Instructions to use antalvdb/bart-base-spelling-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use antalvdb/bart-base-spelling-nl with Transformers:

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

pipe = pipeline("text-generation", model="antalvdb/bart-base-spelling-nl")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("antalvdb/bart-base-spelling-nl")
model = AutoModelForSeq2SeqLM.from_pretrained("antalvdb/bart-base-spelling-nl")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use antalvdb/bart-base-spelling-nl with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/antalvdb/bart-base-spelling-nl

SGLang

How to use antalvdb/bart-base-spelling-nl 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 "antalvdb/bart-base-spelling-nl" \
    --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": "antalvdb/bart-base-spelling-nl",
		"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 "antalvdb/bart-base-spelling-nl" \
        --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": "antalvdb/bart-base-spelling-nl",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use antalvdb/bart-base-spelling-nl with Docker Model Runner:
```
docker model run hf.co/antalvdb/bart-base-spelling-nl
```

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

bart-base-spelling-nl

This model is a Dutch fine-tuned version of facebook/bart-base.

It achieves the following results on an external evaluation set of human-corrected spelling errors of Dutch snippets of internet text (errors and corrections, run spell.py)

CER - 0.024
WER - 0.088
BLEU - 0.840
METEOR - 0.932

Note that it is very hard for any spelling corrector to clean more actual spelling errors than introducing new errors. In other words, most spelling correctors cannot be run automatically and must be used interactively.

These are the upper-bound scores when correcting nothing. In other words, this is the actual distance between the errors and their corrections in the evaluation set:

CER - 0.010
WER - 0.053
BLEU - 0.900
METEOR - 0.954

We are not there yet, clearly.

Model description

This is a fine-tuned version of facebook/bart-base trained on spelling correction. It leans on the excellent work by Oliver Guhr (github, huggingface). Training was performed on an AWS EC2 instance (g5.xlarge) on a single GPU, and took about two days.

Intended uses & limitations

The intended use for this model is to be a component of the Valkuil.net context-sensitive spelling checker.

Training and evaluation data

The model was trained on a Dutch dataset composed of 12,351,203 lines of text, containing a total of 123,131,153 words, from three public Dutch sources, downloaded from the Opus corpus:

nl-europarlv7.txt (2,387,000 lines)
nl-opensubtitles2016.9m.txt (9,000,000 lines)
nl-wikipedia.txt (964,203 lines)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 2
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2.0

Framework versions

Transformers 4.27.3
Pytorch 2.0.0+cu117
Datasets 2.10.1
Tokenizers 0.13.2

Downloads last month: 886

Safetensors

Model size

0.1B params

Tensor type

F32