---
language:
- de
- dgs
tags:
- sign-language-translation
- transformer
- SLT
datasets:
- rwth_phoenix_weather_2014_t
license: mit
---

# Transformer DGS Phoenix14t

A **minimal 3-layer Transformer** for Sign Language Translation (SLT), trained on **PHOENIX-2014T**. It translates video features (like S3D) into German text using the `google-bert/bert-base-german-cased` tokenizer.

## Intended Use & Performance
This model is intended for **educational purposes**. It demonstrates how to map continuous sign language embeddings to natural language sentences.

**Baseline Performance:**
* **~13 BLEU** using standard S3D features.
* **~18 BLEU** using features from [neccam/slt](https://github.com/neccam/slt).
  
## Quick Start

**Note:** This model uses custom code. You must use `trust_remote_code=True`.

```python
from transformers import AutoModel, AutoTokenizer

# Load Model & Tokenizer
model = AutoModel.from_pretrained("Shakibyzn/transformer-dgs-phoenix14t", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Shakibyzn/transformer-dgs-phoenix14t", trust_remote_code=True)
```

## Train and Inference
train.py and inference.py scripts are provided in the repository to demonstrate the train/test loop. This can be easily adapted for other sign language datasets or feature extractors (e.g., I3D/CLIP). It handles the loading of features and labels, and the standard training/validation loop.