]]
- Yn += [node["Y"]]
- for child in node["children"]:
- E += [(nid, nid_map[child["nid"]])]
- Xe += [node["X"], child["X"], None]
- Ye += [node["Y"], child["Y"], None]
-
- # make figure
- fig = go.Figure()
- fig.add_trace(
- go.Scatter(
- x=Xe,
- y=Ye,
- mode="lines",
- line=dict(color="rgb(210,210,210)", width=1),
- hoverinfo="none",
- )
- )
- fig.add_trace(
- go.Scatter(
- x=Xn,
- y=Yn,
- mode="markers",
- name="nodes",
- marker=dict(
- symbol="circle-dot",
- size=18,
- color="#6175c1",
- line=dict(color="rgb(50,50,50)", width=1)
- # '#DB4551',
- ),
- text=labels,
- hoverinfo="text",
- opacity=0.8,
- )
- )
- return fig
-
-
## Input/Output ###
@@ -1259,6 +1174,7 @@ def write_json(json_dict, json_fid):
with open(json_fid, "w", encoding="utf-8") as f:
json.dump(json_dict, f)
+
def write_subgroup_npmi_data(subgroup, subgroup_dict, subgroup_files):
"""
Saves the calculated nPMI statistics to their output files.
@@ -1278,6 +1194,7 @@ def write_subgroup_npmi_data(subgroup, subgroup_dict, subgroup_files):
with open(subgroup_cooc_fid, "w+") as f:
subgroup_cooc_df.to_csv(f)
+
def write_zipf_data(z, zipf_fid):
zipf_dict = {}
zipf_dict["xmin"] = int(z.xmin)
diff --git a/data_measurements/embeddings.py b/data_measurements/embeddings.py
index e20989923997f629f8fb27196825dbac875f3419..2d3e9e7330732b67b6b1c9f5b226cd4317a4e4b6 100644
--- a/data_measurements/embeddings.py
+++ b/data_measurements/embeddings.py
@@ -146,11 +146,12 @@ class Embeddings:
[(node["nid"], nid) for nid, node in enumerate(self.node_list)]
)
torch.save((self.node_list, self.nid_map), self.node_list_fid)
+ print(exists(self.fig_tree_fid), self.fig_tree_fid)
if self.use_cache and exists(self.fig_tree_fid):
self.fig_tree = read_json(self.fig_tree_fid)
else:
self.fig_tree = make_tree_plot(
- self.node_list, self.text_dset, self.text_field_name
+ self.node_list, self.nid_map, self.text_dset, self.text_field_name
)
self.fig_tree.write_json(self.fig_tree_fid)
@@ -460,14 +461,12 @@ def fast_cluster(
return node_list
-def make_tree_plot(node_list, text_dset, text_field_name):
+def make_tree_plot(node_list, nid_map, text_dset, text_field_name):
"""
Makes a graphical representation of the tree encoded
in node-list. The hover label for each node shows the number
of descendants and the 5 examples that are closest to the centroid
"""
- nid_map = dict([(node["nid"], nid) for nid, node in enumerate(node_list)])
-
for nid, node in enumerate(node_list):
# get list of
node_examples = {}
diff --git a/data_measurements/npmi.py b/data_measurements/npmi.py
index 66ee424e9d9c05efbcd931e37f10b6b0f8cc1961..0c740b031c702a83afc7252d47b6b0185bcc39e1 100644
--- a/data_measurements/npmi.py
+++ b/data_measurements/npmi.py
@@ -34,7 +34,7 @@ logs.propagate = False
if not logs.handlers:
- Path('./log_files').mkdir(exist_ok=True)
+ Path("./log_files").mkdir(exist_ok=True)
# Logging info to log file
file = logging.FileHandler("./log_files/npmi.log")
diff --git a/data_measurements/streamlit_utils.py b/data_measurements/streamlit_utils.py
index 07a3d36578bdffdedcdbe3c32cbb3aef503920e4..b1cf0929507ddfe96a354a1dbc1ee113d0348630 100644
--- a/data_measurements/streamlit_utils.py
+++ b/data_measurements/streamlit_utils.py
@@ -108,9 +108,7 @@ def expander_general_stats(dstats, column_id):
"Use this widget to check whether the terms you see most represented"
" in the dataset make sense for the goals of the dataset."
)
- st.markdown(
- "There are {0} total words".format(str(dstats.total_words))
- )
+ st.markdown("There are {0} total words".format(str(dstats.total_words)))
st.markdown(
"There are {0} words after removing closed "
"class words".format(str(dstats.total_open_words))
@@ -130,14 +128,10 @@ def expander_general_stats(dstats, column_id):
st.markdown(
"There are {0} duplicate items in the dataset. "
"For more information about the duplicates, "
- "click the 'Duplicates' tab below.".format(
- str(dstats.dedup_total)
- )
+ "click the 'Duplicates' tab below.".format(str(dstats.dedup_total))
)
else:
- st.markdown(
- "There are 0 duplicate items in the dataset. ")
-
+ st.markdown("There are 0 duplicate items in the dataset. ")
### Show the label distribution from the datasets
@@ -167,7 +161,14 @@ def expander_text_lengths(dstats, column_id):
st.markdown(
"### Here is the relative frequency of different text lengths in your dataset:"
)
+<<<<<<< HEAD
st.pyplot(dstats.fig_tok_length, use_container_width=True)
+=======
+ try:
+ st.image(dstats.fig_tok_length_png)
+ except:
+ st.pyplot(dstats.fig_tok_length, use_container_width=True)
+>>>>>>> a0a4b0702daf66f24d3114997945e3d42906bc95
st.markdown(
"The average length of text instances is **"
+ str(dstats.avg_length)
@@ -178,8 +179,21 @@ def expander_text_lengths(dstats, column_id):
# This is quite a large file and is breaking our ability to navigate the app development.
# Just passing if it's not already there for launch v0
if dstats.length_df is not None:
+<<<<<<< HEAD
start_id_show_lengths= st.selectbox("Show examples of length:", sorted(dstats.length_df["length"].unique().tolist()))
st.table(dstats.length_df[dstats.length_df["length"] == start_id_show_lengths].set_index("length"))
+=======
+ start_id_show_lengths = st.selectbox(
+ "Show examples of length:",
+ sorted(dstats.length_df["length"].unique().tolist()),
+ key=f"select_show_length_{column_id}",
+ )
+ st.table(
+ dstats.length_df[
+ dstats.length_df["length"] == start_id_show_lengths
+ ].set_index("length")
+ )
+>>>>>>> a0a4b0702daf66f24d3114997945e3d42906bc95
### Third, use a sentence embedding model
diff --git a/data_measurements/zipf.py b/data_measurements/zipf.py
index 61808cb8a45dbf04e3383d17208dc81873db6dc8..843ee00254f90eb86e10974d5c4019e696246d14 100644
--- a/data_measurements/zipf.py
+++ b/data_measurements/zipf.py
@@ -34,7 +34,7 @@ logs.propagate = False
if not logs.handlers:
- Path('./log_files').mkdir(exist_ok=True)
+ Path("./log_files").mkdir(exist_ok=True)
# Logging info to log file
file = logging.FileHandler("./log_files/zipf.log")