Instructions to use MishaD/FinetunedBertForSentimentClassification91 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use MishaD/FinetunedBertForSentimentClassification91 with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("MishaD/FinetunedBertForSentimentClassification91", set_active=True) - Notebooks
- Google Colab
- Kaggle
File size: 342 Bytes
273d5d2 | 1 2 3 4 5 6 7 8 9 10 | import streamlit as st
from model import classify_text
st.markdown("### Sentiment classification (negative vs. positive)")
title = st.text_area("Your sentiment for classification", "I would not use it")
if st.button("Classify!"):
prob = float(classify_text(title))
st.markdown(f"**Model**: {round(prob, 5)}% of being positive")
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