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# # Here we are gonns do zero shot text classification using llm
# %%
import pandas as pd
books = pd.read_csv("books_cleaned.csv")
# %%
books["categories"].value_counts().reset_index()
# %%
books["categories"].value_counts().reset_index().query("count > 50")
# %%
books[books["categories"] == "Juvenile Fiction"]
# %%
books[books["categories"] == "Juvenile Nonfiction"]
# %%
category_mapping = {'Fiction' : "Fiction",
'Juvenile Fiction': "Children's Fiction",
'Biography & Autobiography': "Nonfiction",
'History': "Nonfiction",
'Literary Criticism': "Nonfiction",
'Philosophy': "Nonfiction",
'Religion': "Nonfiction",
'Comics & Graphic Novels': "Fiction",
'Drama': "Fiction",
'Juvenile Nonfiction': "Children's Nonfiction",
'Science': "Nonfiction",
'Poetry': "Fiction"}
books["simple_categories"] = books["categories"].map(category_mapping)
# %%
books
# %%
from transformers import pipeline
# %%
fiction_categories = ["Fiction", "Nonfiction"]
pipe = pipeline("zero-shot-classification",
model="facebook/bart-large-mnli",
device=0) # use your first CUDA GPU
# %%
sequence = books.loc[books["simple_categories"] == "Fiction", "description"].reset_index(drop=True)[0]
# %%
pipe(sequence, fiction_categories)
# %%
import numpy as np
max_index = np.argmax(pipe(sequence, fiction_categories)["scores"])
max_label = pipe(sequence, fiction_categories)["labels"][max_index]
max_label
# %%
def generate_predictions(sequence, categories):
predictions = pipe(sequence, categories)
max_index = np.argmax(predictions["scores"])
max_label = predictions["labels"][max_index]
return max_label
# %%
from tqdm import tqdm
actual_cats = []
predicted_cats = []
for i in tqdm(range(0, 300)):
sequence = books.loc[books["simple_categories"] == "Fiction", "description"].reset_index(drop=True)[i]
predicted_cats += [generate_predictions(sequence, fiction_categories)]
actual_cats += ["Fiction"]
# %%
for i in tqdm(range(0, 300)):
sequence = books.loc[books["simple_categories"] == "Nonfiction", "description"].reset_index(drop=True)[i]
predicted_cats += [generate_predictions(sequence, fiction_categories)]
actual_cats += ["Nonfiction"]
# %%
predictions_df = pd.DataFrame({"actual_categories": actual_cats, "predicted_categories": predicted_cats})
predictions_df
# %%
predictions_df["correct_prediction"] = (
np.where(predictions_df["actual_categories"] == predictions_df["predicted_categories"], 1, 0)
)
# %%
predictions_df["correct_prediction"].sum() / len(predictions_df)
# %%
isbns = []
predicted_cats = []
missing_cats = books.loc[books["simple_categories"].isna(), ["isbn13", "description"]].reset_index(drop=True)
# %%
for i in tqdm(range(0, len(missing_cats))):
sequence = missing_cats["description"][i]
predicted_cats += [generate_predictions(sequence, fiction_categories)]
isbns += [missing_cats["isbn13"][i]]
# %%
missing_predicted_df = pd.DataFrame({"isbn13": isbns, "predicted_categories": predicted_cats})
# %%
missing_predicted_df
# %%
books = pd.merge(books, missing_predicted_df, on="isbn13", how="left")
books["simple_categories"] = np.where(books["simple_categories"].isna(), books["predicted_categories"], books["simple_categories"])
books = books.drop(columns = ["predicted_categories"])
# %%
books
# %%
books[books["categories"].str.lower().isin([
"romance",
"science fiction",
"scifi",
"fantasy",
"horror",
"mystery",
"thriller",
"comedy",
"crime",
"historical"
])]
# %%
books.to_csv("books_with_categories.csv", index=False)
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