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sentimentiq-weights
Rhumeet/sentimentiq-weights
sentimentiq-weights is a machine learning model from Rhumeet. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Trained weights for all 10 models from SentimentIQ — a benchmark comparing classical ML through fine-tuned BERT for 5-class sentiment prediction on 7 million Yelp reviews.
Access
Public
Updated Jul 26, 2026
Repo size
873 MB
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1
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Repo makeup
Click a slice to open those files.
.pt857 MB · 98%
At a glance
- License
- mit
- Access
- Public
- Created
- Jul 26, 2026
- Updated
- Jul 26, 2026
- SHA
- 5dcccfce
Model card
From the Hugging Face model README
SentimentIQ — Trained Model Weights
Trained weights for all 10 models from SentimentIQ — a benchmark comparing classical ML through fine-tuned BERT for 5-class sentiment prediction on 7 million Yelp reviews.
Files
| File | Model | Test Macro F1 |
|---|---|---|
BERT_finetuned.pt | Fine-tuned BERT (best overall) | 0.703 |
LSTM_multilayer.pt | Multi-layer bidirectional LSTM | 0.678 |
Seq2Seq_LuongAttention.pt | Seq2Seq + Luong attention | 0.662 |
Seq2Seq_NoAttention.pt | Seq2Seq, no attention | 0.661 |
GRU_multilayer.pt | Multi-layer bidirectional GRU | 0.661 |
Seq2Seq_BahdanauAttention.pt | Seq2Seq + Bahdanau attention | 0.660 |
LogisticRegression.pkl | TF-IDF + Logistic Regression | 0.641 |
RNN_multilayer.pt | Multi-layer bidirectional RNN | 0.637 |
LinearSVM.pkl | TF-IDF + Linear SVM | 0.631 |
NaiveBayes.pkl | TF-IDF + Multinomial Naive Bayes | 0.559 |
Usage
.pt files are PyTorch state_dict checkpoints — load them into the corresponding model class defined in the training notebook. .pkl files are pickled scikit-learn estimators, loadable directly via pickle.load().
Full Project
See the SentimentIQ GitHub repository for the complete pipeline: EDA, preprocessing, training code, TOPSIS-based model ranking, and error analysis.
- License
- mit
- Created
- Jul 26, 2026
- Updated
- Jul 26, 2026