Instructions to use jwhong2006/t5-PostOCRAutoCorrecttion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jwhong2006/t5-PostOCRAutoCorrecttion with Transformers:

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

pipe = pipeline("text-generation", model="jwhong2006/t5-PostOCRAutoCorrecttion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("jwhong2006/t5-PostOCRAutoCorrecttion")
model = AutoModelForSeq2SeqLM.from_pretrained("jwhong2006/t5-PostOCRAutoCorrecttion")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jwhong2006/t5-PostOCRAutoCorrecttion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jwhong2006/t5-PostOCRAutoCorrecttion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jwhong2006/t5-PostOCRAutoCorrecttion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jwhong2006/t5-PostOCRAutoCorrecttion

SGLang

How to use jwhong2006/t5-PostOCRAutoCorrecttion 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 "jwhong2006/t5-PostOCRAutoCorrecttion" \
    --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": "jwhong2006/t5-PostOCRAutoCorrecttion",
		"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 "jwhong2006/t5-PostOCRAutoCorrecttion" \
        --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": "jwhong2006/t5-PostOCRAutoCorrecttion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jwhong2006/t5-PostOCRAutoCorrecttion with Docker Model Runner:
```
docker model run hf.co/jwhong2006/t5-PostOCRAutoCorrecttion
```

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-PostOCRAutoCorrecttion

This model is a fine-tuned version of google-t5/t5-small on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 15000

Training results

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

Downloads last month: 24

Safetensors

Model size

60.5M params

Tensor type

F32

Model tree for jwhong2006/t5-PostOCRAutoCorrecttion

Base model

google-t5/t5-small

Finetuned

(2287)

this model

jwhong2006
/

t5-PostOCRAutoCorrecttion

t5-PostOCRAutoCorrecttion

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for jwhong2006/t5-PostOCRAutoCorrecttion

Dataset used to train jwhong2006/t5-PostOCRAutoCorrecttion