Instructions to use mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh
- SGLang
How to use mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh with Docker Model Runner:
docker model run hf.co/mrapacz/interlinear-pl-mt5-large-emb-concat-diacritics-bh
| license: cc-by-sa-4.0 | |
| language: | |
| - pl | |
| metrics: | |
| - bleu | |
| base_model: | |
| - mT5-large | |
| library_name: transformers | |
| datasets: | |
| - mrapacz/greek-interlinear-translations | |
| # Model Card for Ancient Greek to Polish Interlinear Translation Model | |
| This model performs interlinear translation from Ancient Greek to {Language}, maintaining word-level alignment between source and target texts. | |
| ## Model Details | |
| ### Model Description | |
| - **Developed By:** Maciej Rapacz, AGH University of Kraków | |
| - **Model Type:** Neural machine translation (T5-based) | |
| - **Base Model:** mT5-large | |
| - **Tokenizer:** mT5 | |
| - **Language(s):** Ancient Greek (source) → Polish (target) | |
| - **License:** CC BY-NC-SA 4.0 | |
| - **Tag Set:** BH (Bible Hub) | |
| - **Text Preprocessing:** Diacritics | |
| - **Morphological Encoding:** emb-concat | |
| ### Model Performance | |
| - **BLEU Score:** 0.57 | |
| - **SemScore:** 0.68 | |
| ### Model Sources | |
| - **Repository:** https://github.com/mrapacz/loreslm-interlinear-translation | |
| - **Paper:** https://aclanthology.org/2025.loreslm-1.11/ | |