--- license: cc0-1.0 --- **Note:** Due to nature of toxic comments data and code contain explicit language. Data is from kaggle, the *Toxic Comment Classification Challenge*
https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/data?select=train.csv.zip A copy of the data exists on the `data` directory. Trained over 20 epoch in a runpod ### 🤗 Running demo here: https://huggingface.co/spaces/vluz/Tox
Code requires pandas, tensorflow, and streamlit. All can be installed via `pip`. ```python import os import pickle import streamlit as st import tensorflow as tf from tensorflow.keras.layers import TextVectorization @st.cache_resource def load_model(): model = tf.keras.models.load_model(os.path.join("model", "toxmodel.keras")) return model @st.cache_resource def load_vectorizer(): from_disk = pickle.load(open(os.path.join("model", "vectorizer.pkl"), "rb")) new_v = TextVectorization.from_config(from_disk['config']) new_v.adapt(tf.data.Dataset.from_tensor_slices(["xyz"])) # fix for Keras bug new_v.set_weights(from_disk['weights']) return new_v st.title("Toxic Comment Test") st.divider() model = load_model() vectorizer = load_vectorizer() default_prompt = "i love you man, but fuck you!" input_text = st.text_area("Comment:", default_prompt, height=150).lower() if st.button("Test"): if not input_text: st.write("âš  Warning: Empty prompt.") elif len(input_text) < 15: st.write("âš  Warning: Model is far less accurate with a small prompt.") if input_text == default_prompt: st.write("Expected results from default prompt are positive for 0 and 2") with st.spinner("Testing..."): inputv = vectorizer([input_text]) output = model.predict(inputv) res = (output > 0.5) st.write(["toxic","severe toxic","obscene","threat","insult","identity hate"], res) st.write(output) ``` Put `toxmodel.keras` and `vectorizer.pkl` into the `model` dir. Then do: ``` stramlit run toxtest.py ``` Expected result from default prompt is 0 and 2
Full code can be found here:
https://github.com/vluz/ToxTest/