Instructions to use dh-unibe/trocr-medieval-escriptmask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dh-unibe/trocr-medieval-escriptmask with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="dh-unibe/trocr-medieval-escriptmask")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("dh-unibe/trocr-medieval-escriptmask")
model = AutoModelForMultimodalLM.from_pretrained("dh-unibe/trocr-medieval-escriptmask", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dh-unibe/trocr-medieval-escriptmask with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dh-unibe/trocr-medieval-escriptmask"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dh-unibe/trocr-medieval-escriptmask",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dh-unibe/trocr-medieval-escriptmask

SGLang

How to use dh-unibe/trocr-medieval-escriptmask 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 "dh-unibe/trocr-medieval-escriptmask" \
    --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": "dh-unibe/trocr-medieval-escriptmask",
		"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 "dh-unibe/trocr-medieval-escriptmask" \
        --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": "dh-unibe/trocr-medieval-escriptmask",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dh-unibe/trocr-medieval-escriptmask with Docker Model Runner:
```
docker model run hf.co/dh-unibe/trocr-medieval-escriptmask
```

EScriptorium)

Handwritten Text Recognition model for Medieval Scripts.

Part of the developments at the Digital Humanities@University of Bern. Developed by Jonas Widmer and Tobias Hodel based on different ground truth (see below).

Base model: microsoft/trocr-base-handwritten

Epochs: 19.05 / 20
Eval CER: 0.0329

This is a combined model of ground truth of different charter and book scripts from a variety of projects and institutions, aiming at building a generic model for Latin scripts of the Middle Ages. It is mainly based on documents from the project CREMMA Manuscrits médiévaux latins, HIMANIS (CNRS), Itinera Nova (Stadsarchief Leuven), and Charters and Records of Königsfelden (Universität Zürich).

Based on the following data: CREMMA Manuscrits médiévaux latins has been produced by Clérice, Thibault and Chagué, Alix and Vlachou Efstathiou, Malamatenia. It is licensed under a CC-BY 4.0 license. URL: https://github.com/HTR-United/CREMMA-Medieval-LAT

HIMANIS is partially published as HIMANIS Guérin produced by Stutzmann, Dominique; Hamel, Sébastien; Kernier, Iseut de; Mühlberger, Günter; Hackl, Günter. Licensed under a CC-BY 4.0 license. DOI: 10.5281/zenodo.5535306

Charters and Records of Königsfelden Abbey and Bailiwick (1308-1662) has been produced by Halter-Pernet, Colette; Teuscher, Simon; Hodel, Tobias; Barwitzki, Lukas; Egloff, Salome; Henggeler, Fabian; Nadig, Michael; Steinmann, Anina; Stettler, Sabine; Prada Ziegler, Ismail. Licensed under a CC-BY 4.0 license. DOI: 10.5281/zenodo.5179361

The model is based on the same data as the following PyLaia model (available on Transkribus): https://readcoop.eu/model/charter-scripts-german-latin-french/

The model has not been extensively tested. Potential biases are still to be identified.

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Model size

0.3B params

Tensor type

F32

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