Update decoder model card

README.md

@@ -0,0 +1,51 @@
+---
+language: en
+license: mit
+tags:
+  - sentiment-analysis
+  - text-classification
+  - decoder
+library_name: sentimentizer
+task: text-classification
+---
+# Sentimentizer DECODER Sentiment Model
+## Description
+
+A Transformer Encoder-Decoder for sentiment classification built on pre-trained GloVe embeddings. The encoder processes the input sequence, and the decoder attends to the encoder outputs to produce a sentiment prediction.
+
+## 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("decoder")
+download_weights(
+    "decoder",
+    weights_path,
+    repo_id="ryeyoo/sentimentizer-decoder",
+    dict_path=DriverConfig.files.dictionary_file_path,
+)
+
+# Load and run inference
+from sentimentizer.models.decoder import get_trained_model
+from sentimentizer.tokenizer import get_trained_tokenizer
+
+model = get_trained_model(device="cpu")
+tokenizer = get_trained_tokenizer()
+
+probs = model.predict_text('amazing food great service')
+for label, prob in sorted(probs.items(), key=lambda x: -x[1]):
+    print(f'{label}: {prob:.4f}')
+# e.g. positive: 0.8300, neutral: 0.1200, negative: 0.0500
+```
+
+## Files
+
+- `decoder_weights.pth` — Model state dictionary
+- `yelp.dictionary` — Gensim dictionary for tokenization