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language: en
license: mit
tags:
- sentiment-analysis
- text-classification
- encoder
library_name: sentimentizer
task: text-classification
---
# Sentimentizer ENCODER Sentiment Model
## Description
A Transformer Encoder for sentiment classification built on pre-trained GloVe embeddings. The model uses multi-head self-attention with positional encodings and a classification token (CLS) to produce a sentiment score.
## Training Data
Trained on the [Yelp Open Dataset](https://www.yelp.com/dataset) reviews, with GloVe Wiki-Gigaword-100 pre-trained embeddings. Reviews are tokenized with a custom dictionary (20k vocab, min frequency 3) and padded/truncated to 200 tokens.
## Usage
```python
from sentimentizer.hf import download_weights
from sentimentizer.config import DriverConfig, weights_path_for
# Download weights + dictionary from Hugging Face Hub
weights_path = weights_path_for("encoder")
download_weights(
"encoder",
weights_path,
dict_path=DriverConfig.files.dictionary_file_path,
)
# Load and run inference
from sentimentizer.models.encoder import get_trained_model
from sentimentizer.tokenizer import get_trained_tokenizer
model = get_trained_model(device="cpu")
tokenizer = get_trained_tokenizer()
import numpy as np
token_ids = tokenizer.tokenize_text("amazing food great service")
score = model.predict(token_ids)
print(f'Sentiment score: {score.item():.4f}') # >0.5 = positive, <0.5 = negative
```
## Files
- `encoder_weights.pth` — Model state dictionary
- `yelp.dictionary` — Gensim dictionary for tokenization
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