Instructions to use DEplain/trimmed_longmbart_docs_apa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DEplain/trimmed_longmbart_docs_apa with Transformers:

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

pipe = pipeline("text-generation", model="DEplain/trimmed_longmbart_docs_apa")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("DEplain/trimmed_longmbart_docs_apa")
model = AutoModelForSeq2SeqLM.from_pretrained("DEplain/trimmed_longmbart_docs_apa")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DEplain/trimmed_longmbart_docs_apa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DEplain/trimmed_longmbart_docs_apa

SGLang

How to use DEplain/trimmed_longmbart_docs_apa 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 "DEplain/trimmed_longmbart_docs_apa" \
    --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": "DEplain/trimmed_longmbart_docs_apa",
		"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 "DEplain/trimmed_longmbart_docs_apa" \
        --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": "DEplain/trimmed_longmbart_docs_apa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DEplain/trimmed_longmbart_docs_apa with Docker Model Runner:
```
docker model run hf.co/DEplain/trimmed_longmbart_docs_apa
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

DEplain German Text Simplification

This model belongs to the experiments done at the work of Stodden, Momen, Kallmeyer (2023). "DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada. Association for Computational Linguistics. Detailed documentation can be found on this GitHub repository https://github.com/rstodden/DEPlain

We reused the codes from https://github.com/a-rios/ats-models to do our experiments.

Model Description

The model is a finetuned checkpoint of the pre-trained LongmBART model based on mbart-large-cc25. With a trimmed vocabulary to the most frequent 30k words in the German language.

The model was finetuned towards the task of German text simplification of documents.

The finetuning dataset included manually aligned sentences from the datasets DEplain-APA-doc only.

Model Usage

This model can't be used in the HuggingFace interface or via the .from_pretrained method currently. As it's a finetuning of a custom model (LongMBart), which hasn't been registered on HF yet. You can find this custom model codes at: https://github.com/a-rios/ats-models

To test this model checkpoint, you need to clone the checkpoint repository as follows:

  # Make sure you have git-lfs installed (https://git-lfs.com)
  git lfs install
  git clone https://huggingface.co/DEplain/trimmed_longmbart_docs_apa
  
  # if you want to clone without large files – just their pointers
  # prepend your git clone with the following env var:
  GIT_LFS_SKIP_SMUDGE=1

Then set up the conda environment via:

  conda env create -f environment.yaml

Then follow the procedure in the notebook generation.ipynb.

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Dataset used to train DEplain/trimmed_longmbart_docs_apa

Paper for DEplain/trimmed_longmbart_docs_apa

DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification

Paper • 2305.18939 • Published May 30, 2023