Instructions to use flax-community/t5-base-dutch-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flax-community/t5-base-dutch-demo with Transformers:

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

pipe = pipeline("text-generation", model="flax-community/t5-base-dutch-demo")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("flax-community/t5-base-dutch-demo")
model = AutoModelForSeq2SeqLM.from_pretrained("flax-community/t5-base-dutch-demo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use flax-community/t5-base-dutch-demo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flax-community/t5-base-dutch-demo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flax-community/t5-base-dutch-demo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/flax-community/t5-base-dutch-demo

SGLang

How to use flax-community/t5-base-dutch-demo 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 "flax-community/t5-base-dutch-demo" \
    --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": "flax-community/t5-base-dutch-demo",
		"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 "flax-community/t5-base-dutch-demo" \
        --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": "flax-community/t5-base-dutch-demo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use flax-community/t5-base-dutch-demo with Docker Model Runner:
```
docker model run hf.co/flax-community/t5-base-dutch-demo
```

YAML Metadata Error:"language[0]" with value "dutch" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.

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

t5-base-dutch-demo 📰

Created by Yeb Havinga & Dat Nguyen during the Hugging Face community week

This model is based on t5-base-dutch and fine-tuned to create summaries of news articles.

For a demo of the model, head over to the Hugging Face Spaces for the Netherformer 📰 example application!

Dataset

t5-base-dutch-demo is fine-tuned on three mixed news sources:

CNN DailyMail translated to Dutch with MarianMT.
XSUM translated to Dutch with MarianMt.
News article summaries distilled from the nu.nl website.

The total number of training examples in this dataset is 1366592.

Training

Training consisted of fine-tuning t5-base-dutch with the following parameters:

Constant learning rate 0.0005
Batch size 8
1 epoch (170842 steps)

Evaluation

The performance of the summarization model is measured with the Rouge metric from the Huggingface Datasets library.

    "rouge{n}" (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,
    "rougeL": Longest common subsequence based scoring.

Rouge1: 23.8
Rouge2: 6.9
RougeL: 19.7

These scores are expected to improve if the model is trained with evaluation configured for the CNN DM and XSUM datasets (translated to Dutch) individually.

Downloads last month: 91

Safetensors

Model size

0.2B params

Tensor type

F32

flax-community
/

t5-base-dutch-demo

t5-base-dutch-demo 📰

Dataset

Training

Evaluation

Datasets used to train flax-community/t5-base-dutch-demo

Spaces using flax-community/t5-base-dutch-demo 2