Instructions to use CodeTed/Chinese_Spelling_Correction_T5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeTed/Chinese_Spelling_Correction_T5 with Transformers:

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

pipe = pipeline("text-generation", model="CodeTed/Chinese_Spelling_Correction_T5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("CodeTed/Chinese_Spelling_Correction_T5")
model = AutoModelForSeq2SeqLM.from_pretrained("CodeTed/Chinese_Spelling_Correction_T5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeTed/Chinese_Spelling_Correction_T5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeTed/Chinese_Spelling_Correction_T5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeTed/Chinese_Spelling_Correction_T5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeTed/Chinese_Spelling_Correction_T5

SGLang

How to use CodeTed/Chinese_Spelling_Correction_T5 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 "CodeTed/Chinese_Spelling_Correction_T5" \
    --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": "CodeTed/Chinese_Spelling_Correction_T5",
		"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 "CodeTed/Chinese_Spelling_Correction_T5" \
        --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": "CodeTed/Chinese_Spelling_Correction_T5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeTed/Chinese_Spelling_Correction_T5 with Docker Model Runner:
```
docker model run hf.co/CodeTed/Chinese_Spelling_Correction_T5
```

CodeTed commited on Jan 5, 2024

Commit

ef9dd97

1 Parent(s): da99d48

Update README.md

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README.md CHANGED Viewed

@@ -44,8 +44,8 @@ This model was obtained by `instruction-tuning` the corresponding `ClueAI/Prompt
 ## Usage
 ```python
 from transformers import AutoTokenizer, T5ForConditionalGeneration
-tokenizer = AutoTokenizer.from_pretrained("CodeTed/traditional_CSC_t5")
-model = T5ForConditionalGeneration.from_pretrained("CodeTed/traditional_CSC_t5")
 input_text = '糾正句子裡的錯字: 為了降低少子化，政府可以堆動獎勵生育的政策。'
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids
 outputs = model.generate(input_ids, max_length=256)

 ## Usage
 ```python
 from transformers import AutoTokenizer, T5ForConditionalGeneration
+tokenizer = AutoTokenizer.from_pretrained("CodeTed/Chinese_Spelling_Correction_T5")
+model = T5ForConditionalGeneration.from_pretrained("CodeTed/Chinese_Spelling_Correction_T5")
 input_text = '糾正句子裡的錯字: 為了降低少子化，政府可以堆動獎勵生育的政策。'
 input_ids = tokenizer(input_text, return_tensors="pt").input_ids
 outputs = model.generate(input_ids, max_length=256)