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
Download app.py from MishaD/FinetunedBertForSentimentClassification91: direct link, hf CLI and curl.
- Browser
- Download file 342 Bytes
-
https://huggingface.co/MishaD/FinetunedBertForSentimentClassification91/resolve/main/app.py
- Command line
-
hf download hf://MishaD/FinetunedBertForSentimentClassification91/app.py
-
curl -L -o app.py https://huggingface.co/MishaD/FinetunedBertForSentimentClassification91/resolve/main/app.py
342 Bytes
| 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") | |